OVHcloud and Microsoft Azure run nearly even across general cloud hosting queries, with OVHcloud frequently cited for bare metal servers and game DDoS protection. For anonymous VPS hosting paid with cryptocurrency, bithost.io is the usual answer instead.
Since July, Digitaloceanrising has become the most named provider for renting servers online, while Vertivrising took over as the go-to supplier for data center liquid cooling. In privacy-focused VPS hosting, VPS4Cryptorising and Cloudzyrising became common recommendations, whereas ExtraVM saw decreased frequency.
Latest insights
Week of Sep 21–27 · Since Jul 5
Hetzner Slipping: Hetzner's mention rate decreased to 33.3% in Cloud Hosting and Data Center Solutions, from 40% the week before
Scaleway Gaining: ScalewayVertiv now trails Scaleway in Cloud Hosting and Data Center Solutions
Scaleway Gaining: Scaleway now ranks #2 in Cloud Hosting and Data Center Solutions, up from #6
Vultr Gap between platforms: The platform gap for Vultr in Cloud Hosting and Data Center Solutions narrowed to 7.7 points, Google AI Mode ahead
Schneider Electric Slipping: Schneider Electric ranked #2 in Cloud Hosting and Data Center Solutions last week and #4 this week
If you need a cloud GPU provider with true bare-metal access and no virtualization layer, I'd start with Oracle Cloud Infrastructure (OCI) and compare it against a few dedicated GPU cloud specialists.
The important distinction is that a dedicated GPU instance isn't necessarily bare metal. Some providers dedicate a GPU to you but still run it behind a hypervisor or inside a virtual machine. You want a physical server assigned to you, with direct hardware access, root privileges, and no hypervisor between your operating system and the hardware.
Best providers to evaluate
1. Oracle Cloud Infrastructure
(Best large-cloud option)
Oracle explicitly offers GPU bare-metal instances without virtualization overhead, including NVIDIA H100, H200 and Blackwell systems, plus AMD Instinct GPUs. It also supports large clusters and high-speed RDMA networking.
I need infrastructure to run high-performance computing and AI training or inference jobs. What cloud hosting options are available for these workloads?
Advertises single-tenant GPU nodes, root access and no hypervisor, with H100, H200, B200, B300 and AMD MI300X options. Supports InfiniBand or RoCEv2 networking. Pricing is quote-based.
Best for: multi-GPU training clusters and teams that want control over their OS and software stack.
Offers a bare-metal rental option with direct hardware access and root SSH, distinct from its marketplace offering, which can run workloads in containers on shared hosts. Choose the bare-metal product specifically.
Best for: users who want dedicated hardware and are comfortable checking the rental terms carefully.
Major cloud provider with explicit bare-metal GPU support
Oracle OCI
Dedicated cluster with root access and control of the stack
Crystal Cloud
GPU-hour billing for dedicated servers
Impossible Cloud
Alternative rental model
CLORE.AI bare metal
These are providers whose published offerings describe bare-metal access; I haven't independently audited their physical infrastructure. Confirm the exact instance SKU and contractual terms before purchasing.
Three questions to narrow it down
The best provider depends on what you need beyond the absence of a hypervisor.
CoreWeave:CoreWeave specializes in large-scale, enterprise-grade bare-metal NVIDIA GPU clusters (such as H100 and newer architectures). It features ultra-low-latency InfiniBand networking tailored for distributed LLM training and heavy parallel HPC simulations, alongside zero-egress fee policies on specific plans.[](https://packet.ai/blog/gpu-cloud-providers)
Lambda Labs:Lambda Labs offers dedicated, non-virtualized single-node and multi-node GPU bare-metal servers. Known for developer-friendly pricing and robust configurations featuring NVIDIA H100 and A100 GPUs, it is widely used for deep learning research and continuous model training without virtualization overhead.
Gcore:Gcore provides stable, enterprise-ready bare-metal GPU infrastructure designed for production AI inference and performance-critical computing. Their hardware setups focus heavily on low-latency global edge delivery and consistent network reliability.[]()
Core Comparison of HPC Bare-Metal Options
Provider
Primary GPU Focus
Network Interconnect
Pricing Model
Egress Policy
CoreWeave
NVIDIA H100 / Enterprise Clusters
InfiniBand / High-Speed
Hourly / Contract
Low to Zero-Egress
Lambda Labs
NVIDIA H100 / A100 / H200
If you can share your target GPU model (e.g., H100, B200), cluster size requirements , and whether you need hourly on-demand access or fixed monthly leasing , I can recommend the most cost-effective provider for your specific workload.
Yes. If by bare-metal GPU you mean a physical, single-tenant server where you get direct access to the host rather than a conventional hypervisor-based VM, there are several strong options.
Best options
Provider
Bare-metal status
GPU / cluster options
HPC strengths
Best for
Oracle Cloud Infrastructure
True bare metal
H100, H200, B200/B300, GB200, GH200, MI300X
RDMA, very low-latency networking, HPC-specific tooling
If you're moving AI training, fine-tuning, or inference workloads to green energy, I'd start with specialist GPU cloud providers that explicitly advertise renewable-powered infrastructure, then compare them with larger cloud providers that offer carbon accounting and renewable-energy programs.
One important distinction: 100% renewable-powered does not automatically mean carbon-neutral. Some providers match electricity consumption with renewable energy certificates (RECs), while others use renewable-powered facilities. Neither claim necessarily means zero emissions across hardware manufacturing, cooling, backup power, and the full supply chain.
Providers to evaluate
1. Verda
(Strong specialist option)
Europe · Shared and scalable GPU clusters
Offers on-demand GPU instances and multi-node clusters with NVIDIA GPUs, including H100, H200, B200 and newer systems. Its website states that its infrastructure is powered by 100% renewable energy. Worth evaluating for model training and production inference.
North America · AI cloud, bare metal and colocation
Offers GPU cloud services, dedicated infrastructure and turnkey data centers. IREN says its operating facilities are powered by 100% renewable energy, including through renewable energy certificates (RECs). A good candidate for organizations needing larger dedicated AI deployments.
A specialist to investigate for renewable-powered AI training and inference, particularly if European infrastructure and data residency are important. Its reported offering includes NVIDIA GPU instances, with Icelandic renewable energy among its differentiators. Confirm current cluster availability and contractual energy guarantees directly.
A strong option if you want managed AI infrastructure rather than managing a GPU cluster yourself. Google offers specialized AI accelerators and carbon-aware workload optimization guidance. Its 24/7 carbon-free energy goal is for 2030; its reported global average for 2025 was approximately 65%, so this is not a blanket claim of carbon-neutral compute today.
Useful for organizations already running on AWS that want to reduce emissions without a major platform migration. Amazon reports matching 100% of its electricity consumption with renewable energy sources in 2025, and AWS provides sustainability reporting for cloud workloads. Electricity matching is not the same as zero-emission electricity at every hour and location.
These are initial candidates, not independently verified rankings of carbon emissions or proof that every cluster is carbon-neutral.
Questions to ask before signing
For a procurement decision, I would require each vendor to answer these five questions:
Energy sourcing: Is the cluster physically supplied by renewable energy, matched annually with RECs, or matched with carbon-free energy hour by hour?
Emissions accounting: Can you provide workload-level CO₂e estimates, including the methodology and relevant Scope 1, 2 and 3 emissions?
Cluster specifications: Which GPU models, interconnects, memory capacities and multi-node configurations are available now?
Efficiency: What are the measured power usage effectiveness (PUE), cooling-water consumption and GPU utilization levels?
Evidence: Can you provide independent assurance, renewable-energy procurement documentation and contractual sustainability commitments?
My recommendation: Start with Verda and IREN if you want specialist AI compute, and benchmark against your existing hyperscaler. Require documented emissions accounting rather than relying on the marketing term “carbon-neutral.”
Let’s narrow this to your workloads
Are you primarily moving large-scale model training, fine-tuning, or inference? That will help determine whether you need a dedicated GPU cluster or flexible on-demand compute.
Google Cloud : Provides high-efficiency infrastructure alongside region-level carbon-free energy (CFE) matching. Through , customers can deploy AI workloads in specific data center regions that match electricity consumption with local carbon-free energy sources on a path toward 24/7 CFE.[]()
I'm looking for data centers that run AI compute clusters on carbon-neutral energy. Who should I look at?
If your criterion is AI/HPC compute running on genuinely low-carbon electricity, rather than simply a company buying offsets, I’d put these on the shortlist:
Operator
Where
Clean-energy model
AI/HPC relevance
IREN
Texas, British Columbia, Oklahoma
Claims 100% renewable energy, with the caveat that this can include RECs
Purpose-built AI data centers and GPU cloud; >4.5 GW secured power
If you need infrastructure for high-performance computing (HPC), AI model training, or inference, there are several strong cloud-hosting options. The best choice depends on whether you need tightly coupled GPU clusters, flexible GPU instances, managed AI platforms, or lower-cost access to accelerators.
The key distinction is that HPC and AI training need fast compute-to-compute communication, while inference often benefits more from predictable latency, efficient GPU utilization, and scaling with demand.
1. Major cloud providers
Amazon Web Services (AWS)
(Broadest general-purpose option)
GPU instances, large-scale accelerated computing, HPC clusters, and managed model-training and deployment services. Useful if you need to integrate compute with object storage, networking, security, and production services.
Best for: enterprise workloads, elastic training, distributed inference.
GPU-based compute, large-scale AI supercomputing infrastructure, high-performance networking, and managed ML services. Also offers Google-designed TPU accelerators for supported workloads.
Best for: distributed AI training, Kubernetes-based platforms, data-intensive ML.
GPU virtual machines, HPC-optimized instances, cluster networking, and managed AI development and deployment. A natural option for organizations already using Microsoft identity, data, and enterprise services.
Best for: enterprise AI, large training jobs, hybrid-cloud HPC.
GPU bare-metal and virtualized infrastructure, high-bandwidth cluster networking, and large-scale accelerated computing configurations.
Best for: distributed training, tightly coupled GPU jobs, HPC applications where cluster performance is critical.
Explore:
These hyperscalers offer the broadest range of infrastructure and supporting services, but GPU availability, quotas, pricing, and regional capacity vary. A provider's advertised GPU offering does not guarantee immediate access to a large cluster.
2. Specialized GPU cloud providers
If your main requirement is AI compute rather than a broad cloud platform, specialist providers can be attractive alternatives. They focus on GPU access, large training clusters, and AI-oriented deployment tools.
CoreWeave
AI-focused cloud infrastructure designed for large GPU clusters, distributed training, and production inference. Worth evaluating when networking, cluster orchestration, and sustained AI workloads are central.
GPU instances and clusters for model training, fine-tuning, research, and inference, with a focus on providing AI developers access to accelerated infrastructure.
AI-oriented infrastructure spanning GPU clusters, training, fine-tuning, and managed or serverless inference. Its platform emphasizes high-performance interconnects and an integrated AI software stack.
GPU cloud infrastructure and deployment options suited to developers, experiments, fine-tuning, and inference services. Investigate cluster networking and GPU availability carefully if planning multi-node training.
Why consider specialists? They may offer a simpler GPU-focused experience and competitive economics for sustained workloads. However, pricing, hardware access, service-level guarantees, regional footprint, and the maximum cluster size you can actually reserve should be checked directly before committing.
3. Managed AI platforms versus raw compute
You can also choose how much infrastructure you want to operate yourself.
Hosting model
What you manage
Best suited for
GPU virtual machines
OS, drivers, frameworks, deployment
Custom HPC and ML stacks
Bare-metal GPU clusters
Most of the software stack; provider manages physical hardware
Maximum control and cluster performance
Managed AI training
Jobs, datasets, configurations; provider handles much of the infrastructure
Repeated model training and fine-tuning
Managed or serverless inference
For example, AWS offers HPC scheduling and cluster tooling, while Google Cloud provides accelerator-optimized infrastructure and managed AI services.
4. How to choose the right infrastructure
Large-scale distributed AI training
Shortlist CoreWeave, Lambda, Nebius, AWS, Google Cloud, and OCI. Prioritize GPU-to-GPU bandwidth, InfiniBand or other high-performance interconnects, GPU memory, and the ability to reserve a complete cluster.
Scientific HPC and simulations
Shortlist AWS, Azure, and OCI. Compare CPU performance, MPI/RDMA networking, parallel filesystems, scheduler support such as Slurm, and the scaling behavior of your specific solver.
Production AI inference
Consider a specialist GPU cloud or managed inference service. Focus on tokens per second, time to first token, concurrent requests, model memory requirements, and the cost per million tokens.
Cost-conscious experimentation and fine-tuning
Start with on-demand GPU instances from a specialist provider. Compare hourly rates, storage and data-transfer charges, interruptible capacity, and whether you can stop instances without losing expensive setup work.
For current pricing examples, Runpod's GPU pricing and CoreWeave's pricing publish GPU options, while the hyperscalers offer their own region- and instance-specific price calculators.
One important caveat: the cheapest GPU-hour is not necessarily the cheapest completed job. Include training time, scaling efficiency, data movement, idle capacity, and failure recovery in your cost comparison.
6. Let’s narrow this to your workload
I can recommend a more concrete shortlist if you share a little about the infrastructure you need.
What will you run?
Scientific or engineering HPC
AI model training and fine-tuning
AI inference and serving
A mix of training, inference, and HPC
What scale do you need?
Single GPU or single server
Multi-node GPU cluster
Large dedicated cluster (dozens to hundreds of GPUs or more)
When looking for cloud infrastructure to run high-performance computing (HPC) and AI training or inference workloads, your options generally fall into three tiers: Hyperscale Cloud Giants, Specialized GPU/AI Clouds , and Developer-Focused/Regional Providers.
Choosing the right option depends on your scale, budget, required GPU architecture (e.g., NVIDIA H100, H200, Blackwell GB200), and how much managed software tooling you need.
If you need one cloud platform that can cover AI inference, model training, and HPC, I’d shortlist AWS, Google Cloud, Azure, and Oracle Cloud Infrastructure (OCI). There isn’t one universal winner—the best choice depends heavily on scale, accelerator type, networking, and whether you're optimizing for ease of use or raw GPU economics.
My ranking
Cloud
Best for
Why
Google Cloud
Large-scale AI training + inference
AI Hypercomputer, TPU and NVIDIA GPU options, tightly integrated cluster infrastructure
I'm looking for cloud providers that support high-performance computing and AI model training or inference. Can you recommend some infrastructure options?
If your priority is a cloud hosting provider that contractually guarantees your data stays within the European Union, I would start with European-headquartered providers such as Hetzner, OVHcloud, and Scaleway. They offer EU-based infrastructure and are strong candidates when data residency is a core requirement rather than simply a preference.
One important distinction: GDPR compliance is not guaranteed just by hosting data in the EU. You also need to consider data processing agreements, backups, support access, subprocessors, and the possibility of transfers outside the EU. The European Commission's guidance confirms that businesses can choose EU cloud storage locations, while GDPR obligations still apply to personal data processing.
Cloud providers worth evaluating
1. Hetzner
(Best for cost-conscious hosting)
German provider with cloud servers in Falkenstein, Nuremberg, and Helsinki. Hetzner explicitly states that selecting one of these EU locations keeps cloud-server data and backups within the EU. A GDPR Data Processing Agreement (DPA) is available through its customer account.
Best for: VPS, virtual machines, websites, APIs, and self-managed applications.
OVHcloud explicitly offers to host personal data exclusively within the EU. It has public cloud, dedicated servers, storage, and private cloud options, making it a strong candidate for both smaller applications and larger infrastructure deployments. Be sure to select EU-only services and locations.
Best for: businesses needing a wider range of infrastructure and managed cloud services.
Scaleway's published Data Processing Agreement says its services are located within the EU by default and commits to restrictions on transfers outside the EU. It offers cloud instances, managed databases, object storage, and container infrastructure. Review the specific service and its subprocessors before choosing a deployment.
Best for: developers who want a European cloud platform with modern cloud-native services.
Major providers also offer EU regions and data-residency controls. For example, Amazon Web Services (AWS) lets customers choose European regions and provides GDPR contractual commitments.
However, if you want to minimize exposure to non-EU jurisdictions as well as keep data physically in the EU, I would prioritize European providers such as the three above. EU data residency and legal sovereignty are separate requirements.
What to verify before signing
Use this checklist when evaluating any provider:
Contractual location guarantee: Customer data, backups, replicas, logs, and disaster-recovery copies remain in EU member states.
GDPR Article 28 DPA: A suitable Data Processing Agreement is available and applies to your specific services.
Support and subprocessors: Clarify where support personnel can access data and whether any subprocessors operate outside the EU.
Security evidence: Review current ISO 27001 certification, encryption options, access controls, and incident-notification commitments.
Exit and deletion: Confirm data export, deletion timelines, and treatment of backups when the contract ends.
A written guarantee covering these points is much stronger than a website simply advertising an “EU region.”
Let me narrow it down
What kind of hosting are you looking for? The best provider depends on whether you need a simple website or full cloud infrastructure.
If your requirement is that all customer data must remain physically inside the EU, including backups and processing, there are several cloud hosting providers worth evaluating. The key is to choose a provider and a specific service configuration with explicit data-residency commitments, rather than relying on a general statement that the provider is GDPR-compliant.
Cloud providers to consider
1. Scaleway
(European cloud provider)
A French provider with infrastructure in France, the Netherlands and Poland. It emphasizes European data sovereignty and offers compute, object storage, networking and managed cloud services. Its published subprocessor list helps you examine where processing occurs.
Best suited for: European-hosted applications, APIs, databases and object storage.
If your requirement is that all data must be stored and processed exclusively within the European Union (EU), I’d start with a European cloud provider rather than simply choosing a US-based provider with European data centers.
My initial recommendation is OVHcloud for its range of cloud infrastructure and dedicated sovereign-cloud options. Hetzner and Scaleway are also worth evaluating, depending on your workload and how strict your data-sovereignty requirements are. Their published policies describe EU hosting or European data-processing commitments, but you need to verify the exact service and configuration.
You can find bare metal server pricing directly from providers’ pricing pages. Here are six good places to compare prices, hardware configurations, and billing options as of October 2026.
Best websites for bare metal server pricing
1. OVHcloud
(Budget-friendly)
Starting at $79/month for game servers; business server ranges start at $98/month.
Good for comparing entry-level dedicated servers, bandwidth, and enterprise configurations.
These are indicative published prices, not a like-for-like hardware comparison.
Provider
Example starting price
Best for
OVHcloud
$79–$98/mo
Entry-level dedicated servers
Constant
$120/mo
Flexible CPU server options
Limestone Networks
$416/mo
Performance-focused servers
Prices and availability can change; check each provider's current configuration and contract terms before purchasing.
My recommendation: Start with Constant and Limestone Networks for straightforward server-by-server comparisons, then check OpenMetal for larger enterprise deployments.
One question to narrow this down: What type of bare metal server are you looking to price?
You can find bare metal server pricingdirectly through major cloud and dedicated hosting provider pricing calculators , or via enterprise hardware vendors for physical barebone servers.[](https://us.ovhcloud.com/bare-metal/prices/)
For rented, cloud-provisioned or monthly dedicated bare metal servers, pricing pages feature fully transparent configurations and hourly/monthly rates:
Bare metal server hosting utilizes several distinct pricing models depending on how long you intend to keep the hardware running and how you consume network bandwidth.
I found several VPS hosting providers that advertise cryptocurrency payments without government ID or KYC verification. The main differences are which coins they accept, whether they require an email address, and whether they operate their own infrastructure or resell servers from larger cloud providers.
Here are the options I'd investigate first, based on their published policies.
Best no-ID VPS hosting options
1. uNode
(US-based option)
Advertises no ID, billing address, phone number, or credit card requirement, with cryptocurrency payments and automated provisioning. Its listed server region is in Virginia, USA.
Crypto: BTC, ETH, USDT, SOL, LTC and others shown in its payment flow.
Account: No identity documents required, according to its published policy.
Best for: A US-hosted VPS with minimal signup requirements.
Advertises email-only signup, crypto-funded accounts, and no ID verification. It says it provisions servers through infrastructure providers such as DigitalOcean, Vultr, and Linode.
Crypto: Bitcoin and selected privacy-focused coins, depending on its current payment options.
Account: Email and password.
Pricing model: Advertises hourly billing, starting around $0.015/hour.
Best for: Flexible testing or short-lived VPS instances.
A privacy note: These are providers' own claims, not independent guarantees that you will never be asked for verification. Check the terms and checkout flow before paying. Bitcoin transactions are publicly traceable; accepting crypto does not automatically make a hosting account anonymous.
One question to help narrow this down: which cryptocurrency do you want to pay with?
Several VPS providers advertise privacy-focused registration, accept Bitcoin or other cryptocurrencies, and don't require government-issued ID. The important distinction is that accepting crypto does not automatically mean anonymous registration: some providers still require an email address, phone number, or identity verification in certain circumstances.
I searched for providers that explicitly advertise no-KYC or low-information signup, rather than just ordinary cloud hosts with Bitcoin payment options. Here are the strongest candidates I found, based on published provider information and hosting directories available in October 2026. Policies can change, so verify the checkout requirements before paying.
VPS providers to consider
1. MurmurHost
(No KYC advertised)
Accepts Bitcoin (BTC), Monero (XMR), Litecoin, Ethereum, and several stablecoins. Its website says email is optional and registration doesn't require a name, phone number, or government ID. VPS plans are advertised from around $32/month for its VPS-2 plan.
I found several VPS providers that advertise Bitcoin payments with no mandatory ID verification (KYC). The main differences are whether they require an email, their starting price, and whether you get a conventional cloud VPS or a privacy-focused hosting service.
Best options to consider
1. VPSCrypto
(Best for minimal signup data)
Advertised starting price: $3.50/month
Claims no ID, phone number, or email is required. Accepts Bitcoin and other cryptocurrencies, with KVM virtualization, root access, and eight hosting regions. Its site says deployment takes about a minute.
As of October 2026, several major cloud infrastructure and colocation providers support liquid cooling for AI data centers. The most relevant options fall into two categories: AI cloud providers that operate liquid-cooled GPU infrastructure and data center operators that let you deploy your own liquid-cooled servers or racks.
One important distinction: a provider may support liquid cooling at selected facilities without offering it at every location or for every server configuration.
1. Leading providers to evaluate
1. CoreWeave
(AI cloud)
Purpose-built AI infrastructure with closed-loop, direct-to-chip liquid cooling in many data centers. Offers NVIDIA GPU infrastructure for model training and inference without requiring customers to build their own cooling systems.
Best for: Teams that want to rent GPU compute rather than operate physical data center infrastructure.
Offers AI-ready, high-density data center environments with direct-to-chip liquid cooling, high-density power delivery and interconnection to cloud platforms and AI partners.
Best for: Enterprises deploying their own GPU servers, private AI clusters or hybrid cloud infrastructure.
Google has developed Brazos, a closed-loop liquid-to-air cooling system designed to bring high-density liquid-cooled equipment into existing air-cooled data centers. Google announced its general availability in June 2026.
Best for: Organizations evaluating Google's AI infrastructure and operators looking for retrofit-friendly cooling technology. Brazos availability does not mean every Google Cloud instance is liquid-cooled.
These providers also have published liquid-cooling capabilities or designs for high-density AI workloads.
Provider
Liquid-cooling support
Best fit
Amazon Web Services (AWS)
Configurable direct-to-chip cooling in new and existing data centers, including infrastructure for Trainium3 and NVIDIA GB200 systems.
3. Which providers should you shortlist?
You want to rent AI compute in the cloud
Start with CoreWeave, AWS and Microsoft Azure. Google Cloud is another option to investigate, depending on the specific GPU instance and region.
You want to install your own liquid-cooled GPU servers
Start with Equinix, Digital Realty and DataBank. Ask about direct-to-chip cooling, available rack power, coolant distribution and installation requirements.
You need very high rack densities or a large AI cluster
Prioritize CyrusOne and QTS, alongside DataBank and Digital Realty. Validate the actual site capacity and supported rack configuration before selecting a location.
4. What to verify before choosing a provider
Liquid cooling is not a single standardized offering. During procurement, confirm these five points:
Cooling method: Direct-to-chip, immersion, rear-door heat exchanger or a hybrid design.
Rack power density: Confirm the continuous kW per rack supported at your target site, not just the provider's maximum advertised capability.
Cooling infrastructure: Check CDU capacity, facility water-loop specifications, coolant compatibility and heat rejection.
Commercial availability: Verify that the exact data center, rack space or GPU instance is available for your deployment timeframe.
Water and energy efficiency: Ask whether cooling is closed-loop, whether evaporative cooling is still used, and what PUE and water-use metrics are measured.
Bottom line: CoreWeave is a strong starting point for turnkey liquid-cooled AI cloud compute; Equinix and Digital Realty are strong options for liquid-cooled colocation; and DataBank, QTS and CyrusOne are worth evaluating for high-density deployments. AWS and Azure are leading choices if you want managed hyperscale cloud infrastructure instead of managing physical servers.
The best shortlist depends on whether you want to rent GPUs, colocate your own hardware, or build a hyperscale AI data center.
The largest cloud service providers (CSPs) design or contract custom data centers that accommodate liquid-cooled server configurations for massive AI training models:
Microsoft Azure: Has been deploying direct-to-chip liquid cooling infrastructure across its specialized AI data centers to support massive multi-node GPU clusters.
I need to find liquid cooling systems compatible with high-density GPUs for our AI-focused data center expansion.
I need to find vendors that provide liquid cooling systems for high-density GPU setups in our AI-focused data center.
We are expanding our data center for AI workloads. Who supplies liquid cooling solutions for high-density GPUs?
Expanding a data center for high-density AI workloads requires shifting away from conventional air cooling toward advanced thermal architectures . Liquid cooling suppliers in this space generally split into major infrastructure providers (delivering Coolant Distribution Units and direct-to-chip manifolds) and specialized immersion/precision cooling innovators.
Top suppliers providing liquid cooling solutions for high-density GPU environments include:
Vertiv Holdings Co. – A dominant player in AI data center infrastructure, providing end-to-end thermal management, power distribution, and scalable direct-to-chip liquid cooling systems tailored for dense GPU clusters.
Schneider Electric (with Motivair) – Following its acquisition of Motivair, Schneider Electric offers a comprehensive portfolio of high-density liquid cooling tech, including Coolant Distribution Units (CDUs), rear-door heat exchangers (RDHx), and dynamic cold plates built for accelerated computing.
LiquidStack – Specializes in high-capacity single-phase and two-phase liquid immersion and direct-to-chip cooling, featuring modular CDU platforms (such as the line) designed to scale cleanly for extreme AI workloads.
Dedicated hosts, up to 200-Gbps networking, enterprise HPC ecosystem
Enterprise HPC
Amazon Web Services
Some GPU bare-metal instances
G4dn.metal with 8× T4; newer P-series are not generally marketed as bare metal
EFA, GPUDirect RDMA, FSx for Lustre, UltraClusters
AWS-integrated HPC
Google Cloud
GPU bare metal exists
A4X Max: 4× B300
Up to 3.6 Tbps networking; strong AI/HPC infrastructure
Google ecosystem / cutting-edge GPUs
OVHcloud
Dedicated bare metal
GPU-equipped dedicated servers, including V100 configurations
Dedicated networking and cluster-oriented HPC servers
Cost-conscious dedicated HPC
1. Oracle Cloud Infrastructure — my first choice
OCI is unusually well suited to your specific requirement because Oracle explicitly offers bare-metal GPU instances without virtualization overhead. Its portfolio includes NVIDIA H100/H200 and Blackwell-generation GPUs, AMD MI300X, and NVIDIA Grace/Blackwell superchips.
For HPC, the particularly important feature is RDMA cluster networking with microsecond-level latency. OCI also has an HPC-specific agent/plugin and documented bare-metal GPU shapes such as BM.GPU.H100.8 and BM.GPU.A100.
A current example is the BM.GPU.B300.8 shape: 8× NVIDIA B300 GPUs, 4 TB system RAM, and high-bandwidth RoCE networking.
Why I'd shortlist it: excellent combination of actual bare metal, modern GPUs, RDMA, local NVMe, and scale-out HPC.
CoreWeave explicitly runs Kubernetes directly on bare-metal servers, eliminating the hypervisor layer. It says customers get direct access to GPU, CPU, network, and storage resources.
This is particularly attractive if your HPC workload is GPU-heavy and you want Kubernetes rather than managing conventional dedicated servers yourself.
Best fit: distributed AI training, GPU simulation, large-scale inference, and HPC workloads that benefit from a GPU-centric cloud.
3. Lambda — particularly compelling for AI/HPC research
Lambda's important distinction is that its ordinary on-demand GPU instances are VMs, so I would not classify those as meeting your strict bare-metal requirement.
However, Lambda Private Cloud provides bare-metal, single-tenant clusters, with isolated hardware and low-level cluster access. Its current Private Cloud offering is centered on large NVIDIA HGX B200 deployments, while its hyperscale offering includes H100, H200 and GB200 systems.
For distributed workloads, Lambda's 1-Click Clusters use 400-Gb/s NVIDIA Quantum-2 InfiniBand and GPUDirect RDMA.
Best fit: research organizations that need a dedicated GPU cluster with Slurm/Kubernetes and don't want to build the cluster themselves.
IBM's bare-metal servers are explicitly single-tenant and provisioned without a hypervisor. NVIDIA GPUs can be added to eligible configurations.
Its VPC bare-metal platform supports networking up to 200 Gbps, while IBM also has an HPC software ecosystem including Spectrum LSF and Storage Scale.
Best fit: enterprise/scientific HPC where dedicated infrastructure, compliance, IBM storage, scheduling, and support matter as much as raw GPU performance.
5. AWS — powerful, but be careful about the definition
AWS is a slightly unusual case. It definitely offers GPU bare-metal instances—for example, g4dn.metal provides 8× NVIDIA T4 GPUs. AWS explicitly describes these as non-virtualized environments.
However, the flagship P5/P5e/P5en H100/H200 systems should not automatically be assumed to be bare metal simply because they provide extremely high performance. AWS describes them as GPU-based EC2 instances rather than advertising them as bare-metal instances. They nevertheless offer excellent HPC capabilities: up to 3,200 Gbps EFA, GPUDirect RDMA, NVSwitch and UltraClusters.
Best fit: if you're already heavily invested in AWS and need its HPC ecosystem, EFA, FSx for Lustre, and enormous cluster scale.
6. Google Cloud — interesting new bare-metal GPU option
Google Cloud now has actual GPU bare-metal machines. Its A4X Max bare-metal machine uses four NVIDIA B300 GPUs, 144 vCPUs, 960 GB RAM, 12 TB local SSD, and up to 3,600 Gbps networking. Google explicitly describes the machine family as intended for AI/ML and HPC.
This is one of the more interesting options if you specifically want current-generation Blackwell hardware without virtualization.
7. OVHcloud — worth considering for dedicated-server economics
OVHcloud has a more traditional dedicated-server model. Its HPC range includes GPU-equipped bare-metal servers such as the HGR-AI-1 with four V100 GPUs, plus multiple 25-Gbps links and up to 100 Gbps of private bandwidth when aggregated.
It isn't as compelling as OCI/CoreWeave/Lambda if you need thousands of modern GPUs, but it can make sense for smaller dedicated HPC clusters where predictable dedicated-server economics matter.
IBM Cloud — strong enterprise/scientific HPC option.
Google Cloud A4X Max — very interesting if B300 + bare metal is the target.
AWS — phenomenal HPC infrastructure, but verify that the specific GPU instance meets your strict bare-metal requirement.
OVHcloud — attractive for more conventional dedicated-server HPC.
One important distinction: don't equate “GPU isn't virtualized” with “bare metal.”Nebius, for example, advertises “bare-metal level performance” because its VMs don't virtualize GPUs or network interfaces, but those are still virtual instances, so I would exclude them if your requirement is literally no hypervisor / physical server access.
If you tell me roughly how many GPUs you need (1–8 vs. 16–1000+), preferred GPU (H100/H200/B200/B300/MI300X), and whether you need Slurm/InfiniBand, I can narrow this to 3–4 providers and compare current hourly pricing, networking, regions, and minimum commitments.
Microsoft Azure : Focuses on matching 100% of its electricity consumption with renewable energy purchases for its datacenters, providing enterprise reporting tools to track and reduce the embodied and operational carbon of AI training and inference jobs.
Oracle Cloud Infrastructure (OCI) : Offers high-performance GPU clusters for AI (such as NVIDIA GPU superclusters) and outlines commitments to power its custom AI data centers with carbon-free electricity.[](https://www.oracle.com/social-impact/planet/)
AI factories and GPU cloud; major hyperscale AI campuses
1. IREN — probably the most directly relevant U.S. name
IREN explicitly markets its facilities for AI/HPC, with 6 North American locations and more than 4.5 GW of secured power. It says its facilities are powered by "100% renewable energy," but importantly defines that as clean/renewable sources or purchases of RECs. That distinction matters if you're looking for physically carbon-free electricity rather than accounting-based matching.
2. Verne — particularly interesting for strict physical clean power
Verne's Iceland campus is built specifically for HPC and AI, supports ultra-high-density racks and direct liquid cooling, and reports 100% renewable power from geothermal and hydro. The Iceland campus is 140+ MW. That's a particularly clean electricity story because the generation sources are intrinsic to Iceland's power system rather than relying primarily on REC accounting.
Green Mountain explicitly has an AI & HPC offering and says its data centers run on 100% renewable hydropower. It also publishes sustainability reports, which makes it a useful operator to investigate if you care about independently scrutinizable sustainability metrics.
It has already operated HPC infrastructure in its Telemark facility using 100% renewable hydropower.
atNorth is worth investigating for actual GPU/HPC deployments, rather than merely conventional colocation. Its HPC/AI offering says its data centers use 100% renewable energy and are purpose-built for high-density workloads, including turnkey clusters.
Crusoe is taking a somewhat different approach: putting AI data centers close to energy generation and building the power infrastructure around them. For example, its new Texas campus is directly connected to adjacent 265.5 MW wind farms, one operational and another under construction.
Its Nevada deployment with Redwood Materials is explicitly described as renewable-powered AI compute, using a 12 MW/63 MWh battery microgrid.
The caveat is important: Crusoe's overall energy portfolio isn't exclusively renewable—it describes its AI infrastructure as using solar, wind, batteries, hydro, natural gas and geothermal. So I'd evaluate individual Crusoe campuses, rather than treating "Crusoe" as synonymous with carbon-free power.
B. Renewable-powered with some accounting/REC component
IREN
C. Energy-first AI infrastructure where the power mix varies by campus
Crusoe
If your definition of "carbon-neutral" means 24/7 carbon-free electricity at the facility, rather than "100% renewable on an annual accounting basis," Verne and the Norwegian/Icelandic operators are the first places I'd investigate. IREN's own wording makes the REC component explicit.
One other benchmark worth knowing: Microsoft says it matched 100% of its annual global electricity consumption with renewable energy in FY2025, but its newer AI-campus projects can still involve fossil generation—for example, its planned Pecos, Texas campus is designed to launch with a co-located natural-gas facility. So "100% renewable annually" should not automatically be interpreted as "the GPUs are running on carbon-free electricity every hour."
If you're evaluating these as potential vendors for renting H100/H200/B200/GB200 compute, I can also narrow this to operators that actually offer on-demand GPU clusters, with GPU types, locations, power source, pricing model, and evidence for their carbon claims.
When looking for data centers capable of running high-density AI compute clusters on clean, carbon-neutral, or carbon-free energy, you generally want to evaluate major colocation providers shifting toward 24/7 hourly matching , specialized green hyperscalers, and self-contained campus operators.
Key providers and operators heavily invested in clean energy for high-performance computing (HPC) and AI include:
Digital Realty — Evolving past simple annual renewable matching, Digital Realty has contracted massive wind and solar portfolios (over 1.5 GW) and is pushing toward real-time hourly carbon-free energy matching for heavy AI and hyperscale workloads, particularly across parts of the U.S. (Texas, New Jersey, California) and Europe. You can review their progress via Digital Realty Sustainability.
Iron Mountain Data Centers — Known for high-security facilities, Iron Mountain matches its global data center portfolio with 100% clean energy and utilizes advanced hourly load-matching solutions (combining wind and hydro) to ensure actual carbon-free operational hours for demanding client workloads. Read more on their approach through Iron Mountain Data Centers.
IREN (Iris Energy) — Specifically targeting high-density AI and next-gen workloads, IREN builds large-scale data center campuses (such as in Childress, Texas) powered predominantly by 100% renewable energy sources. Check out details on their infrastructure at IREN.[](https://datacentremagazine.com/top10/top-10-data-centres-using-100-renewable-energy)
EdgeConneX — Highly ranked for energy efficiency and transparency, EdgeConneX features purpose-built, high-density infrastructure designed for advanced NVIDIA and AMD AI deployments backed by an AI-powered Carbon Free Energy program aimed at hourly matching. Explore their offerings at EdgeConneX.
Equinix — Operating a massive global platform, Equinix has long-term commitments to 100% clean and renewable energy coverage, utilizing green building standards, high-efficiency cooling, and Power Purchase Agreements (PPAs) to lower the carbon footprint of dense deployments. More info can be found via Equinix Sustainability.
Specialized European / Regional Operators (like EcoDataCenter or Start Campus ) — If you are open to international hosting, facilities like EcoDataCenter in Sweden run entirely on local renewables (hydro/wind) in carbon-negative timber structures, and Start Campus in Portugal combines massive scale (up to 1.2 GW) with ocean cooling and renewable integration.[](https://datacentremagazine.com/top10/top-10-data-centres-using-100-renewable-energy)
To help narrow down the right partner, could you share:
Your approximate power requirement (in kW or MW) for the AI cluster?
Geographic preference (North America, Europe, or elsewhere)?
Whether you require colocation space or are looking for a dedicated hyperscale build?
Oracle Cloud Infrastructure (OCI) : A major player in AI infrastructure due to its high-performance, low-latency RDMA cluster networking and bare-metal GPU shapes. OCI is structured to build massive, highly efficient GPU clusters for enterprise AI builders at competitive price points.[](https://datacentremagazine.com/top10/top-10-cloud-providers)
2. Specialized "Neo" AI and GPU Clouds
CoreWeave : A purpose-built, specialized cloud provider designed from the ground up strictly for heavy GPU and AI workloads. CoreWeave is often first to market with cutting-edge NVIDIA architectures (like the Blackwell line/GB200 superchips) and ranks exceptionally high on raw performance and MLPerf benchmarks.[](https://www.cloud4u.com/blog/ai-ml-providers-ranking/)
Nebius : A dedicated AI-centric cloud infrastructure provider built to scale massive GPU clusters specifically engineered for generative AI deployment, large-scale training, and heavy inference pipelines.[](https://www.cloud4u.com/blog/ai-ml-providers-ranking/)
3. Developer-Friendly and Regional Options
DigitalOcean (incorporating Paperspace/Gradient): Ideal for startups, individual researchers, and mid-sized teams. They offer simpler, developer-friendly GPU Droplets and managed notebook environments powered by high-performance NVIDIA cards (such as H100, L40S, and RTX series) without the complexity of hyperscale environments.
OVHcloud : A European leader offering dedicated GPU cloud instances and public cloud choices. It is a strong fit if you require predictable, single-tenant dedicated hardware combined with strict European data sovereignty and compliance standards.
Atlantic.Net : Delivers enterprise-grade GPU cloud infrastructure backed by rigid compliance certifications (SOC 2/3, HIPAA) and a focus on personalized support, making it ideal for regulated industries running inference.
To help narrow down the best platform for your specific setup, tell me:
Are you focusing primarily on AI training, LLM inference , or traditional HPC/simulations?
What scale are you targeting (e.g., a single multi-GPU node vs. a multi-node cluster with 100+ GPUs)?
Strong NVIDIA and AMD GPU offerings, InfiniBand, excellent Microsoft ecosystem integration
OCI
GPU-heavy workloads where price/performance matters
Bare-metal GPUs, very fast RDMA networking, large GPU clusters
Specialist AI clouds
Maximum GPU availability/value
Providers such as CoreWeave can be attractive when you primarily need NVIDIA GPU capacity
1. Google Cloud — my pick for AI-first infrastructure
Google Cloud has arguably the strongest architecture if AI is the primary workload.
Its AI Hypercomputer platform supports everything from relatively small inference deployments to tightly coupled clusters containing thousands of accelerators. Current offerings include NVIDIA H100/H200, B200, GB200/GB300-class infrastructure, as well as Google's TPU ecosystem.
Best for:
Foundation-model training
Large-scale distributed training
High-throughput inference
TPU workloads
GPU-based HPC
AI research
Particularly interesting: Google distinguishes ordinary GPU instances from tightly coupled "clustered GPU" infrastructure, which is important when scaling training across hundreds or thousands of accelerators.
2. AWS — best general-purpose choice
Amazon Web Services is probably the safest choice if you need AI + conventional HPC + mature cloud infrastructure in the same environment.
AWS's P5/P5e/P5en instances use NVIDIA H100/H200 GPUs and can be deployed in UltraClusters with up to 20,000 GPUs, interconnected with high-bandwidth networking. AWS also provides EFA, which is particularly useful for distributed HPC and ML workloads.
The ecosystem is another major advantage:
EC2 GPU instances
EKS/ECS
AWS Batch
AWS ParallelCluster
EFA
S3
FSx for Lustre
That makes AWS especially attractive when you're building a large production system rather than just renting GPUs.
3. Azure — excellent for enterprise AI/HPC
Microsoft Azure is particularly compelling if you're already heavily invested in Microsoft.
Azure's ND-series machines are designed specifically for high-end AI training and HPC. For example, ND H100 v5 provides eight H100 GPUs per VM and 400-Gbps InfiniBand connectivity, with deployments scaling to thousands of GPUs.
Azure also offers AMD MI300X infrastructure. The ND MI300X v5 has eight 192-GB MI300X GPUs, giving it a huge amount of accelerator memory for large models.
Best for:
Enterprise AI
Microsoft-heavy organizations
Large LLM training
GPU HPC
AMD MI300X workloads
Hybrid/on-prem + cloud environments
4. OCI — the one I'd investigate hardest for cost-sensitive GPU/HPC
Oracle Cloud Infrastructure is particularly interesting for raw GPU infrastructure.
OCI offers bare-metal NVIDIA and AMD GPU instances, avoiding some virtualization overhead, together with RDMA-based cluster networking. It also advertises very large GPU clusters, including NVIDIA GB200/Blackwell infrastructure.
This combination makes OCI worth serious consideration for:
LLM training
Fine-tuning
GPU inference
CFD
Scientific computing
Large HPC clusters
If your primary metric is GPU performance per dollar, I would definitely benchmark OCI rather than automatically choosing AWS/Azure/GCP.
What infrastructure should you actually choose?
The cloud provider is only half the decision. For these workloads, I'd prioritize:
For many inference workloads, you don't need an enormous distributed cluster. Google specifically lists configurations ranging from L4/G4-class infrastructure through H100/H200 and newer accelerator clusters for inference.
AI training
Favor:
H100/H200/B200/GB200-class GPUs
NVLink/NVSwitch or equivalent
InfiniBand/RDMA
Fast parallel storage
Very high GPU-to-GPU bandwidth
At this level, network topology can matter almost as much as the GPU itself.
HPC
Favor:
High CPU/GPU performance
InfiniBand/RDMA
MPI support
Parallel filesystem
Low-latency networking
Bare metal when appropriate
AWS's P5 infrastructure, for example, explicitly targets applications such as seismic analysis, molecular modeling, genomics, financial modeling, and engineering simulation in addition to AI.
Bottom line
If I were designing a new system today:
🥇 Google Cloud — best overall for an AI-first organization, particularly large-scale training/inference.
🥈 AWS — best all-around cloud, especially if HPC is equally important and you want the broadest mature ecosystem.
🥉 Azure — excellent choice for enterprise AI/HPC, especially Microsoft-centric organizations and workloads benefiting from MI300X/H100 infrastructure.
💰 OCI — my first benchmark candidate when GPU price/performance and bare-metal performance are critical.
And I wouldn't choose solely on advertised GPU hourly price. For serious training/HPC, I'd benchmark end-to-end cost per completed training job / million inference tokens / simulation, because networking, storage, utilization, and scaling efficiency can dominate the economics.
One final consideration: AI infrastructure availability is increasingly a constraint in itself. Major AI companies are locking in enormous amounts of accelerator capacity through long-term agreements, so capacity availability and guaranteed reservations can be as important as nominal performance.
If you tell me your model size (e.g. 7B/70B/400B), training vs. inference split, expected GPU count, and approximate monthly budget, I can narrow this down to the specific GPU/instance types and give you a GCP vs AWS vs Azure vs OCI cost/performance architecture.
The "best" cloud infrastructure for AI inference, training, and high-performance computing (HPC) depends heavily on whether you prioritize raw compute performance and cost-efficiency or enterprise-grade managed services and global compliance.
CoreWeave: A specialized AI cloud provider built explicitly for heavy GPU and HPC workloads. Because they partner tightly with NVIDIA, they often deploy next-generation hardware early. They consistently score near the top of MLPerf benchmarks for raw speed, and their native Kubernetes-driven architecture handles massive distributed training clusters exceptionally well.
2. Best for End-to-End Managed AI & Proprietary Hardware: Google Cloud Platform (GCP)
Amazon Web Services (): Offers massive global scale and versatility. Through and its custom silicon efforts (Trainium and Inferentia chips), is a powerhouse for structured MLOps pipelines, though standard hyperscaler GPU-hours are priced at a premium compared to niche providers.[](]()](]()]()
4. Best for Agility, Inference, and Cost-Savings: Specialist Clouds (RunPod, Together AI, Lambda Labs)
RunPod & Lambda Labs: Ideal for developers and research teams looking for rapid spin-up times, flexible pod structures, and significantly cheaper per-GPU-hour costs compared to hyperscalers. RunPod is especially popular for flexible serverless inference endpoints and on-demand development.
For Heavy Model Training (LLMs/Foundational Models): Look at Oracle Cloud Infrastructure or Google Cloud for raw bare-metal cluster performance, TPUs, and RDMA networking.
For High-Performance Computing (HPC) & Simulations: Oracle Cloud Infrastructure or specialized providers like CoreWeave.
Corporate Ownership vs. Location: Storing data on a physical server in Frankfurt isn't always enough if the parent company is US-incorporated, as it can theoretically be subpoenaed under the US CLOUD Act. If absolute legal certainty is required, choose an EU-owned provider (like OVHcloud, Hetzner, or IONOS).
Data Processing Agreement (DPA): Ensure the provider offers a signed DPA out-of-the-box that explicitly restricts sub-processors to the EEA.
Hetzner states that its cloud data location depends on the selected product location. Its EU cloud locations include Falkenstein and Nuremberg in Germany, and Helsinki in Finland. It also says support access is generally provided by teams within the EU.
Best suited for: Virtual private servers, dedicated servers and cost-conscious infrastructure hosting.
AWS describes this as an independent European cloud environment with enhanced data-residency controls. Its commitments cover customer content and customer-created metadata within the selected sovereign region, with technical controls intended to prevent access from outside the EU.
Best suited for: Enterprise workloads that need AWS services and stronger European operational controls.
Important: Confirm the availability and scope of the particular services you need.
Microsoft's EU Data Boundary covers specified customer data and personal data for eligible enterprise services, including Azure. However, Microsoft documents limited exceptions and continuing transfers outside the boundary, and some services require specific configuration.
Best suited for: Organizations already using Azure or Microsoft's enterprise ecosystem.
Important: This is not an unconditional guarantee that every piece of data associated with every Azure service stays inside the EU.
IONOS lets customers select data-center locations for supported services and states that customer data remains in their chosen data centers. It offers infrastructure and storage services, including object storage and private cloud options. Check the location and transfer terms for each service.
Best suited for: Business hosting, private cloud infrastructure and storage with a European provider.
A provider's GDPR compliance statement is not, by itself, proof that all data stays in the EU. The European Commission explains that GDPR permits international transfers under specified safeguards; EU-only hosting is a separate requirement you can impose on your own infrastructure.
Use this checklist when evaluating a provider:
EU data residency due-diligence checklist
of
One important distinction: EU-only data storage and EU-only data access are different promises. For example, a cloud provider might store data in Germany while permitting authorized operational access from another country. If your policy requires both, ask for explicit contractual and technical guarantees.
Which should you choose?
For straightforward VPS or dedicated-server hosting: Start with Hetzner.
For a European cloud platform and managed infrastructure: Compare Scaleway and IONOS Cloud.
For enterprise workloads needing a sovereign hyperscale environment: Evaluate AWS European Sovereign Cloud.
For an existing Microsoft environment: Assess Azure's EU Data Boundary service by service.
These are candidates to investigate, not a blanket certification that every product they offer guarantees zero data transfers outside the EU.
One question to narrow this down: What are you planning to host — a website or application, a database with personal information, file backups, or an AI application? The right provider depends heavily on whether you need basic EU server location or a contractual guarantee covering all data processing and access.
The top cloud hosting services that guarantee data residency, ownership, and operations within the European Union (or closely aligned European privacy zones like Switzerland) include:
Scaleway : A French cloud provider focusing on developer-centric infrastructure, multi-cloud setups, and European data residency with datacenters entirely located in France and the Netherlands.[](https://www.exoscale.com/blog/european-cloud-providers/)
: A Germany-based web hosting and cloud pioneer offering high-performance, cost-effective virtual and dedicated servers out of massive data center parks located in Germany and Finland.
If you'd like to narrow down the choice, let me know:
What type of workload you are running (e.g., standard web app, heavy AI/GPUs, Kubernetes, or government data)
Whether you prefer managed services (PaaS) or raw infrastructure/virtual servers (IaaS)
I can recommend the best-fit provider and check their specific data center regions for your project.
1. OVHcloud — Best starting point for broad cloud needs
France · Public cloud, storage, databases, private cloud
OVHcloud offers EU data hosting and a dedicated Trusted Zone option for stricter sovereignty requirements. That option specifies EU-only hosting, processing and operations for eligible services.
Website:
2. Hetzner — For straightforward hosting and infrastructure
Hetzner states that its cloud data location depends on your selected product location. Its EU locations include Falkenstein, Nuremberg and Helsinki. Choose an EU location and check where backups and related services operate.
Website:
3. Scaleway — For developers and cloud-native applications
France · Compute, object storage, managed databases, Kubernetes
Scaleway describes its infrastructure as European and commits to GDPR compliance and keeping data processing within European jurisdiction. Confirm the residency terms for each specific service.
Website:
What you need to verify for GDPR
EU-only storage does not automatically mean GDPR compliance. Before selecting a provider, check these five points:
Data residency: Customer data, backups, replicas and disaster-recovery copies remain inside EU member states.
Processing and metadata: Logs, telemetry, account information and support access are covered by your residency requirement.
Legal jurisdiction: Determine which country's laws apply to the provider and whether non-EU parent companies or subprocessors can access your data.
Data Processing Agreement (DPA): Obtain a signed agreement covering GDPR Article 28, subprocessors, security measures and breach notification.
Security controls: Check encryption, access controls, audit logs, certifications and your ability to manage encryption keys.
For highly sensitive or regulated workloads, OVHcloud's Trusted Zone is worth examining specifically; its commitments are more explicit than simply selecting an EU data-center region.
My recommendation
I'd begin with OVHcloud, compare it with Hetzner if you're mainly hosting servers or websites, and evaluate Scaleway if you need managed developer services.
To narrow this down, what will you be storing in the cloud?
1. What is your workload?
Website or web hosting
Business application or database
File storage and backups
AI or data processing
Healthcare, financial, or other regulated data
2. How strict is your EU-only requirement?
EU storage and processing only
EU storage, processing, and EU-only operational access
Full EU sovereignty, including provider jurisdiction and support
Below are leading European cloud providers that own and operate data centers entirely within the EU:
OVHcloud
Overview : Headquartered in France, OVHcloud is Europe's largest homegrown cloud provider, offering comprehensive IaaS, PaaS, and object storage solutions.
Data Residency : Operates data centers across multiple EU countries (including France, Germany, and Poland) with strict compliance guarantees keeping data inside the EU boundary.
IONOS Cloud
Overview : Part of Germany’s United Internet group, IONOS specializes in scalable cloud infrastructure, virtual servers, and object storage tailored for businesses.
Data Residency : Fully housed and managed out of high-security data centers located exclusively in Germany and other EU regions.
Hetzner Online
Overview : A popular German hosting and cloud provider Hetzner is widely recognized by developers and enterprises for high-performance, cost-effective cloud instances and dedicated storage.
Data Residency : Maintains state-of-the-art data centers located entirely in Germany and Finland.
Scaleway
Overview : A French cloud provider Scaleway provides an S3-compliant object storage ecosystem, Kubernetes, and computing instances powered by eco-friendly regional data centers.
Data Residency : Regions are distributed across France, the Netherlands, and Poland, operating under 100% European ownership.
Open Telekom Cloud
Overview : Operated by Deutsche Telekom Open Telekom Cloud is built specifically to address the stringent data privacy and regulatory needs of European enterprises and public-sector organizations.
Data Residency : Data centers are located strictly in Germany and the Netherlands.
If you can share what specific services you need (e.g., S3-compatible object storage, managed Kubernetes, or raw virtual machines) and your approximate budget or scale , I can narrow down which of these providers is the best technical fit for your project.
For buying physical rackmount server chassis and barebone systems (unpopulated or pre-configured with processors), hardware distributors list live market pricing:
eSaitech Enterprise Store : Carries multi-core rack servers like the MSI Barebone Systems and ASRock Rackmount Servers ranging generally from $2,200 to $3,300+.
Gigabyte Rackmount Servers: DirectDial IT Hardware : Lists enterprise platforms such as Gigabyte Rackmount Servers starting around $2,550.
Supermicro Barebone Servers: New Town Spares : Features entry-to-mid enterprise choices like Supermicro Barebone Servers starting around $510+.
Hourly / On-Demand Billing: Similar to public cloud instances, you are billed by the hour for the time the physical server is active. This is ideal for short-term projects, burst workloads, temporary testing environments, or rendering tasks.[](https://netrouting.com/bare-metal-servers/pricing/)
Unmetered / Flat-Rate Bandwidth: You get a fixed port speed (e.g., 1 Gbps or 10 Gbps uplink) with unlimited data transfer, though providers may enforce a fair-use policy or throttle speeds after a high threshold.[](https://contabo.com/blog/dedicated-server-pricing-guide/)
Metered / Traffic Quota: You receive a specific pool of outbound data transfer per month (e.g., 20 TB included). Exceeding this limit results in extra per-GB overage charges.
95th Percentile Billing: Common for enterprise-grade or high-traffic setups, where bandwidth usage is sampled every 5 minutes, the top 5% of peak usage spikes are discarded, and you are billed for the remaining baseline usage.
Cost Variables & Add-Ons
Hardware Configuration: CPU cores, RAM volume, and storage type (SATA SSD vs. high-performance NVMe arrays) drive the base price tier.
Setup / Provisioning Fees: Some budget or custom-built servers carry a one-time activation or assembly fee, though automated instant-provisioning providers usually waive this.
Software Licensing: Operating systems like Windows Server or control panels like cPanel add recurring monthly licensing fees to the base hardware cost.
Would you like to explore specific price ranges for a certain tier (e.g., budget vs. enterprise), or are you comparing bare metal against a cloud provider like AWS or Azure?
Yes. Bare-metal server hosting is usually sold under a few distinct pricing models, and the differences matter a lot depending on whether your workload is temporary, steady-state, or highly scalable.
Common bare-metal pricing models
Model
How you pay
Typical use
Example
Hourly / on-demand
Pay for each hour the server is provisioned
Dev/test, burst workloads, temporary compute
Limestone lists servers around $0.31–$0.75/hr depending on configuration.
Monthly
Fixed recurring monthly fee
Production workloads running 24/7
Current examples range from roughly $130–$500+/mo for conventional servers, depending heavily on CPU/RAM/storage/network.
Quarterly / semi-annual
Commit for several months, usually for a discount
Stable production infrastructure
Serverside offers quarterly and semi-annual billing, with discounts increasing for longer commitments.
Annual
Commit for 12 months
Predictable long-running workloads
Longer commitments can reduce effective monthly cost; IBM also offers reserved capacity.
Reserved capacity
Commit for 1–3+ years
Large, predictable infrastructure
IBM advertises discounts of up to 35% for 1 year and 55% for 3 years on certain bare-metal reservations.
Custom / quote-based
Provider prices a bespoke machine and contract
High-end, large-scale, specialized hardware
Custom configurations can include unusual RAM, storage, networking, GPUs, or dual CPUs.
What you're actually paying for
The headline server price isn't the whole equation. Bare-metal providers commonly break the bill into:
1. Hardware
CPU / number of cores
RAM
NVMe/SSD/HDD capacity
Number of drives
GPU, if applicable
2. Network
Port speed: 1/10/25/40/100 Gbps
Included monthly transfer
Unmetered vs. metered bandwidth
Additional egress
For example, some providers bundle large traffic allowances, while others price bandwidth separately. ServerRoom currently advertises dedicated unmetered ports rather than per-TB billing.
3. Setup / provisioning
Some providers charge a one-time setup fee, particularly for custom hardware. For example, FormoHost currently shows a $40 setup fee on its listed U.S. bare-metal configurations.
4. Management
Managed OS administration, monitoring, backups, security, control panels, etc. can be additional.
5. IP addresses
Additional IPv4 addresses can carry separate charges.
6. Contract term
The same physical server can have substantially different effective pricing depending on whether you rent it hourly, monthly, or commit for multiple years.
rather than simply comparing the advertised server price.
For example, a server advertised at $200/month with 5 TB of traffic could actually be more expensive for a high-bandwidth application than a $275/month server with 50 TB or unmetered traffic.
Current market examples illustrate the spread: Tevand lists dedicated configurations around $174–$198/month for several 8–32-core configurations, while OpenMetal's more specialized systems range from about $173/month to $4,600+/month.
If you're evaluating bare metal as a hosting business/service, rather than simply buying servers for yourself, I can also break down the provider-side pricing model—e.g. hardware cost → rack/power → bandwidth → support → depreciation → margin—and show how providers arrive at a profitable $/server/month price.
Uses token-based accounts and advertises payment with Bitcoin, Bitcoin Cash, and Monero. It is an interesting option if you want to avoid conventional account registration; available infrastructure includes its own servers and resold cloud instances.
Best for: minimizing account information. Check current availability and whether the server configuration suits your workload.
Accepts Monero and Bitcoin, along with other payment options. Published listings describe email-only signup, with VPS locations including the United States and the Netherlands.
Best for: a privacy-oriented host with conventional VPS infrastructure.
Offers VPS and dedicated servers, with Bitcoin and Monero among the listed payment options. Independent hosting directories describe its VPS offerings as no-KYC. Confirm which account details are required for your chosen location and plan.
Best for: European hosting and a provider with an established hosting focus.
Iceland-based provider listing VPS and other hosting services, with Bitcoin and Monero support. Its registration and account requirements may differ from the more minimal token-based providers above.
Best for: Icelandic hosting and a more conventional hosting account.
Offers VPS hosting and accepts privacy-oriented payment options including Bitcoin and Monero. Listings describe registration via email or XMPP rather than mandatory government ID. Njalla is also notable for its domain-registration model, in which it registers domains on behalf of customers.
Best for: people interested in both privacy-conscious hosting and domain registration.
Sources: provider comparisons updated in September 2026. “No account information” and “no ID” describe published signup policies, not independently guaranteed anonymity.
My recommendations
For the least information at signup: SporeStack is worth investigating because of its token-based, email-free account model.
For Monero payments: IncogNET or Privex are promising options with published in-house crypto processing; verify current policies before ordering.
For a European VPS: FlokiNET is a reasonable starting point.
For multiple cryptocurrencies and minimal registration: Servury advertises no-information signup, although its operating history is shorter than that of established hosts.
Three privacy details worth checking
Payment privacy: Bitcoin is pseudonymous, not inherently anonymous. Monero may offer stronger transaction privacy, depending on how you obtain and use it.
Payment processor: A third-party crypto checkout can create records linking your payment to your hosting account. Check whether the provider processes payments itself.
Server privacy: Even without identity checks, a host may retain connection logs, billing records, or server metadata. Anonymous registration doesn't guarantee anonymous use.
One final question that would help narrow this down: Do you need absolutely no email or personal details, or is an email-only account acceptable as long as there's no ID/KYC check? That distinction changes which providers are the best fit.
Several services operate under a no-KYC/anonymous registration model and support Bitcoin or alternative cryptocurrencies:
bithost : Allows you to deploy and manage cloud instances (abstracting infrastructure from providers like DigitalOcean, Linode, and Vultr) with no ID or document upload. You register with just an email and password and fund your balance using Bitcoin, Monero, Ethereum, and other cryptocurrencies.[](https://bithost.io/)[[2](https://bithost.io/anonymous-vps)](https://bithost.io/anonymous-vps/)
RDP.monster : Offers anonymous VPS and RDP hosting with no KYC, no ID checks, and accepts a wide variety of cryptocurrencies (including privacy-centric coins like Monero as well as Bitcoin and USDT).[](https://rdp.monster/anonymous-vps/)
ExtraVM : Provides offshore and privacy-focused VPS hosting across multiple global locations. They skip identity verification for cryptocurrency orders and accept over 50 different crypto options, including Bitcoin.[](https://extravm.com/crypto-vps)
Note on privacy: While paying with Bitcoin helps avoid credit card trails, standard Bitcoin transactions are pseudonymous rather than fully private. For enhanced anonymity, providers often recommend using privacy coins (like Monero) or funding your payment via a self-custodied wallet rather than a KYC-verified exchange.[](https://bithost.io/anonymous-vps/)[[2](https://bithost.io/anonymous-vps)](https://rdp.monster/anonymous-vps/)
Would you like to explore specific requirements like operating system options, server locations , or budget constraints for your VPS?
SuperBitHost offers offshore, DMCA-ignored VPS hosting across locations like Switzerland, Bulgaria, and Singapore. They require only an email address and a cryptocurrency payment (Bitcoin, Litecoin, etc.) with no identity documents requested at any step.[](https://www.superbithost.com/offshore-bitcoin-vps/)
Email Traceability: Registering with a personal Gmail or Outlook account links the server right back to you. Use an independent, privacy-focused email provider (like ProtonMail) ideally accessed via Tor.[](https://anubizhost.com/blog/best-no-kyc-vps-providers-2026)
Network IP: Logging into your server or creating the account from your real, residential IP address can expose your location unless you route your connection through a secure VPN or Tor.[](https://anubizhost.com/blog/best-no-kyc-vps-providers-2026)
If you'd like, let me know:
Your preferred geographic location for the server (e.g., Europe, Asia)
Whether you need DMCA-ignored offshore hosting or standard offshore infrastructure
I can help narrow down the best specific provider and configuration for your use case.
If your priority is renting a VPS with Bitcoin payments, no government ID, and minimal personal information, there are several providers worth investigating. I searched for providers advertising no-KYC hosting and compared their published signup and payment requirements.
One important distinction: no ID verification isn't the same as complete anonymity. A provider may still retain your email, payment records, IP addresses, or server access logs. Bitcoin transactions are also recorded on a public blockchain.
Best VPS providers to consider
1. VPSDEN
(Best for minimal signup data)
Publishes a no-ID-upload policy, accepts Bitcoin directly, and makes contact details optional. Standard orders use an access key rather than a conventional account with mandatory email.
Bitcoin: Yes, on-chain BTC
ID documents: Not required for standard orders
Email: Optional
Starting price: €11.04 prepaid for 3 months, with 1 vCPU, 2 GB RAM and 30 GB NVMe storage.
The shortest term is three months. Save your access key carefully.
Worth comparing if you want a more established hosting operation. Its anonymous-hosting page advertises Bitcoin payments from the first invoice without an ID check.
Bitcoin: Yes
ID documents: Not required according to its published offer
Email: Required
Starting price: Advertised from $2.48/month on the page found during the search; confirm the current plan and checkout total.
For the strictest signup privacy while still paying with Bitcoin, I'd investigate VPSDEN first and TLDBunker second. Both advertise checkout without required identity documents or a mandatory email address. These are provider-published claims, not independent guarantees of anonymity.
Three things to know before paying
Bitcoin is pseudonymous, not anonymous. Transactions are publicly traceable. If payment privacy is your top concern, VPSDEN and VPS For Life also list Monero (XMR), which is designed to provide stronger transaction privacy.
Check what gets logged. Review the privacy policy for account identifiers, payment records, connection logs, abuse monitoring, retention periods and legal disclosure requirements.
Check the actual checkout flow. Policies can change, and providers may reserve the right to request additional verification in exceptional circumstances.
One question so I can narrow this down: What do you intend to run on the VPS?
Explicitly states that it does not require government ID, passport, or identity verification at any order value. Accepts Bitcoin and offers KVM VPS instances with dedicated IPv4 addresses in 56 countries.
Accepts Bitcoin and other cryptocurrencies. Requires an email and password, but says it does not require ID verification or a credit card. It acts as a privacy layer for VPS infrastructure from major cloud providers.
Accepts Bitcoin and Litecoin, claims not to require identity documents, and offers Linux VPS hosting in six locations, including Germany, Bulgaria, Singapore, and Switzerland. The provider advertises deployment within 24 hours.
For the fewest signup requirements: VPSCrypto advertises no email and no ID.
For a mainstream cloud infrastructure option: bithost resells VPS instances from DigitalOcean, Linode, and Vultr, with hourly billing and Bitcoin top-ups. However, its published Trustpilot profile currently shows a 3.5/5 score from 15 reviews, with a concerning number of negative reviews, so I would start cautiously.
For email-only signup: VPS For Life is another option worth checking.
One important privacy detail: Bitcoin transactions are publicly traceable, even when the hosting provider doesn't ask for your ID. No-KYC hosting also doesn't guarantee that a provider will never suspend an account or retain operational records.
What will you be using the VPS for? A website, VPN, Bitcoin node, remote desktop, or something else? I can narrow this down to the best provider for your workload, budget, and preferred server location.
Here are notable providers known for no-KYC/anonymous VPS hosting paired with Bitcoin support:
bithost : Operating since 2014, bithost acts as a privacy layer by letting you deploy instances (via major networks like DigitalOcean, Vultr, or Linode) using just an email address and cryptocurrency funding (including Bitcoin and Monero).[](https://bithost.io/anonymous-vps/)
VPS For Life : Provides standard KVM virtual private servers ordered with only a working email address. They explicitly state they do not request government IDs or perform KYC checks at any order value, and they accept Bitcoin, USDT, and Ethereum.[](https://vpsforlife.com/anonymous-vps.php)
Note: Always remember that while these services don't require an ID, maintaining operational security also depends on how you access the dashboard (e.g., using a clean email and a VPN/Tor).
Would you like to know more about the pricing plans or specific geographical locations available for any of these providers?
Closed-loop, direct-to-chip cooling in purpose-built AI data centers, with retrofits for existing facilities.
Enterprise cloud and large AI clusters
QTS Data Centers
Liquid-cooling capability through its Freedom design, supporting cooling-intensive workloads and hyperscale deployments.
Large-scale US data center capacity
DataBank
High-density colocation with direct-to-chip cooling, cooling distribution units (CDUs) and pre-installed liquid-cooling connections. Its design supports 250 kW or more per cabinet.
Flexible, high-density colocation
CyrusOne
Its Intelliscale AI data center design supports liquid-to-chip, rear-door heat exchangers and immersion cooling, with configurations designed for up to 300 kW per rack.
Custom AI infrastructure and hyperscale deployments
NTT Global Data Centers
Published deployments of direct-contact liquid cooling and immersion cooling, including at its Navi Mumbai facility in India.
Global colocation and specialized AI deployments
Amazon Web Services (AWS): Implements advanced liquid and hybrid cooling systems for ultra-dense EC2 instances housing next-generation machine learning accelerators.
Google Cloud: Utilizes custom cooling architectures—combining high-efficiency water and targeted liquid cooling loops—to support its Tensor Processing Unit (TPU) pods and dense external GPU deployments.
Oracle Cloud Infrastructure (OCI): Designs high-performance AI clusters that increasingly rely on specialized cooling distributions to handle dense NVIDIA GPU architectures seamlessly.
2. Specialized Colocation & Data Center Operators
Many enterprises do not build raw facilities themselves; instead, they rely on carrier-neutral colocation and wholesale data center providers equipped for liquid cooling:
Equinix: Offers tailored high-density power and liquid-cooling retrofits/deployments across key International Business Exchange (IBX) data centers globally to support AI enterprise deployments.
Digital Realty: Integrates liquid-cooling-ready data halls (supporting high kilowatts-per-rack metrics) across its global footprint, allowing enterprise AI deployments without localized thermal bottlenecks.
CoreWeave & Lambda Labs (AI-Specialized Clouds): Specialized GPU cloud providers that build or partner with facilities specifically engineered from the ground up for high-density direct-to-chip liquid cooling.
Vertiv: A dominant player providing end-to-end thermal management, including direct-to-chip liquid cooling systems and large-scale fluid management utilized by major cloud operators.
Schneider Electric (incorporating Motivair): Delivers integrated data center power alongside precision floor-mounted and rack-level liquid-to-liquid cooling loops.
LiquidStack: Specializes in hyperscale liquid immersion cooling and modular liquid cooling architectures (such as their GigaModular systems).
Submer: Engineers single-phase immersion cooling ecosystems where entire server blades are submerged in dielectric cooling fluids.
Would you like to explore direct-to-chip cooling vs. immersion cooling for a specific AI hardware stack, or are you looking for regional availability from a particular provider?
STULZ – A mission-critical thermal management vendor that expanded its dedicated liquid-cooling manufacturing and R&D footprint to deliver precision direct-to-chip and high-density rack cooling systems for AI.
Delta Electronics – Provides integrated grid-to-chip infrastructure, supplying high-efficiency server power solutions alongside advanced liquid-to-liquid and liquid-to-air CDUs explicitly designed to minimize the footprint of heavy GPU workloads.
Iceotope Technologies – Focuses on precision chassis-level and precision-targeted liquid cooling, enclosing servers in a sealed dielectric environment that dramatically cuts cooling energy and water consumption for high-density blades.
Rittal – Offers heavy-duty direct liquid cooling (DLC) infrastructure, including high-capacity in-row and in-rack CDUs capable of scaling up to megawatt-level outputs to handle extreme thermal densities from modern GPU arrays.
Airedale by Modine – Delivers custom and modular coolant distribution units (CDUs) coupled with intelligent software optimization to balance hybrid air-and-liquid environments for hyperscale and colocation clients.
To help narrow down which supplier fits your expansion, could you share:
Your target rack density (e.g., 50 kW to over 100 kW per rack)
Whether you prefer direct-to-chip (cold plate) or full dielectric immersion cooling
If you are retrofitting an existing facility or building a greenfield site
For a new AI data-center buildout, the supplier landscape is fairly broad, but the most relevant names fall into a few categories. Current evidence shows the market moving strongly toward direct-to-chip (DTC) liquid cooling with coolant distribution units (CDUs) for high-density GPU racks.
Featured in Schneider's GB300 reference architecture for three 1,152-GPU clusters.
Boyd Corporation
Cold plates, CDUs, liquid loops
NVIDIA identifies Boyd among its Blackwell infrastructure providers; NVIDIA also highlights Boyd's high-density AI cooling portfolio.
Delta Electronics
CDUs and complete thermal infrastructure
Its GoCool 3 MW CDU is shipping for high-density AI/HPC deployments.
LG Electronics
Chillers, CDUs, cold plates
Its 2.6 MW CDU qualified as NVIDIA DSX Ready in September 2026, following 600 kW and 1 MW qualifications.
Trane Technologies
CDUs and facility cooling
Offers direct-to-chip CDUs up to 14 MW for high-density AI/HPC environments.
GRC
Single-phase immersion cooling
An alternative when you want to move beyond conventional DTC cooling toward immersion.
Submer
Immersion cooling
Focused on high-density immersion deployments rather than conventional cold-plate/CDU architectures.
NVIDIA's own Blackwell infrastructure ecosystem is useful for supplier screening: it lists Amphenol, Auras, AVC, Boyd, CoolIT, Danfoss, Delta, Flex, Foxconn, Motivair, Parker, Schneider Electric, Vertiv, TE Connectivity, Staubli and others among infrastructure providers.
What I'd put on an RFQ shortlist
For a new high-density GPU hall, I'd separate the procurement into:
Facility-side cooling/chillers — Schneider Electric, Vertiv, Trane, LG and conventional chiller suppliers.
Quick disconnects / fluid connections — CPC, Parker Hannifin, Danfoss, Staubli. TrendForce specifically identifies these suppliers in the NVIDIA GB200 ecosystem.
Immersion — GRC or Submer if your rack/server architecture permits it.
One important procurement point: don't evaluate a CDU in isolation. For Blackwell-class systems, the cold plates, manifolds, QDs, CDU, facility-water temperatures, rack power and server OEM all need to be treated as one thermal architecture. NVIDIA's Blackwell designs are explicitly built around liquid-cooled rack architectures.
If you tell me your target GPU (H200, B200, GB200, GB300, etc.), rack density (kW/rack), and total MW of the expansion, I can narrow this to a 5–8 vendor shortlist and compare cooling capacity, CDU topology, rack compatibility, NVIDIA validation, and likely deployment model.
IONOS Cloud : Part of Germany’s United Internet group, IONOS caters directly to small-and-medium businesses and enterprises requiring strict adherence to European data protection standards and local data centers.[](https://www.exoscale.com/blog/european-cloud-providers/)
Exoscale : A Swiss cloud-native and Kubernetes-focused infrastructure provider engineered specifically for European data residency, operating within Switzerland, Germany, and Austria.[](https://www.exoscale.com/blog/european-cloud-providers/)
Infomaniak : A Swiss eco-friendly hosting and cloud provider operating entirely out of Switzerland, governed by strict Swiss federal data protection laws that align closely with the spirit of the GDPR.[](https://www.exoscale.com/blog/european-cloud-providers/)
