We need to serve a model that requires a very large amount of RAM. What is the best cloud provider for high-memory CPU instances?

If the model needs hundreds of GB to multiple TB of RAM on a single machine, I’d focus on the hyperscalers’ dedicated memory-optimized families rather than ordinary high-memory VMs.

My shortlist

AWS is the first provider I'd investigate if your requirement is simply "put a huge model into RAM and serve it." AWS currently has U7i/U7in/U7inh instances up to 32 TiB (32,768 GiB) of RAM. The U7inh-32tb has 1,920 vCPUs, 200-Gbps networking and 160-Gbps EBS bandwidth.Amazon Web Services, Inc.+1

aws.amazon.com

Google Cloud is a very strong alternative, especially if you don't need 32 TiB in a VM. Its M2 family goes to 12 TiB, while the newer X4 family provides 6–32 TiB on bare metal. Google explicitly positions these families for very high memory-to-vCPU workloads.Google Cloud Documentation

cloud.google.com

Azure's current Msv3/Mdsv3 High Memory family reaches about 15.2 TiB / 15,200 GiB with up to 832 vCPUs. It's compelling if the rest of your infrastructure is already on Azure.Microsoft Learn

But there's an important wrinkle for model serving

If you're talking about an LLM, "lots of RAM" may not be the right optimization target. For example:

• A quantized 70B model can fit comfortably within ~64–128 GB depending on quantization and runtime.
• 400B–500B-class models can push you into the hundreds of GB to >1 TB range.
• If the model actually needs TBs of RAM, CPU inference may be extremely slow compared with GPU inference unless the workload has unusually low throughput requirements.
• If you're using CPU inference deliberately, memory bandwidth can matter more than raw RAM capacity.

So I wouldn't choose the provider solely by maximum RAM.

If you tell me the model (e.g. Llama 405B, Qwen 3 235B, DeepSeek, etc.), quantization, desired tokens/sec, and whether you need one machine or can distribute it across machines, I can narrow this down to the specific instance type and compare approximate hourly/monthly costs across AWS, GCP, Azure, and cheaper providers.

Google Cloud is a very strong alternative, especially if you don't need 32 TiB in a VM. Its M2 family goes to 12 TiB, while the newer X4 family provides 6–32 TiB on bare metal. Google explicitly positions these families for very high memory-to-vCPU workloads.docs.cloud.google.com

cloud.google.com

For a model that needs a very large amount of RAM but primarily CPU inference/training, the “best” cloud provider depends on how much memory you need, whether you need GPUs, and whether you care more about raw capacity, price, or operational simplicity. For high-memory CPU-only instances, the main contenders are:

My usual recommendation by model size

Up to ~1 TB RAM

Look first at:

• AWS R7i/R8i/R9i-style memory optimized instances
• Google Cloud M-series
• Azure M-series

These are easy to operate and give you good CPU, networking, and storage integration.Amazon Web Services, Inc.+1

1–6 TB RAM

Shortlist:

• AWS U7i / High Memory instances
• Google Cloud M-series/X-series
• Azure Msv2/Mv2

These are intended for things like huge in-memory databases, large language models that fit in RAM, and analytics engines.Google Cloud Documentation+1

6–32 TB RAM

The realistic hyperscaler choices are:

• AWS U7i — up to 32 TiB memory.Amazon Web Services, Inc.+1
• Google Cloud X4 bare metal — 6–32 TB memory class machines.Google Cloud Documentation
• Azure Mv2/Msv2 High Memory — up to ~11.4 TB.Microsoft Learn

For serving a large language model specifically

A few practical considerations matter more than the provider:

• RAM bandwidth matters: CPU inference for LLMs is often memory-bandwidth limited, not CPU-core limited.
• Quantization changes everything: a model that needs 1 TB in FP16 may fit in ~250–500 GB with 4-bit quantization.
• NUMA topology matters: very large instances can have multiple CPU sockets; your inference stack needs to handle NUMA well.
• Consider GPUs if latency matters: CPU-only serving can be economical for batch inference, but GPUs usually dominate for interactive latency.

For CPU-only LLM serving, I would typically start by benchmarking:

1. AWS high-memory Intel/AMD instances
2. Google Cloud memory-optimized instances
3. A lower-cost bare-metal provider if you need many always-on machines

If you share:

• model size (e.g., 70B, 405B),
• precision/quantization (FP16, INT8, 4-bit),
• expected requests/sec,
• latency target,
• budget,

I can narrow this down to specific instance families and estimated capacity.

When it comes to serving a model that requires a massive amount of RAM on CPU instances, Amazon Web Services (AWS) stands out as the best overall cloud provider due to the sheer scale and availability of its Amazon EC2 High Memory Instances.aws.amazon.com+1

The "Big Three" cloud providers approach ultra-high-memory workloads quite differently:

1. Amazon Web Services (AWS) — The Leader for Raw Scale

• The Offering: Amazon EC2 High Memory (U-1) Instances
• Max RAM Specs: Ranging from 3 TiB all the way up to 24 TiB of memory in a single instance.
• Why it wins: If your model needs multi-terabytes of RAM on a single node (e.g., massive uncompressed LLMs, giant embedding tables, or in-memory knowledge graphs that exceed standard server limits), AWS is the only major provider offering single virtual instances scaling up to 24 TB of RAM. They are backed by robust enterprise infrastructure, certified for heavy workloads, and tightly integrated with the AWS Nitro System for high network and storage bandwidth.aws.amazon.com+1

2. Microsoft Azure — Strong Enterprise Alternative

• The Offering:Azure Memory Optimized M-series / Msv3 High Memory
• Max RAM Specs: Scales up to 16 TiB (16,384 GB) of RAM on their high-memory specific virtual machines.
• Why consider it: If your enterprise stack is already anchored in Azure or you rely heavily on the Azure ecosystem for Active Directory/identity management, the M-series offers exceptional high-memory capacity. It falls short of AWS's 24 TiB ceiling, but 16 TiB is more than sufficient for the vast majority of extreme memory use cases.learn.microsoft.com+2

3. Google Cloud Platform (GCP) — Best for Flexibility, Lower Caps

• The Offering:Google Cloud Compute Engine Memory-optimized / Highmem Machine Families+1
• Max RAM Specs: Typically scales up to a few terabytes (such as standard highmem configurations scaling up to roughly 3 TiB to 4 TiB depending on the generation, like X4 or M3 machine types).
• Why consider it: GCP is praised for custom machine types and balanced price-to-performance, but it cannot match the extreme single-node multi-terabyte ceilings (12 TiB to 24 TiB) found on AWS or Azure.digitalocean.com

Summary Recommendation

• Choose AWS if you need absolute maximum headroom (single nodes scaling past 16 TiB up to 24 TiB).
• Choose Azure if you need up to 16 TiB inside a tightly managed corporate Azure environment.
• Evaluate whether your model can be split across multiple smaller, networked memory-optimized nodes (using distributed inference frameworks) rather than relying on a single astronomical CPU instance, which can significantly open up your options across all three clouds.learn.microsoft.com+1

To help narrow down the exact instance type and pricing estimate, tell me:

• How many terabytes or gigabytes of RAM does your model strictly require?
• Do you prefer a single massive node or a distributed multi-node setup?

If the model is CPU-only and needs hundreds of GB to multiple TB of RAM, I’d narrow the field to AWS and Azure, with Google Cloud worth considering for workloads that fit below ~4 TB.

AWS currently lists High Memory instances all the way through 24 TB RAM, including u-24tb1.112xlarge and u-24tb1.metal.AWS Documentation+1

Azure goes even further: its current Msv3/Mdsv3 Very High Memory family provides 32 TB (30,400 GiB), with up to 1,792 vCPUs and 185 Gbps networking.Microsoft Learn+1

Google Cloud's M3 Ultramem tops out at 3,904 GB on m3-ultramem-128, so I'd generally eliminate it if you're talking about a model requiring >4 TB in a single machine.Google Cloud Documentation

My practical recommendation

If you need ≤4 TB: compare AWS, Azure, and GCP on price and CPU architecture. The difference in hourly cost can matter much more than the nominal RAM capacity.

If you need 4–15 TB: I'd start with AWS vs. Azure. Both have purpose-built machines in this range. Azure's current Msv3 HM lineup goes from roughly 5.7 TB to 15.2 TB.GitHub

If you need 16–32 TB: Azure deserves the first look, because its current Mv3 VHM machines reach 32 TB. AWS reaches 24 TB.Microsoft Learn+1

But there's an important wrinkle for model serving: RAM capacity isn't the only thing that matters. For a CPU-served LLM, I'd compare:

• RAM bandwidth, not just capacity
• NUMA topology
• CPU generation and core count
• AVX-512 / AMX support if your inference stack can exploit it
• memory-per-core
• whether the model actually fits in one NUMA domain or gets spread across sockets
• hourly/on-demand vs. reserved pricing
• availability of the giant instances in your desired region

For example, an enormous 32-TB machine can be a poor choice for a model that only needs 2 TB but is sensitive to memory bandwidth—you may get substantially better throughput from a smaller, newer CPU configuration.

If you tell me approximately how much RAM the model needs (e.g. 512 GB, 1 TB, 2 TB, 8 TB) and whether it's an LLM/embedding model/etc., I can compare the current AWS/Azure/GCP instances, including CPU architecture, RAM bandwidth, networking, and approximate cost, and identify the configurations I'd benchmark first.

My practical recommendation

If you need ≤4 TB: compare AWS, Azure, and GCP on price and CPU architecture. The difference in hourly cost can matter much more than the nominal RAM capacity.

If you need 4–15 TB: I'd start with AWS vs. Azure. Both have purpose-built machines in this range. Azure's current Msv3 HM lineup goes from roughly 5.7 TB to 15.2 TB.github.com

If you need 16–32 TB: Azure deserves the first look, because its current Mv3 VHM machines reach 32 TB. AWS reaches 24 TB.

But there's an important wrinkle for model serving: RAM capacity isn't the only thing that matters. For a CPU-served LLM, I'd compare:

When serving a model that requires a massive amount of RAM on CPU instances, Amazon Web Services (AWS) stands out as the best overall cloud provider for extreme scale, while Google Cloud Platform (GCP) offers the best balance of flexibility and performance for standard enterprise tiers.

The major hyperscalers approach high-memory configurations differently:

• 1. Amazon Web Services (AWS) — Best for Absolute Scale (Up to 24 TiB)
  • Top Lineup:Amazon EC2 High Memory (U-1) Instances
  • Max RAM: Scales up to an astonishing 24 TiB (24,000 GB) of RAM on bare-metal instances (u-24tb1.metal).
  • Why choose it: If your model or in-memory architecture is so large that it fundamentally cannot fit into standard multi-hundred-gigabyte limits, AWS is unmatched. They offer discrete tiers at 6 TiB, 9 TiB, 12 TiB, 18 TiB, and 24 TiB.
  • Downside: These are hyper-specialized, heavy-duty bare-metal systems typically requiring specific reservation or enterprise arrangement to spin up efficiently.aws.amazon.com+2
• 1. Google Cloud Platform (GCP) — Best for High-Memory-to-vCPU Ratios and Customization
  • Top Lineup: Google Cloud Memory-optimized machine family (M3 / M2 series)
  • Max RAM: Up to 4 TB of RAM (e.g., m3-ultramem-128 with 128 vCPUs and ~3.9 TB RAM).
  • Why choose it: GCP provides roughly 30.5 GB of memory per vCPU core on its ultramem tiers, delivering incredible memory bandwidth (~298 GB/s) which is vital when CPU bottlenecks threaten inference or data loading speeds. GCP's billing model and custom machine type flexibility also make fine-tuning resource allocation straightforward.docs.cloud.google.com+2
• 1. Microsoft Azure — Best for Enterprise Hybrid & Mid-to-High Scaling
  • Top Lineup:Azure Msv3 High Memory Virtual Machines
  • Max RAM: Ranging from 6 TB up to 16 TB of RAM on their newer generation Msv3 high-memory series powered by 4th Gen Intel Xeon Scalable processors.
  • Why choose it: Strong choice if your infrastructure is already deeply integrated into the Microsoft ecosystem or if you require solid localized regional enterprise compliance alongside robust 6 TB–16 TB RAM envelopes.learn.microsoft.com+1

To help narrow down the exact instance type and cost estimates, tell me:

• How much RAM does the model actually need to load and run? (6 TB+, around 4 TB, or under 2 TB?)
• Are you looking for on-demand flexibility or long-term committed use discounts?

If the model genuinely needs hundreds of GB to multiple TB of RAM and is CPU-only, I’d shortlist Google Cloud and Azure first, with AWS as another strong option.

My ranking

Google Cloud is particularly interesting now: its current memory-optimized lineup includes X4 bare-metal instances from 6–32 TB RAM, plus M4/M4N VMs up to 6 TB and M2 VMs up to 12 TB.Google Cloud Documentation+1

Azure's current Msv3/Mdsv3 High Memory VMs offer 6–16 TB RAM, with up to 832 vCPUs.Microsoft Learn

AWS has U7i instances at 6, 8, 12, 24, and 32 TB configurations in its current high-memory lineup. Note that the older U-9tb1/U-12tb1 families are no longer available for new launches, so I'd specifically investigate U7i rather than older AWS recommendations.AWS Documentation+1

What I'd choose

If your model needs ≤4 TB: I'd benchmark Google Cloud M4/M3/M2 vs. Azure Msv3 and choose based largely on price in the region you need.

If it needs 4–12 TB: I'd lean Google Cloud, particularly M2/M4, because it has a very mature large-memory offering and now also has newer generations.Google Cloud Documentation

If it needs >12 TB: Google Cloud X4 is especially compelling because you can get a single bare-metal machine with up to 32 TB RAM.Google Cloud Documentation

If cost is the primary concern: don't choose based on the advertised hourly VM price alone. At this scale, reserved/committed pricing, regional availability, CPU generation, NUMA topology, and memory bandwidth can change the economics substantially.

One other important question: how much RAM does your model actually require (e.g. 512 GB, 1 TB, 4 TB, 16 TB), and does it need x86 or can it run on ARM? If you give me that number, I can compare the specific instance types and current prices across AWS/Azure/GCP/Oracle and tell you which is likely cheapest.

When serving a model that requires a massive amount of RAM (ranging from several hundred gigabytes to multiple terabytes), the "best" cloud provider depends on whether you need extreme scale (multi-TB bare metal) or cost-effective high-density virtual machines (1–2 TB range).

The major hyperscalers handle ultra-high-memory workloads through distinct memory-optimized families:

1. Google Cloud (GCP) — Best for Extreme Scale (Up to 32 TB)

If your model requires single-node multi-terabyte memory space (e.g., massive giant-scale LLMs or in-memory knowledge graphs loaded entirely on CPU), Google Cloud's Compute Engine Memory-optimized Machine Family is a top contender.docs.cloud.google.com+1

• Top Offerings: The X4 bare metal instance family provides massive scale with configurations spanning 6 TB up to 32 TB of memory on Intel Sapphire Rapids platforms. Their M4/M4N and legacy M2/M3 tiers also scale cleanly from 1 TB up to 12 TB.
• Pros: Unmatched single-node RAM ceilings (up to 32 TB) with high vCPU-to-memory ratios.
• Cons: High-end bare metal instances require specific quota approvals and have steep hourly costs.docs.cloud.google.com+2

2. Microsoft Azure — Best for Enterprise Scale & SAP/In-Memory Integration (Up to 32 TB)

Azure competes toe-to-toe with GCP at the ultra-high tier through its heavy-duty M-series.

• Top Offerings: Azure's M-series and Mv3 / M-series high-memory variants (such as Standard_M896ixds ) scale up to 32 TB of RAM paired with massive core counts. For standard large-scale deployment tiers (1 TB to 6 TB), they offer reliable, highly integrated enterprise virtualization.news.ycombinator.com+1
• Pros: Excellent enterprise support, tight integration with corporate Microsoft stacks, and deep regional availability for monster-sized instances.
• Cons: Like GCP/AWS, these tier-1 mammoth sizes require going through a formal quota request process via support tickets.news.ycombinator.com+1

3. Amazon Web Services (AWS) — Best Ecosystem Maturity & Mid-to-High Range (1–6+ TB)

AWS offers a diverse array of memory-optimized choices through the Amazon EC2 Instance Types.aws.amazon.com

• Top Offerings:
  • R-series (e.g., R8a, R9g): Great for standard high-memory footprints up to ~1.5 TB running on efficient architecture (including custom silicon like Graviton or AMD EPYC).
  • High Memory (U-1 / U7i) instances: Scale up to 3 TiB, 6 TiB, and higher multi-terabyte setups purpose-built for massive database and in-memory loads.aws.amazon.com+4
• Pros: The AWS Nitro System provides exceptional EBS and network bandwidth isolation; unmatched ecosystem tooling.usage.ai+3

Summary Recommendation

• Choose Google Cloud (X4 series) if you need the absolute highest single-node memory ceiling (pushing past 12 TB up to 32 TB).
• Choose AWS (R-series or High Memory) if you want the most seamless ecosystem tooling, predictable networking performance via Nitro, and footprints under 6 TB.
• Choose Azure if your infrastructure is already anchored in the Microsoft ecosystem and you need certified enterprise scaling.aws.amazon.com+4

To help narrow down the ideal instance type and cost model, could you share:

• How much RAM your model precisely requires (e.g., 500 GB, 2 TB, 16 TB+)?
• Whether you prefer a single massive node or a distributed multi-node CPU cluster?
• Your preferred cloud ecosystem (if any)?

If the model is CPU-only and RAM-hungry, my first choice today would be Google Cloud, with Azure a very close second.

My ranking

Google's current Compute Engine lineup is particularly compelling: X4 bare-metal instances range from 6–32 TB RAM, while M2 goes to 12 TB.Google Cloud Documentation+1

Azure's current Msv3 High Memory instances go up to 15,200 GiB (~16 TB), with up to 832 vCPUs, and offer substantial network/storage bandwidth.Microsoft Learn

AWS is a little less attractive for this specific requirement. Its U-9tb1 through U-24tb1 instances are now listed as no longer available for new launches, with AWS recommending U7i instead.AWS Documentation

But there's an important distinction

If by "model" you mean something like a large LLM running entirely in system RAM, I would not simply pick the instance with the most RAM. I'd optimize for:

• RAM capacity — obviously, but leave ~10–20% headroom for OS/runtime overhead.
• Memory bandwidth — often more important than raw vCPU count for CPU inference.
• NUMA topology — huge machines can have multiple NUMA domains, which can materially affect inference performance.
• CPU instruction set — AVX-512 / AMX support can make a large difference depending on your inference engine.
• Network bandwidth — particularly if you're sharding the model across multiple machines.
• Hourly price / committed pricing — these machines can cost many thousands of dollars per month.

For example, if your model needs ~500 GB, I'd look at a much smaller memory-optimized instance rather than jumping to a multi-terabyte machine. If it needs 2–4 TB, Google Cloud's M3/M4 or Azure's Msv3 offerings become particularly interesting. If it needs 10+ TB, I'd strongly investigate Google's X4 and Azure Msv3 HM.

If you tell me the model size (e.g. 500 GB, 1 TB, 4 TB, 10 TB), quantization (FP16/INT8/4-bit), and whether inference is single-process or distributed, I can compare the specific instances and estimate the $/hour and $/million tokens for AWS vs. Azure vs. GCP.

AWS is a little less attractive for this specific requirement. Its U-9tb1 through U-24tb1 instances are now listed as no longer available for new launches, with AWS recommending U7i instead.

But there's an important distinction