High-memory CPU cloud hosting

When serving a large model (such as a massive LLM or heavy in-memory embedding pipeline) that requires extreme amounts of RAM on CPU, the "best" cloud provider depends on whether you need single-node scale (tens of terabytes on one giant machine) or cost-effective distributed horizontal scaling.

The top major hyperscalers stack up as follows for extreme-memory CPU needs:

1. Amazon Web Services (AWS) — Best for Absolute Maximum Single-Node RAM

AWS offers the broadest and most scalable tier of memory-optimized virtual machines, making it the gold standard if your model refuses to be sharded and must live entirely within the RAM of a single physical server.aws.amazon.com

• Instance Families:
  • R-series (R7i, R8i, etc.): Standard high-memory choices scaling up to 3 TiB of RAM.
  • X2iedn: High memory-to-vCPU ratios going up to 4 TiB of RAM.
  • High Memory (U7i) Instances: Purpose-built massive bare-metal-adjacent instances that scale from 2 TiB up to a staggering 32 TiB of RAM on a single instance.aws.amazon.com+1
• Pros: Unmatched upper limits for single-node scaling; robust ecosystem of management and deployment tools.sedai.io+1
• Cons: On-demand pricing for the multi-terabyte tier is extremely high, and you often have to commit to 1-to-3-year terms or specialized provisioning.
• Explore further: Check out the AWS Memory Optimized Instances documentation.aws.amazon.com

2. Microsoft Azure — Strongest Alternative for Massive Enterprise Memory

Azure matches AWS stride-for-stride at the top end of the scale, offering massive single-node memory configurations tailored for heavy analytics and in-memory databases that easily translate to giant model hosting.learn.microsoft.com

• Instance Families:
  • E-series: General memory-optimized workloads.
  • Mv3 / Msv2 High Memory Series: Virtual machine sizes scaling up to 32 TB of memory with immense networking throughput (up to 185 Gbps) and high storage bandwidth.learn.microsoft.com
• Pros: Excellent memory-to-CPU ratios; exceptional network performance on top-tier sizes to handle heavy inference payload streaming.learn.microsoft.com
• Cons: Complex quota approval processes are often required to spin up multi-terabyte instances on-demand.
• Explore further: Review the specifications on Azure Memory-Optimized Virtual Machines.learn.microsoft.com

3. Google Cloud Platform (GCP) — Best for Custom Flex Sizing & Cost

If your model requires a non-standard ratio of RAM-to-vCPUs, GCP stands out because of its custom machine type flexibility.sedai.io

• Instance Families:
  • High-memory predefined instances in the Compute Engine line.
  • Custom Machine Types: Unlike AWS or Azure where you are locked into fixed ratios, GCP lets you custom-provision exact vCPU counts and dial RAM up independently (up to hundreds of GBs or low-TBs per VM) so you don't over-pay for unused CPU cores just to hit a RAM target.
• Pros: Granular custom configurations mean you only pay for the exact RAM/CPU balance you need; typically competitive on sustained-use discounts.
• Cons: Does not scale up to the massive 32 TiB single-node monolith heights found in AWS/Azure (though more than adequate for 95% of large model use cases).
• Explore further: Look into Google Cloud Compute Engine options.

Summary Recommendation

• Choose AWS (U7i or X-series) if you need a monolithic single-node setup exceeding 6 TiB+ of RAM.
• Choose GCP if your required RAM falls into the sub-6 TiB range and you want to custom-tune the CPU-to-RAM ratio to minimize wasted spend on idle processor cores.aws.amazon.com

To help narrow down the optimal instance type and pricing commitment, could you share:

• How many gigabytes or terabytes of RAM your model actually needs?
• Whether you prefer a single massive server or are open to sharding/distributing across multiple smaller nodes?

For serving a model that requires a very large amount of RAM, I would start by comparing AWS, Google Cloud, Microsoft Azure, and specialized high-memory providers on maximum RAM per instance, RAM-to-vCPU ratio, memory bandwidth, and total cost.

The key distinction is whether your model needs hundreds of gigabytes of RAM or multiple terabytes. For CPU-based inference, you generally want a memory-optimized instance with enough RAM to hold the model weights, runtime overhead, and a reasonable amount of working memory without swapping.

Best cloud providers for high-memory CPU inference

• 

1. Amazon Web Services (AWS EC2 U7i)

(Best default shortlist)

• Maximum RAM: 32 TiB per instance.
• Best for: Large models that must fit entirely in system memory, production workloads, and established cloud infrastructure.
• Considerations: Very large instances may require quota approval and regional availability checks.

AWS lists U7i as a family designed for large in-memory workloads, including large language models.aws.amazon.com+1

AWS U7i specifications

---

• 

2. Google Cloud (Compute Engine X4/X5)

• Maximum RAM: X4 up to 32 TB; X5 up to 48 TB.
• Best for: Extremely large memory footprints, particularly when bare-metal performance is attractive.
• Considerations: Check the exact machine type, region, and provisioning requirements.

Google documents X4 and X5 as bare-metal memory-optimized families.docs.cloud.google.com+1

Google Cloud memory-optimized machines

---

• 

3. Microsoft Azure (Msv3 / Mv3)

• Maximum RAM: Up to approximately 30 TB for the documented Mv3 very-high-memory configuration.
• Best for: Organizations already using Azure, enterprise deployments, and large in-memory workloads.
• Considerations: Compare the specific CPU configuration and memory available to your region.

Azure's Mv3 very-high-memory family includes a configuration with 30,400 GiB of RAM.learn.microsoft.com+1

Azure very-high-memory specifications

Which one would I choose?

These are starting points, not a price ranking. Instance availability and pricing vary by region and purchasing model.

The important caveat for model serving

The most RAM does not necessarily mean the best inference performance. For a CPU-served language model, measure:

• Memory capacity: Model weights plus runtime overhead and KV cache.
• Memory bandwidth: Often a major limit on tokens per second.
• CPU architecture and NUMA: Large instances can have substantial cross-socket memory-access overhead.
• Concurrency: More simultaneous requests require more KV cache and working memory.
• Cost per generated token: Compare measured throughput against the full instance cost, not just hourly price.

If your model is a quantized LLM, it is worth testing a smaller instance with high memory bandwidth before paying for multiple terabytes of RAM.

Let me narrow this down

What is the model's approximate memory requirement, and is it an LLM?

1. Required RAM

128–512 GB

512 GB–1 TB

1–4 TB

4–16 TB

16–48 TB or more

---

2. Model type

Large language model (LLM)

Embedding or reranking model

Other CPU-bound model

---

3. Workload

Production, continuous traffic

Occasional inference

Batch inference

---

4. Main priority

Lowest cost per token

Highest throughput

Maximum RAM in one machine

Simplest production deployment

[Compare the best instances]

With those details, I can narrow this to specific instance families and compare current costs rather than just maximum RAM.