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Choose a cloud accelerator in two stages: first determine whether the model’s weights, KV cache, and serving overhead fit in device memory; then benchmark the configurations that fit against your latency and throughput targets. Quantization can shrink model weights, but it does not guarantee that the complete workload fits or performs well.
Start by defining the inference workload
Before comparing accelerator names, pin down what you intend to serve. A configuration that works for one model or traffic pattern may not work for another, even at the same quantization level.
- Model: exact model and parameter count.
- Quantization: format and implementation, such as INT8, FP8, or INT4, plus the inference engine and kernels you plan to use.
- Traffic: prompt and output lengths, expected concurrent sequences, and batching policy.
- Service targets: acceptable time to first token, inter-token latency, and throughput at the concurrency you need.
These details determine both memory use and performance. In particular, long contexts and more concurrent sequences increase KV-cache requirements.
Estimate memory before comparing performance
Calculate the weight floor
A useful first screening estimate is parameter count multiplied by bytes per parameter. AWS Prescriptive Guidance gives a 7B model as approximately 14 GB at FP16, 7 GB at FP8/INT8, and 3.5 GB at INT4/NVFP4. Google Cloud’s 2024 serving guidance gives similar estimates: 14 GB at FP16, 7 GB at FP8/INT8, and 3.5 GB at 4-bit. These figures estimate model weights, not total serving memory. Actual model files and formats can add metadata and alignment details.
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Quantization can reduce the memory needed for weights. AWS describes AWQ and GPTQ as methods that convert higher-precision weights into lower-bit formats, reducing GPU memory use during inference: AWS, “Accelerating LLM inference with post-training weight and activation quantization using AWQ and GPTQ on Amazon SageMaker AI”. The reduction depends on the model, format, and implementation; it should not be treated as a guarantee about the whole serving workload.
Add KV cache and runtime overhead
During inference, the serving stack also needs memory for the KV cache and runtime workspaces. Cache use varies with context length, concurrency, and implementation. Google Cloud’s 2024 guidance suggests allocating up to 80% of GPU memory to weights and reserving 20% for KV cache. Treat that as a rule of thumb from that guidance, not a universal sizing law: some workloads need a different split, and runtime overhead still needs to be accounted for.
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Use usable GPU memory—not host RAM—as the capacity check. Provider catalogs report these separately, and host memory does not substitute for GPU VRAM or HBM when the model’s working set must reside on the accelerator.
Apply the memory gate
Reject a configuration if the estimated weights, KV cache, and runtime needs cannot fit in its available device memory. If the model must be split across accelerators, check that your serving software supports the partitioning and that the devices have a suitable interconnect. Summed memory across GPUs is not automatically one pool: sharding introduces communication overhead and operational complexity.
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Shortlist cloud configurations by capacity and compatibility
Provider catalogs include different accelerator generations, memory capacities, and software paths. The figures below are provider-published examples, not a head-to-head performance comparison. Availability and configuration details can vary by region and change over time.
| Provider configuration | Published accelerator memory or positioning | What to check |
|---|---|---|
| Google Cloud G2 with NVIDIA L4 | 24 GB per L4; Google positions G2 for cost-optimized inference. | Potential candidate for smaller or lightly loaded models only if the full working set and performance target fit. Google Cloud GPU machine families. |
| Google Cloud A2 with NVIDIA A100 | 40 GB and 80 GB A100 variants; Google positions A2 for fine-tuning, large-model, and cost-optimized inference uses. | Confirm the specific machine configuration, accelerator count, and available memory. Google Cloud GPU machine families. |
| Google Cloud A3 with H100 or H200; A4 with B200 | Newer multi-GPU families with higher aggregate device memory. | Check reservation or capacity conditions, device interconnect, and whether your serving framework can use the configuration efficiently. Aggregate memory alone does not establish fit. Google Cloud GPU machine families. |
| AWS g6 with L4 | 22 GB per accelerator in AWS Prescriptive Guidance’s example. | Verify the current instance configuration and regional availability. AWS accelerator memory guidance. |
| AWS g6e with L40S | 44 GB per accelerator in AWS Prescriptive Guidance’s example. | Verify current configuration and regional availability. AWS accelerator memory guidance. |
| AWS g7e with RTX PRO 6000 Blackwell | 96 GB per accelerator in AWS Prescriptive Guidance’s example. | Verify current configuration and regional availability. AWS accelerator memory guidance. |
| AWS p5 with H100; p5en with H200 | 80 GB per H100 and 141 GB per H200 in AWS Prescriptive Guidance’s examples. | Check accelerator count, instance details, and capacity for your region. AWS accelerator memory guidance. |
| AWS p6-b200; p6-b300 | 180 GB per B200 and 268 GB per B300 in AWS Prescriptive Guidance’s examples. | Confirm current availability and deployment conditions for the exact configuration. AWS accelerator memory guidance. |
| AWS Trainium and Inferentia | Memory figures are not stated in the cited AWS compute overview. | Evaluate only if your model, serving framework, and operators support AWS Neuron; these are not drop-in GPU equivalents. AWS accelerated computing. |
Benchmark only configurations that pass the memory gate
Memory fit makes a configuration eligible; it does not make it the winner. AWS Prescriptive Guidance puts the next step this way: “Once viable accelerators have been identified based on memory requirements, the next step is determining whether they can meet the workload’s latency and throughput objectives.”
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Benchmark using the intended model, quantization format, inference engine, prompt and generation lengths, concurrency, and batch settings. Record:
- Time to first token.
- Inter-token latency.
- Throughput at target concurrency.
- Memory headroom under load.
- Stability during sustained serving.
A model may fit yet miss its latency or throughput target. Quantization support also depends on the model architecture, kernels, and serving stack, so validate the actual path rather than assuming a format is supported because an accelerator is available.
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Compare cost, availability, and operational fit
There is no universal cost winner established by the provider specifications here. Compare the exact configurations you have benchmarked at the utilization pattern and billing terms you expect, and verify details at decision time.
- Price: on-demand, spot, or committed rates and total cost at expected utilization.
- Availability: region, quota, reservation or capacity requirements, and provisioning lead time.
- Scaling and operations: startup time, deployment model, monitoring, autoscaling, storage, and network needs.
- Multi-device behavior: interconnect, communication overhead, framework support, and scaling efficiency.
- Software compatibility: inference engine, drivers or runtime, cloud integration, and—on Trainium or Inferentia—the AWS Neuron path.
Provider catalogs document families and deployment conditions, but they do not establish comparable current regional stock or on-demand prices across providers. Verify both, along with quota and reservations, before committing.
Quick Recap
A practical selection sequence
- Fix the workload: record model, parameter count, quantization, serving engine, context range, concurrency, batching, and service targets.
- Estimate weight memory: use parameter count and bytes per parameter for an initial screen, then confirm the actual model format.
- Budget for the rest: account for KV cache at expected context and concurrency, plus runtime and workspace overhead.
- Filter configurations: retain only single-device or supported sharded arrangements that can hold the full working set; check interconnect and software compatibility.
- Benchmark the survivors: measure latency, throughput, memory headroom, and stability on the real serving stack and workload.
- Verify deployment economics: check current region, capacity, quota, billing terms, and total operating cost for the expected utilization.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
