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Choose a cloud accelerator in two stages: first check that the model’s weights, KV cache and serving overhead fit in device memory; then benchmark the configurations that pass that test against your latency and throughput targets. Quantization can shrink the weights, but it does not guarantee the complete workload will fit—or perform well enough.
Start by defining the inference workload
Before comparing cloud instance names, write down what you plan to serve. Accelerator choice depends on more than the model’s parameter count.
- The exact model and parameter count, plus its quantization format.
- The inference engine and the kernels it uses for that format and model architecture.
- Typical and maximum prompt and generation lengths.
- Expected concurrent sequences and batching policy.
- Service targets for time to first token, inter-token latency, and total throughput.
These details determine both memory use and performance. A configuration that works for short prompts at low concurrency may not work for long contexts or a busy service.
Estimate weight memory, then budget for the rest
Use parameter count as a screening estimate
A first approximation is parameter count multiplied by bytes per parameter. AWS Prescriptive Guidance estimates that a 7B model’s weights require about 14 GB at FP16, 7 GB at FP8 or INT8, and 3.5 GB at INT4 or NVFP4. Google Cloud’s 2024 serving guidance gives similar estimates for FP16, FP8/INT8, and 4-bit weights. These figures estimate weights, not total serving memory; actual files and formats can include metadata and alignment details. See AWS Prescriptive Guidance’s accelerator sizing guidance and Google Cloud’s LLM-serving guidance.
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Quantization reduces weight storage. AWS describes its AWQ and GPTQ approaches as converting higher-precision weights to lower-bit formats to reduce GPU memory during inference. Its article discusses approximately 30%–70% lower GPU memory utilization for the WₓAᵧ configurations it covers, compared with the unquantized base model; that range is specific to those examples, not a promise for every model or quantization recipe. Read AWS’s AWQ and GPTQ article for its scope.
Add KV cache and runtime overhead
During generation, the serving system also needs memory for the key-value (KV) cache and runtime workspaces. Cache use varies with context length, concurrency, model and implementation, so advertised GPU memory cannot all be treated as available for weights. Google Cloud’s 2024 guidance suggests allocating up to 80% of GPU memory to weights and preserving 20% for KV cache. Treat that as a rule of thumb, not a universal sizing law; include the overhead of your own serving stack.
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Also distinguish accelerator memory from host RAM. A cloud machine may advertise substantial system memory, but host RAM does not substitute for GPU VRAM or HBM when the model’s working set must reside on the accelerator.
Use memory fit as a gate, not a verdict
Compare the estimated total working set—weights, cache and runtime headroom—with usable accelerator memory. Reject configurations that cannot hold it, whether on one device or across a supported sharded arrangement. Then test the remaining candidates against the actual service targets. AWS Prescriptive Guidance puts the distinction plainly: “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.” See AWS Prescriptive Guidance, “Right-sizing and auto-scaling an inference system”.
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A model can fit and still miss its time-to-first-token, response-latency or throughput target. Benchmark with the intended model, quantization kernels, prompt and generation lengths, concurrency, and batch settings. Record:
- Time to first token and inter-token latency.
- Throughput at the concurrency you expect to serve.
- Peak memory use and remaining headroom.
- Stability under sustained load and during scaling.
Shortlist cloud configurations by capacity and architecture
Provider catalogs offer different device memories, machine shapes and deployment conditions. The figures below are provider-published specifications or examples, not a head-to-head performance ranking. Confirm the current machine configuration and region before relying on them.
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| Provider and family | Accelerator examples | Published memory information | How to use the listing |
|---|---|---|---|
| Google Cloud G2 | NVIDIA L4 | 24 GB GPU memory per L4 | Google positions G2 for cost-optimized inference. Consider it for smaller or lighter workloads only if the full working set and performance target fit. Google Cloud GPU machine families. |
| Google Cloud A2 | NVIDIA A100 | 40 GB and 80 GB variants | Google lists A2 for fine-tuning, large-model work and cost-optimized inference. Check the exact machine variant. Google Cloud GPU machine families. |
| Google Cloud A3 and A4 | H100 or H200 on A3; B200 on A4 | Multiple GPUs and high aggregate device memory; the cited catalog does not establish one pooled memory figure for every configuration | Some families have capacity provisioning or reservation conditions. Aggregate memory is not automatically one contiguous pool; serving software and interconnect determine whether a sharded model can use it. Google Cloud GPU machine families. |
| AWS GPU examples | L4 on g6; L40S on g6e; RTX PRO 6000 Blackwell on g7e; H100 on p5; H200 on p5en; B200 on p6-b200; B300 on p6-b300 | AWS Prescriptive Guidance examples, per accelerator: 22 GB, 44 GB, 96 GB, 80 GB, 141 GB, 180 GB and 268 GB, respectively | These are provider examples. Verify the current region, instance configuration and available capacity. AWS Prescriptive Guidance accelerator examples. |
| AWS Trainium and Inferentia | AWS purpose-built accelerator families | Not stated in the cited compute-family overview | Evaluate only if your model, serving framework and operational tooling support AWS Neuron; these are not drop-in GPU equivalents. AWS EC2 instance types. |
Account for multi-accelerator serving
Multiple devices can make a larger model feasible, but their memories do not automatically behave like a single large accelerator. Check how the serving framework partitions the model, where weights and cache reside, and whether the devices have an interconnect suited to the workload. Spreading inference across GPUs adds communication overhead and operational complexity; more aggregate memory alone does not prove better latency or throughput.
Compare the candidates that pass the memory gate
Once a configuration can hold the working set, compare it on the dimensions that affect this deployment:
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| Dimension | Questions to answer |
|---|---|
| Memory capacity | How much device memory is usable per accelerator? How are weights and cache placed, and how much headroom remains? |
| Performance | Does the tested configuration meet time-to-first-token, inter-token latency and throughput targets at expected concurrency? |
| Quantization support | Does the inference engine support the format, kernels and model architecture? Does output quality meet requirements? |
| Multi-device scaling | What interconnect is available, and how much communication overhead does the chosen partitioning introduce? |
| Price | What are the applicable on-demand, spot or committed rates, and what does cost look like at expected utilization? Comparable current prices are not established by the provider specifications cited here. |
| Availability | Is the configuration offered in your region? Are quota, reservations or capacity constraints relevant, and what is the provisioning lead time? |
| Compatibility and operations | Does the stack support the required runtime, drivers, cloud integration, monitoring, autoscaling and deployment model? |
Verify price, region and capacity before committing
Published catalogs are not a guarantee that a configuration can be provisioned in your chosen region. Check regional availability, quota, reservation requirements and likely provisioning time with the provider. Compare the actual billing mode and current price for the exact configuration, then estimate total cost under your expected utilization, including storage and networking needs. The cited provider material does not establish a comparable on-demand price or regional stock position across these options.
Recheck the catalogs at decision time: accelerator families, specifications and capacity conditions can change. Treat published memory values as a shortlist aid, then validate availability and measure your own serving stack before procurement.
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