Choose inference hardware from the workload outward: define the model, traffic, and service-level objectives (SLOs); confirm the model and runtime state fit in accelerator memory; then benchmark realistic traffic on the actual serving stack. Choose the least costly configuration that meets your latency, throughput, reliability, and availability requirements—not the accelerator with the biggest peak specification.
What should you know about the workload before choosing hardware?
Start with the service you need to run, not a list of GPUs. The same model can require different infrastructure depending on prompt and response lengths, concurrency, and latency targets, as AWS’s inference-sizing guidance explains.
- Model: record the model and parameter count, the precision or quantization you intend to serve, and the inference backend.
- Input and output: capture average and maximum prompt lengths, expected response lengths, and the maximum context your application actually needs.
- Traffic: estimate requests per second, target and peak concurrency, and daily or seasonal demand peaks. Note whether traffic arrives steadily or in bursts.
- Service objectives: set targets for time to first token (TTFT), inter-token latency, end-to-end latency, throughput, availability, and acceptable queueing.
These measures describe different outcomes. A system can generate many tokens per second overall yet still deliver a slow first token or poor response times at peak concurrency. For a language model, prompt processing (prefill) and token generation (decode) have different performance demands, so prompt and output lengths can shift the bottleneck.
Will the model and its runtime state fit in accelerator memory?
Estimate memory for more than model weights. The serving process also needs room for runtime overhead, activations, and the key-value (KV) cache used to retain context during generation. The KV cache grows with context and batch or concurrency, so a model that loads successfully at low concurrency may still run out of memory under the intended production load.
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Include the maximum context your application permits in the estimate. If the application does not need its configured maximum, reducing that limit may leave more memory for KV cache and support greater throughput. Google Cloud discusses context, cache, and inference configuration in its GKE inference best practices.
Memory capacity is a feasibility check, not a performance ranking: an otherwise fast accelerator is not a viable choice if the model and required runtime state do not fit. Confirm fit under realistic concurrency, with the intended serving stack, rather than relying only on the model’s weight size.
When is one host enough, and when do you need multiple GPUs?
A smaller model or a single-host deployment may fit on a general-purpose GPU. Larger models or high-scale serving may call for multiple accelerators, clustered infrastructure, and a fabric capable of handling accelerator-to-accelerator or host-to-host communication. Those choices also change networking and operational requirements; they are not simply a way to add memory.
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Google Cloud’s guidance distinguishes general GPUs from clustered infrastructure by deployment scale, networking, and management model. Its documented inference choices include L4 and T4 options as well as A100, H100, H200, B200, and GB-series systems. These are provider offerings, not a universal ranking or a mapping from a particular model size to a particular accelerator. See Google Cloud’s comparison of general and clustered GPUs and its accelerator infrastructure choices.
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For a multi-GPU or multi-host design, assess whether communication across the interconnect and network can support the model’s serving pattern. GPU memory, memory bandwidth, compute, and networking can each become the limiting factor depending on the model and deployment shape. A larger cluster is not automatically faster or more cost-effective for a given request profile.
How should you benchmark candidates?
Benchmark the intended production service, not an isolated accelerator specification. AWS says, “Throughput sizing should always be based on workload shapes that resemble production traffic.” Its right-sizing guidance also emphasizes that workload shape and service targets affect infrastructure needs.
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- Hold the setup constant: use the intended model, tokenizer, precision or quantization, inference backend, and software configuration for each candidate.
- Replay representative traffic: include realistic prompt and output-length distributions, target concurrency, peak load, and the cache state expected in service.
- Measure more than peak throughput: record TTFT, inter-token latency, end-to-end latency, generated tokens per second, request rate, errors, and cost under load. Include tail behavior where it matters to your SLOs.
- Record the conditions: report model, prompt and output profile, concurrency, backend, hardware, software versions, and cache state so another engineer can reproduce the comparison.
- Test failure and scaling behavior: where relevant, observe how the service responds to demand changes, unavailable capacity, or a failed component—not just its steady-state result.
These measurements distinguish a configuration that meets an SLO at light load from one that sustains it at the concurrency and traffic peaks the service must handle. NVIDIA’s Inference Reference Architecture is another reference for inference deployment considerations.
How do you compare candidates on cost and operations?
First discard candidates that fail memory-fit or SLO tests. Among the remaining options, compare the cost of useful output under representative load—for example, cost per million generated tokens—alongside utilization and scaling behavior. A lower hourly rate is not necessarily lower cost per useful output if it requires more capacity or misses the service target.
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- Capacity and scaling: consider how the system handles peaks, how quickly capacity can scale, and whether spare capacity is needed to meet availability goals.
- Deployment model: weigh the networking and management demands of a clustered or multi-host system against a single-host deployment.
- Operations: account for availability, reservation choices, software ecosystem, operational burden, and recovery from failures.
The practical decision rule is to select the lowest-cost configuration that passes representative production tests and meets the stated objectives. AWS recommends this approach in its inference right-sizing guidance.
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What do published accelerator comparisons tell you?
Provider specifications and example comparisons can help form a shortlist, but they do not replace a benchmark of your model, serving stack, and traffic. The values below retain the scope and qualifications stated by their sources.
| Published figure | What it describes | How to use it |
|---|---|---|
| 24 GB accelerator memory | NVIDIA L4 in Google Cloud G2, as reported by Google Cloud in 2024. | Provider-published capacity; it does not by itself establish model fit at your context and concurrency. |
| 80 GB accelerator memory | NVIDIA H100 in Google Cloud A3, as reported by Google Cloud in 2024. | Provider-published capacity; it is not a workload performance result. |
| 141 GB accelerator memory | NVIDIA H200 in Google Cloud A3 Ultra, in Google Cloud documentation accessed in 2026. | Provider-published capacity; it is not a workload performance result. |
| 300 GB/s bandwidth and 242 TFLOPS peak mixed-precision compute | Google Cloud’s 2024 LLM-serving table reports these L4 values with structural sparsity; it says values without sparsity are half as high. | These are provider-published specifications, not a benchmark of a production workload. |
Google Cloud’s 2024 LLM-serving article reports 13.8× prefill throughput for A3 versus G2 at 5.5× the cost in its depicted benchmark configuration. That result belongs to that setup and should not be generalized to other models or traffic.
AWS’s current guidance, accessed in 2026, gives an illustrative relative comparison of L4 at 1.0× throughput and 1.0× cost; L40S at 2.5× throughput and 1.7× cost; H100 at 3.5× throughput and 3.0× cost; and H200 at 3.8× throughput and 3.5× cost. These are AWS’s relative figures, not a vendor-neutral benchmark or current price quote. Treat them as an example of how one provider frames a comparison, not as a prediction for your workload.
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What information is needed for a specific hardware recommendation?
The title alone does not specify a model, precision, token distribution, SLO, peak concurrency, deployment region, serving framework, facility constraints, or budget. Without those inputs and measurements, an exact GPU count, model-to-instance mapping, or lowest-cost SKU cannot be responsibly named. Current pricing and instance availability also depend on provider and region, so verify them when evaluating a deployment.
Use this worksheet to turn the decision into a testable shortlist:
Quick Recap
- Model and serving: model and parameter count; precision or quantization; tokenizer; inference backend; software versions.
- Request shape: average and maximum input length; output-length distribution; required maximum context; cache assumptions.
- Load: normal and peak requests per second; target and peak concurrency; burst or seasonal pattern.
- Objectives: TTFT, inter-token and end-to-end latency targets; throughput needs; availability and error requirements; acceptable queueing.
- Constraints: region, deployment and networking model, operational capacity, scaling needs, and budget.
- Benchmark record: candidate hardware, workload profile, cache state, latency and throughput results, errors, and cost under load.
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