The Tool Desk
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Define what the server has to do
Before comparing hardware, describe the workload the server will actually receive. A configuration that works for short prompts and occasional requests may struggle with long contexts or many simultaneous generations.
- Model and software: Record the model and version, framework, serving engine, model format, and required drivers or kernels.
- Request shape: Estimate prompt and output length distributions, maximum context length, request rate, and expected concurrency. Note whether traffic is mainly prompt processing (prefill) or token generation (decode).
- Service objectives: Set a latency objective and specify what it measures: time to first token, inter-token latency, or end-to-end response time. Define the throughput target, such as requests or tokens served within that latency bound.
- Constraints: Include quality requirements, deployment location, budget, power and rack limits, and whether the system must scale across devices or hosts.
These details determine what to test. Google Cloud recommends evaluating throughput against a latency bound with an end-to-end benchmark, rather than choosing a machine from a hardware label alone (Google Cloud’s guide to selecting GPUs for LLM serving on GKE).
Estimate accelerator memory for the whole workload
Model weights are only part of the memory requirement. A useful first-pass estimate is:
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- NVIDIA Volta GV100 Architecture — 4,608 CUDA Cores, 640 1st-Gen Tensor Cores delivering 14 TFLOPS FP32 and 112 TFLOPS deep learning performance for AI training, inference, HPC, and scientific computing workloads
- 32GB HBM2 ECC Memory — 900 GB/s Bandwidth — High-bandwidth memory on a 4096-bit bus with ECC error correction provides the memory capacity and throughput required for the largest AI models, simulations, and datasets
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- NVLink — Scale to 96GB Unified Memory — Connect two V100 GPUs via NVLink at 300 GB/s bi-directional bandwidth to scale GPU memory from 32GB to 96GB for larger AI training and HPC workloads
- Multi-Precision Computing — Supports FP64 (7 TFLOPS), FP32 (14 TFLOPS), FP16 (112 TFLOPS) and INT8 precision modes for flexible deployment across training, inference, and scientific simulation workloads
Required accelerator memory = model weights + inference-server overhead + intermediate activations + (KV cache per sequence × active sequences or batch)
The KV cache stores information used during generation. Its demand depends on sequence length and model configuration, and increases as more sequences are active. Include runtime and allocator buffers, the serving engine, and safety headroom as well as weights and cache.
Google Cloud’s GKE guidance gives 1–2 GB as a typical allowance for inference-server and other system overhead. Treat that as the guide’s estimate, not a fixed amount for every model or server. The same guide works through an example totaling 57 GB of accelerator memory under that example’s model and serving assumptions; that figure is not a general conversion from model size to required memory (Google Cloud’s inference best practices on GKE).
Use the estimate to rule out configurations that cannot hold the working set at the intended context length and concurrency. Then validate it with the actual engine and workload: estimates do not capture every runtime buffer or serving configuration.
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You do not automatically need a GPU for AI inference. CPU execution can be a reasonable candidate for smaller or less demanding workloads if it meets the service objectives. NVIDIA Triton supports CPU inference with OpenVINO; its documentation highlights CPU cores, memory resources, and NUMA layout as relevant factors.
Compare the local CPU and accelerator using the same model, precision, input and output mix, server settings, concurrency, and latency and throughput targets. A one-CPU-versus-one-GPU comparison may not represent a fair or useful server comparison. NVIDIA’s Triton documentation specifically encourages benchmarking on the user’s local CPU hardware (Triton: Accelerating Inference for Deep Learning Models).
For a CPU candidate, check that its core count, memory capacity and bandwidth, NUMA arrangement, and software support suit the workload. Measure it rather than assuming that a CPU is either sufficient or unsuitable based on model size alone.
Choose accelerator capacity and topology after fit
Once memory rules out undersized options, compare compute and memory bandwidth, native support for the intended precision, and whether the software stack can use the devices effectively. If the workload needs multiple accelerators or hosts, also check peer-to-peer connectivity and multi-host networking: technologies such as NVLink and GPUDirect can reduce communication costs in those deployments.
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Google Cloud’s GKE guidance gives the following examples of workload classes and accelerator configurations. They are provider-specific examples, not a universal performance ranking:
| GKE guidance example | Accelerator configurations named |
|---|---|
| Small-model inference | L4 and RTX PRO 6000 |
| Large models on a single host | A100, H100, and B200 |
| Larger deployments | H200 and other configurations |
In that GKE guide’s small-model example, an NVIDIA RTX PRO 6000 cloud configuration is listed with 96 GB of memory per GPU. Check the exact product and configuration for the deployment you are considering; cloud offerings can vary or change (Google Cloud’s inference best practices on GKE).
Compare the complete server, not just the accelerator
An accelerator is one part of the serving system. Compare complete configurations against these axes:
| Axis | What to check |
|---|---|
| Model fit | Weights, runtime overhead, activations, KV cache, and headroom at the target sequence length and concurrency. |
| Latency | End-to-end latency and relevant token-level measures with representative requests. |
| Throughput | Requests or tokens served while remaining within the latency objective. |
| Output quality | Quality at the selected precision or quantization level. |
| Host balance | CPU or vCPU, system memory, NUMA, storage needs for model loading, and network capability. |
| Scaling topology | Device count, peer links, multi-host interconnect, and support in the serving software. |
| Compatibility | Framework, drivers, inference server, kernels, model format, and supported precision. |
| Cost and operations | Purchase or rental cost, power, deployment constraints, region, quota, capacity, and provisioning mode. |
For cloud machines, compare the host CPU, system memory, accelerator memory and count, network, and interconnect alongside the GPU model. Google Cloud’s GPU machine-type documentation provides configurations to check, but region, capacity, and current prices must be verified for the intended deployment (Google Cloud GPU machine types).
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Treat precision and quantization as fit-and-quality decisions
Precision affects both memory use and inference behavior. Lower-precision quantization can reduce memory demand and may improve latency or throughput, but aggressive quantization can noticeably reduce accuracy. Prefer hardware that natively supports the precision you intend to serve, and evaluate output quality as part of the same benchmark used to assess speed and fit.
Do not count a model as viable solely because a quantized version fits. The relevant choice is the configuration that satisfies the memory budget and service targets while preserving acceptable output quality for the application.
Tune serving settings and benchmark under realistic traffic
After selecting compatible candidate hardware, test the full serving configuration. Vary precision, batching, concurrency, number of model instances, and memory reservations where the engine supports them. Use representative prompts and output lengths, concurrency, and traffic patterns; record both latency and throughput rather than relying on a single peak-throughput number.
Serving settings can change utilization as well as response time. For example, Google Cloud’s Cloud Run GPU guidance explains that concurrency set too high can leave requests waiting for GPU access and increase latency, while concurrency set too low can underuse the accelerator and cause excess scale-out. This is specific to that platform, but illustrates why hardware capacity and server configuration must be evaluated together (Google Cloud’s best practices for AI inference on Cloud Run services with GPUs).
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Use a shortlist process that separates feasibility from optimization
- Write down the workload and service objectives. Fix the model, request mix, context, concurrency, quality threshold, latency measure, and throughput target for the first comparison.
- Estimate the working set. Account for weights, runtime overhead, activations, KV cache at the target sequence length and concurrency, and headroom.
- Remove infeasible candidates. Exclude configurations that cannot fit the working set or do not support the required model, precision, or serving software.
- Benchmark CPU and accelerator candidates fairly. Use consistent model, request mix, settings, and service objectives; keep CPU-only when it is a plausible workload match.
- Compare full systems and deployment constraints. Include host resources, device topology, network, power, region, quota, capacity, and cost—not only accelerator specifications.
- Tune and retest. Adjust precision, batching, concurrency, instances, and reservations; confirm quality, latency, and throughput on representative traffic.
Cloud examples are starting points for a shortlist, not guarantees of fit, price, or availability. Verify the current machine configuration and deployment capacity for the region and setup you plan to use, then benchmark your own workload. As Google Cloud puts it, the choice involves trade-offs among features, performance, cost, and availability.
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