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Benchmark an AI-agent server against the traffic it will actually receive, not a single headline throughput test. Define service-level objectives (SLOs) for time to first token, inter-token latency, complete-request latency, throughput, errors, queueing, availability and cost; replay representative conversations and tool-call traces; sweep load until the service saturates; then repeat the same test after each tuning change. A server is ready only when it meets those SLOs at the planned operating point with burst headroom and acceptable recovery, power and cost.
This guide gives you a repeatable method for comparing GPU servers and inference stacks, including vLLM- or Triton-served models, before production deployment.
Start with the deployment decision and measurable SLOs
A benchmark is useful only if it answers a deployment decision. Write down which server, model configuration or GPU topology you are selecting, the traffic it must serve, and the conditions under which the result will be accepted. Avoid adopting a generic claim such as “lowest latency” or “highest tokens per second.” An agent can feel fast at low concurrency yet miss its user-facing SLO as soon as several conversations call tools at once.
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Define the metrics that determine acceptance
| Metric | What it measures | How to report it |
|---|---|---|
| Time to first token (TTFT) | Delay from request submission until the first streamed output token. | p50, p95 and p99, split by workload class. |
| Inter-token latency (ITL) or time per output token (TPOT) | Spacing between streamed tokens after generation starts. | Distribution and tail values; include pauses caused by batching or queueing. |
| Complete-request latency | Time from submission until the final token or final agent response. | p50, p95 and p99, with input and output lengths alongside them. |
| Request throughput | Completed requests per second at a stated load and success criterion. | Steady-state requests per second, not the brief peak during warm-up. |
| Token throughput | Input, output and total tokens processed per second. | Report each separately; output tokens usually drive generation cost. |
| Queue time and depth | Time and number of requests waiting for a worker, batch or KV-cache space. | Average and tail queue time plus active and waiting requests. |
| Error and timeout rate | Requests that fail, exceed a deadline or are cancelled. | Count transport, server, model and client errors separately. |
| Goodput | Share of requests that both complete and meet the defined SLO. | State the exact SLO; raw throughput without goodput can hide unusable latency. |
| Availability and recovery | Whether the service remains usable during faults, restarts and upgrades. | Record interruption duration, failed requests and time to recover. |
| Cost and power | Economic and energy cost of useful work. | Cost per successful request or useful token, with the hardware, region and billing assumptions. |
Carry the workload qualification with every result. A p95 TTFT measured with short prompts and one output token is not interchangeable with a p95 TTFT measured on long, multi-turn agent conversations.
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Separate interactive turns from asynchronous work
Interactive agents need a responsive first token and predictable tail latency while a person waits. Batch enrichment, document processing and overnight agent jobs can trade latency for higher utilization. Give each class its own SLO, concurrency range and benchmark report. Mixing them into one average encourages a server to optimize for the easier workload while violating the requirements of the harder one.
Mirror the production workload
Model serving performance is highly sensitive to prompt length, generated length, streaming, conversation history and the pattern of arrivals. A realistic benchmark therefore describes traffic as distributions and sequences, not as one fixed prompt.
Include the dimensions that change inference behavior
- Prompt and output lengths: sample the actual distribution of input and generated tokens, including short, median and long cases. Long context changes prefill time and KV-cache consumption; long generation changes decode time.
- Context growth: replay multi-turn conversations with their accumulated history instead of sending every turn as an isolated request.
- Streaming: use the same streaming setting and client buffering as production. Streaming changes what users perceive as latency and exposes inter-token pauses that a one-shot response hides.
- Tool-call loops: represent the model turn, tool invocation, tool result and follow-up model turn. Measure both each inference request and the full agent turn.
- Retries and timeouts: include the retry policy that production will use. A modest server error rate can become a large retry storm when every failed request is sent again simultaneously.
- Parallel fan-out: reproduce agents that call several sub-agents or tools concurrently. The parent turn is complete only after the required branches finish, so fan-out changes tail latency and burstiness.
- Shared prefixes: include system prompts, policy text or retrieved context that many requests share. Prefix reuse can improve throughput, but only if the serving stack and cache settings are configured to exploit it.
- Multimodal payloads: include images, audio or other inputs when the deployed model accepts them. Measure preprocessing and transfer time as well as generation.
- Arrival pattern: reproduce the observed request-rate variability, including bursts and quiet periods. A constant stream alone will not expose autoscaling or queue buildup problems.
Use both trace replay and controlled synthetic traffic
A trace replay preserves the correlations and oddities of real use: repeated users, multi-turn state, tool failures and burst timing. It is the best test of production fidelity. A controlled synthetic workload makes a regression reproducible by holding prompt lengths, output lengths, concurrency and arrival distribution constant. Keep both. Use replay to validate that a candidate behaves like production and synthetic cases to isolate a change or compare two servers fairly.
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Instrument a request with a correlation identifier that follows it through the gateway, model server, tool calls, retries and sub-agent branches. Record the time of each model request, queue entry, first token, final token, tool start and tool completion. This lets you distinguish model-serving delay from network, tool or orchestration delay. Report model-only metrics and end-to-end agent metrics separately; otherwise a fast model can appear slow because a downstream tool is slow, or a slow model can be masked by cached tool results.
Freeze the test environment before measuring
Write a configuration manifest before the first run and archive it with the raw outputs. Change one variable at a time. The following details are part of the result, not incidental setup information.
| Area | Record |
|---|---|
| Model | Model name and revision, tokenizer revision, context limit, precision or quantization and any adapters. |
| Serving stack | Server and backend, version, batching policy, streaming mode, scheduler settings and client/load-generator version. |
| Hardware | GPU model and count, GPU memory, CPU model and cores, system RAM, storage and network interface. |
| Parallelism | Tensor and pipeline parallelism, replica count, model placement and interconnect topology. |
| Runtime | GPU driver, CUDA or equivalent runtime, container image, operating-system kernel and relevant libraries. |
| Serving limits | Maximum context length, maximum batch or concurrent requests, maximum sequence count and KV-cache allocation. |
| Network path | Client location, gateway, TLS or compression settings and any proxy between load generator and server. |
| Test control | Warm-up duration, measurement window, clock synchronization, random seeds where applicable and dataset or trace version. |
Warm up before collecting steady-state data
Load model weights, initialize kernels and fill any caches before the measurement window. Keep cold-start and model-load time in a separate test because it answers a different operational question. If the service will scale from zero or replace replicas during normal operation, cold starts belong in the failure and recovery plan, not mixed into the steady-state latency percentiles.
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Choose a harness that matches the question
Use a dedicated inference benchmark rather than a generic HTTP stress tool. The harness must understand streaming tokens, sequence lengths, concurrency and request-rate control.
| Tool | Useful capabilities | Best use |
|---|---|---|
| NVIDIA GenAI-Perf | Command-line measurement of generative-AI inference throughput and latency; synthetic or dataset inputs; concurrency or request-rate load; streaming; CSV and JSON exports; LLM, vision-language, embedding, ranking and LoRA workloads. | Repeatable measurements for a supported inference endpoint and machine-readable result archives. |
| Kubernetes SIG Inference Perf | Model-server-agnostic comparisons; TTFT, TPOT, ITL, token throughput, goodput; QPS-versus-latency and throughput charts; staged saturation sweeps; trace replay; multi-turn and synthetic agentic workloads. | Apples-to-apples comparisons across serving systems or Kubernetes deployments. |
| MLPerf Inference | Prescribed standardized workloads and conditions administered by MLCommons. | Cross-system reference points. Retain application-specific agent tests for the deployment decision. |
NVIDIA describes GenAI-Perf as “a command line tool for measuring the throughput and latency of generative AI models as served through an inference server.” That scope is narrower than an end-to-end agent test, so pair it with orchestration and tool-call measurements.
Make comparisons fair
- Use the same model revision, tokenizer, quantization, context and output-length distribution.
- Use the same client, network path, warm-up and measurement window.
- Apply the same success and timeout rules. Do not count timed-out work as throughput.
- Compare at the same concurrency or request rate, then find each candidate’s sustainable limit separately.
- Keep optimization settings visible. A result obtained with a larger cache, different precision or more replicas is a different configuration, not a faster version of the same one.
Run a staged load sweep to find saturation
Start below the expected operating point and increase load in defined stages. A single maximum-load run cannot show where latency begins to degrade or how much headroom remains.
- Validate correctness at low load. Send a small number of requests and verify output validity, tool-call parsing, streaming order, cancellation and token accounting.
- Warm the deployment. Load the model, allow kernels and caches to stabilize, and discard warm-up samples.
- Measure a low-concurrency baseline. This reveals intrinsic prefill and decode behavior before queueing dominates.
- Increase concurrency or request rate in stages. Use the same step schedule for every candidate. Powers-of-two concurrency stages are easy to reproduce, but add intermediate stages near the expected operating point.
- Hold each stage long enough for steady state. The window must cover several request completions and the full multi-turn workflow; short bursts can report a misleading peak.
- Repeat with realistic context and output lengths. Run separate short, median and long cases if their behavior differs materially.
- Stop at the first sustained SLO failure or unstable queue. Continue only when you are deliberately mapping overload behavior. Record the last stage that met goodput and the first stage that did not.
Inference Perf supports constant-rate, Poisson, concurrent-user, multi-stage, trace-replay and synthetic-agentic modes. GenAI-Perf supports concurrency or request-rate load and exports results for later analysis. Choose the mode that matches your arrival pattern and retain the exact schedule in the report.
Identify the sustainable operating point
Plot request rate against p95 and p99 latency, goodput and queue depth. Saturation often appears as a sharp rise in queue time before GPU utilization reaches a visual maximum. The sustainable point is the highest load at which the required goodput and tail-latency SLOs still hold for the full measurement window. Select a lower production target if bursts, replica loss or rolling upgrades would otherwise consume all available headroom.
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Never publish one number as “performance.” Every result needs a workload description and a distribution.
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Use percentile latency, not averages alone
Report p50 for the typical request and p95 or p99 for the users who encounter queueing, long contexts or contention. For streaming agents, show TTFT and ITL separately: a response can start quickly and then produce tokens too slowly, or start late but decode smoothly. Complete-request latency belongs in the report because users and orchestrators often wait for the final tool-ready answer.
Account for token and sequence lengths
Include input, output and total token counts with each aggregate. A server that processes more tokens per second may still deliver fewer completed agent turns if its output lengths are longer or if many requests fail. Report request throughput and token throughput together, and state whether failed or cancelled sequences are excluded.
Measure queueing and capacity signals
Capture active requests, waiting requests, queue time, GPU utilization, GPU memory, KV-cache use, CPU utilization, system RAM, network traffic and power. High GPU utilization with low goodput can indicate memory pressure, excessive batching delay or long-tail requests. Low utilization with growing queue depth can indicate a CPU, network, scheduler or placement bottleneck.
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Calculate goodput from the actual SLO
Goodput is not raw requests per second. Count only requests that complete successfully and satisfy the stated TTFT, ITL or end-to-end deadline. Keep the denominator explicit so a candidate cannot appear superior by dropping difficult requests or timing them out.
Apply the method to vLLM, Triton and other model servers
The benchmark method is server-agnostic, but each implementation exposes different controls and telemetry. Treat the endpoint contract as the constant and document every server-specific setting.
For a vLLM deployment
Record the vLLM release, model revision, precision, context limit, scheduler and batching settings, tensor-parallel arrangement, replica count and KV-cache budget. Verify that the client uses the same streaming and sampling parameters in every run. Capture queue time, running and waiting sequences, generated-token counts and GPU memory so that a change in scheduler or cache allocation can be tied to a change in tail latency or throughput.
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For a Triton deployment
Record the Triton release, model repository revision, backend, instance groups, dynamic-batching limits, sequence settings and transport protocol. Confirm that the benchmark’s streaming path exercises the same ensemble or backend used in production. Measure both server-side inference and client-observed latency; serialization, ensemble stages and network transfer can add delay outside the model kernel.
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Tune one variable, then rerun the identical test
Google Cloud’s guidance is to “capture a baseline, tune the inference server, rerun benchmarks, and compare results.” Keep that cycle strict: copy the baseline manifest, change one setting, rerun the identical workload and retain both raw outputs.
Variables worth testing
- Tensor and pipeline parallelism: more parallelism can fit a model or increase capacity, but synchronization and communication can add latency.
- KV-cache allocation: allocating more cache can raise concurrency and throughput, while increasing out-of-memory risk.
- Maximum context length: a high limit protects long conversations but reserves resources and can reduce effective capacity when most requests are short.
- Batching and concurrency limits: larger batches improve utilization until queueing, memory pressure or tail latency outweighs the gain.
- Quantization or precision: measure its effect on speed, memory, output quality and failure rate; a faster configuration is not acceptable if it changes agent behavior beyond your quality tolerance.
- Model placement and routing: test replica assignment, locality and load balancing. A fast GPU can lose its advantage if requests cross a congested interconnect or network path.
Keep quality checks in the benchmark. Validate structured tool calls, safety filters, stop conditions and representative answer quality after every performance change.
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Production incidents often occur during transitions rather than steady state. Add explicit resilience scenarios to the benchmark plan.
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Measure image pull, model-load and readiness time for a new replica. During that interval, record whether traffic queues, fails fast or routes to existing replicas. Decide whether the measured delay is compatible with your autoscaling and deployment policy.
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Replica loss and rolling upgrades
Terminate or drain one replica while traffic is active. Record failed requests, retry amplification, queue growth, recovery time and the performance of the remaining replicas. Repeat during a rolling upgrade to verify that capacity never falls below the interactive SLO.
Timeouts, cancellations and transient network faults
Inject client cancellation, gateway timeout and temporary network failure. Verify that the server stops work when cancellation is honored, does not leak KV-cache entries, and does not create duplicate tool actions when a retry arrives.
Autoscaling signals
Scale on signals that match the workload. Google identifies KV-cache utilization and running requests as useful for latency-sensitive workloads, while waiting-queue size is useful for throughput-sensitive workloads. Test scale-up delay, scale-down behavior and oscillation under a burst-and-idle pattern instead of assuming average CPU or GPU utilization is sufficient.
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Compare GPU servers on more than peak speed
Use a comparison matrix so the purchase or placement decision reflects operational reality.
| Comparison axis | Question to answer |
|---|---|
| SLO attainment | At the planned concurrency, what fraction of requests meets every required SLO? |
| Tail latency | What are p95 and p99 TTFT, ITL and complete-request latency for each workload class? |
| Throughput | What request, input-token and output-token rates are sustainable before goodput falls? |
| Saturation limit | At what load do queues, errors or tail latency become unstable? |
| Memory and KV-cache headroom | How much capacity remains at the operating point, and what happens when it is exhausted? |
| Scaling efficiency | How much useful throughput does an additional GPU or replica add after communication overhead? |
| Failure behavior | How many requests fail during loss, restart, throttling or upgrade, and how quickly does service recover? |
| Energy and cost | What power and cost are required per successful request or useful output token? |
| Operations | Can the stack expose the metrics, tracing and autoscaling signals your platform needs? |
| Software maturity | Are the model, backend, driver and deployment versions supported and reproducible? |
Use standardized results as a reference, not a substitute
MLPerf Inference provides prescribed workloads for cross-system reference under controlled conditions. NVIDIA reported 99% scaling efficiency for a 288-GPU GB300 NVL72 submission in MLPerf Inference v6.1 and up to 3.7× higher throughput for Vera Rubin NVL72 versus GB300 NVL72 in that debut. Those are vendor-reported results for specified systems and conditions, not a universal prediction for an agent workload. Retain the application-specific trace and synthetic-agent tests for the final choice.
Make the go/no-go decision
Approve a server only when the evidence covers the planned operating point and the failure cases that matter to users. A practical decision record contains:
- The workload definition, trace or dataset version, prompt and output-length distributions, streaming behavior and agent workflow.
- Every model, tokenizer, server, driver, runtime, hardware, parallelism, cache and batching setting.
- The load schedule, warm-up rule, measurement window, client version and network path.
- p50, p95 and p99 TTFT, ITL and complete-request latency; request and token throughput; queue metrics; errors; resource use; power and cost.
- The sustainable load that met goodput, the first failing stage and the headroom reserved for bursts.
- Cold-start, replica-loss, timeout, cancellation, retry-storm and rolling-upgrade results.
- The exact tuning change associated with each improvement or regression, plus the raw CSV or JSON output.
The final verdict should name the approved configuration and its operating envelope, not just the fastest hardware. If no candidate meets the SLO at the required load with recovery and budget constraints, the correct outcome is to change the model, workload, architecture or capacity plan before deployment.
Quick Recap
Common benchmark mistakes and fixes
| Mistake | Why it misleads | Fix |
|---|---|---|
| Testing one prompt at one concurrency | Hides context variation, queueing and burst behavior. | Use distributions, trace replay and a staged load sweep. |
| Reporting average latency | Long-tail users disappear in the mean. | Report p50, p95 and p99 for TTFT, ITL and end-to-end latency. |
| Counting generated tokens as success | Timed-out or invalid agent turns can inflate throughput. | Use goodput with an explicit SLO and success definition. |
| Mixing cold starts with warm steady state | One operational event distorts every percentile. | Publish separate cold-start and steady-state tests. |
| Changing several settings at once | You cannot attribute a gain or regression. | Change one variable and rerun the identical test. |
| Ignoring tools and retries | The model-only result does not represent the agent transaction. | Trace tool calls, fan-out, retries and cancellation end to end. |
| Driving load from a distant or overloaded client | Client or network limits look like server limits. | Record the path and verify the load generator has headroom. |
| Choosing on peak GPU utilization | Utilization does not prove latency SLOs or useful throughput. | Use goodput, queueing, tail latency, cost and recovery together. |
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