llm-serving-auto-benchmark

name	llm-serving-auto-benchmark
description	Framework-independent LLM serving benchmark skill for comparing SGLang, vLLM, TensorRT-LLM, or another serving framework. Use when a user wants to find the best deployment command for one model across multiple serving frameworks under the same workload, GPU budget, and latency SLA.

LLM Serving Auto Benchmark

Overview

Use this skill to compare LLM serving frameworks such as SGLang, vLLM, and TensorRT-LLM for the same model and workload.

Use a config-driven workflow:

• keep launch-only capacity choices in each framework's base_server_flags
• put the search knobs in search_space
• run the same dataset scenarios for every framework
• generate a bounded candidate list from search_space, with the baseline candidate included first
• keep failed candidates in the result file
• pick the best SLA-passing candidate after normalizing the results

For model-specific starting points, prefer the shipped configs in configs/cookbook-llm/. They define a framework-neutral LLM serving cookbook model set and translate each entry into framework-native SGLang, vLLM, and TensorRT-LLM server flags. Validate those configs before a real run:

python .claude/skills/llm-serving-auto-benchmark/scripts/validate_cookbook_configs.py \
  .claude/skills/llm-serving-auto-benchmark/configs/cookbook-llm

If you have captured target-environment --help files, add --help-dir <artifact-help-dir>. That check only loads configs, verifies the server flag names, and renders candidate commands; it does not launch model servers.

Prefer native tooling when it gives better coverage:

• SGLang: python -m sglang.auto_benchmark when available, otherwise python -m sglang.bench_serving
• vLLM: vllm bench sweep serve for server-parameter sweeps, otherwise vllm serve plus vllm bench serve
• TensorRT-LLM: trtllm-serve for the OpenAI-compatible server plus the TensorRT-LLM serving benchmark client or a common OpenAI-compatible benchmark client

TensorRT-LLM has one hard scope rule in this skill: the server backend is fixed to trtllm-serve serve --backend pytorch. Do not search TensorRT-LLM backend choice. If a request, config, or candidate asks for trt, an engine backend, or any other non-PyTorch TensorRT-LLM server backend, reject that candidate as unsupported for this skill and record the reason. This does not change the benchmark client backend; the TensorRT-LLM benchmark client still uses OpenAI-compatible modes such as --backend openai or --backend openai-chat.

Only pick a winner after each requested framework has had its main serving knobs tuned.

The parameter lists in this skill are not a compatibility contract. They are version-sensitive candidate knob families. Before every real run, record the exact framework version or git commit and verify the concrete CLI flag names with --help in the target environment.

The default search style is framework-neutral: start from a mostly pure-TP baseline, sweep a small set of high-impact runtime knobs, and cap the first pass around 10 candidates per framework. Do not search memory fractions by default.

Validation Environment

This skill is target-agnostic. It assumes any one of the following is available, and nothing more:

• a local GPU host with Docker/Podman and the target framework images pulled;
• a remote GPU host reached via ssh <host> with the framework images already running in a container there;
• a CI runner that can exec into a pre-built image for each framework.

Do not assume a specific operator host name (h100_sglang, b200_*, radixark*, rtx5090_*, etc.) inside this skill's own workflow. The concrete SSH wiring, container names, workspace paths, and HF token plumbing for a given box live in the operator-side per-host skills (for example h100, h100-sglang-diffusion, b200, rtx5090, radixark02, radixark03); this skill only requires that the caller can reach a shell inside a container with sglang, vllm, or tensorrt_llm installed.

Reference files are optional and version-sensitive. Treat historical flag notes as evidence from one image, not as a compatibility guarantee for the next run.

Additional H100 validation on 2026-05-01 used two 2-card models with a bounded search of two SGLang memory-fraction candidates and two vLLM memory-utilization candidates. The workload was random input 512, output 64, 8 prompts, and 2 warmup requests, only to prove the search and summary path can finish quickly.

Model	GPUs	Best SGLang	Best vLLM	Artifact root
Qwen/Qwen3-8B	2x H100, TP=2	sglang_mem086, 21.64 req/s, 1385.05 output tok/s, mean TTFT 70.54 ms	vllm_mem080, 22.88 req/s, 1464.25 output tok/s, mean TTFT 60.56 ms	/data/bbuf/validate/core_skill_validation_20260501/qwen3_8b/auto_benchmark
mistralai/Mistral-7B-Instruct-v0.3	2x H100, TP=2	sglang_mem080, 24.09 req/s, 1541.92 output tok/s, mean TTFT 61.47 ms	vllm_mem090, 24.76 req/s, 1584.54 output tok/s, mean TTFT 58.63 ms	/data/bbuf/validate/core_skill_validation_20260501/mistral_7b_instruct_v03/auto_benchmark

Skill Scope

This skill is a playbook plus a config+validator toolchain, not a turn-key orchestrator. The operator still launches servers, drives workloads, and writes one normalized JSONL row per candidate.

The scripts/ directory contains exactly two tools:

• validate_cookbook_configs.py: load cookbook YAML, render bounded candidate server commands, and check flag names against captured --help snapshots without launching servers.
• compare_benchmark_results.py: turn normalized per-candidate JSONL into the markdown and optional CSV tables described in the Output Contract.

Cookbook configs under configs/cookbook-llm/ must pass the validator. The shorter references/example-plan.yaml is a one-off runtime-plan skeleton and is not expected to pass as-is. Use references/result-schema.md as the single source of truth for SLA key names.

Required Inputs

Collect these before a long run:

• model and tokenizer path, target frameworks, GPU model/count, multi-node allowance, precision, and quantization constraints
• endpoint shape, workload source, dataset scenarios, SLA target, search budget, and artifact output directory
• version manifest: framework package version or git commit, container/Python environment, --help snapshots, and whether each search parameter was accepted by that exact CLI

If real production traffic is the goal, use the real request distribution. A synthetic workload is fine for bring-up and first-pass comparison, but it is not enough for a production choice.

Record each scenario's input/output length distribution in the normalized result rows. This is now part of the profiler handoff contract: if SGLang is slower and sglang-sota-performance invokes llm-torch-profiler-analysis, the profiler workload must reuse the slow SGLang benchmark scenario lengths instead of falling back to its generic prefill 4090->1 and decode 1->2048 defaults.

Known Gotchas

Short list of failure modes that have bitten past validation runs. Check these before starting a long sweep.

• SGLang fa3 attention backends need Hopper or newer. On A100, L40S, RTX 5090, and older GPUs, drop fa3 from the SGLang search_space and keep flashinfer (or triton when FlashInfer is unavailable).
• SGLang bench_serving has two SGLang-facing backends: --backend sglang for the native /generate endpoint and --backend sglang-oai for the OpenAI-compatible endpoint. For cross-framework comparisons, prefer sglang-oai so every framework is measured on the same request path.
• vLLM --enable-dbo only works when the target vLLM image is built with a supported all2all backend. Keep DBO out of the default candidate list unless the operator has verified the image.
• vLLM --max-num-partial-prefills > 1 is model- and runtime-gated. Keep 1 in the default pass; raise only after a preflight with the actual model.
• The historical TensorRT-LLM 1.0.0 validation image accepted --kv_cache_free_gpu_memory_fraction; the older --free_gpu_memory_fraction exited with a CLI error. TensorRT-LLM was refreshed to 1.2.1 stable and 1.3.0 release candidates by 2026-04-28, so re-check the accepted flag name via --help on the target image before a real run.
• The historical TensorRT-LLM 1.0.0 multi-GPU PyTorch-backend validation used --ipc=host, --ulimit memlock=-1, --ulimit stack=67108864, --shm-size=16g, and NCCL_IB_DISABLE=1 (for single-node) or an equivalent NCCL setup. Keep these as a starting point, not as a version-independent requirement.
• The historical TensorRT-LLM 1.0.0 benchmark client took --backend openai or --backend openai-chat; --backend trtllm was rejected. This is separate from the server backend, which is pinned to pytorch by this skill.
• trtllm benchmark_serving --dataset-name random silently falls back to ShareGPT sampling without --random-ids (or --download-path).
• max_seq_len / max_model_len / context_length candidates must cover max(input_len + output_len) across every scenario, including values inside search_space, not just the baseline. The validator checks this; do not bypass it.

Secrets Hygiene

• Never print HF_TOKEN, HUGGINGFACE_HUB_TOKEN, or any upstream API key into a saved artifact. Pass them through container -e VAR (unquoted on the right side so the host value is inherited) and keep them out of server_command and benchmark_command fields written to the result JSONL.
• When a framework echoes the full argv at startup, scrub the log or redact token-shaped substrings before uploading the artifact.

Fairness Rules

Use these rules throughout the benchmark:

• Run every framework on the same GPU type, GPU count, model weights, tokenizer, precision, quantization policy, prompt distribution, output length target, and sampling settings.
• Record framework version, git commit, container image, CUDA/NCCL versions, GPU driver, visible GPU ids, launch command, and benchmark command.
• Warm the server before measuring. Restart or clear state between candidate configurations when cache effects would bias the comparison.
• Compare steady-state fixed-QPS runs separately from burst throughput runs.
• Keep failed candidates in the final results with their failure reason.
• Report both raw throughput and SLA-passing throughput. The fastest failing candidate is not the best deployment command.

Workflow

1. Preflight

Verify all requested frameworks before starting a search:

python -m sglang.launch_server --help
python -m sglang.bench_serving --help
vllm serve --help
vllm serve --help=all
vllm bench serve --help
vllm bench serve --help=all
vllm bench sweep serve --help=all
trtllm-serve serve --help
python -m tensorrt_llm.serve.scripts.benchmark_serving --help

Use the framework-specific --help output in the target environment as the source of truth. Do not keep a stale launch flag just because it appears in an old note.

vLLM 0.19 and newer use grouped help. Plain vllm serve --help only shows the groups, so capture --help=all before deciding whether a search knob exists.

Save these --help outputs into the run artifact directory. If a listed search knob is missing from the current CLI, remove or translate that knob before running the benchmark. Do not silently pass unknown flags.

For TensorRT-LLM, also confirm that trtllm-serve serve --help accepts --backend pytorch. If it does not, mark TensorRT-LLM unsupported in that environment rather than falling back to a different server backend.

For each framework, launch a minimal server, confirm /v1/models or the native model-info endpoint, send one streaming request, run one tiny benchmark with at least 5 requests, then save the launch command, benchmark command, server log, and benchmark output.

Before any GPU-backed smoke run, check the requested GPU ids directly with nvidia-smi. If a requested GPU is already in use, stop and record that fact. Do not silently borrow a different GPU count for a performance comparison. It is fine to run a smaller one-GPU smoke only when the result is clearly labeled as a flow check rather than a fair throughput comparison.

If the target environment runs through containers, follow references/container-runbook.md and save image tags, pull commands, launch/benchmark logs, and cleanup commands.

2. Normalize The Workload

Use one canonical workload for all frameworks. Recommended JSONL row shape:

{"prompt": [{"role": "user", "content": "Summarize this text."}], "output_len": 256}
{"prompt": "Write a short explanation of CUDA graphs.", "output_len": 128}

Optional fields:

{
  "prompt": [{"role": "user", "content": "Use low temperature."}],
  "output_len": 256,
  "extra_request_body": {"temperature": 0.0, "top_p": 0.95},
  "metadata": {"source": "prod-sample"}
}

When converting user data:

• inspect at least 3 rows before conversion
• preserve request-level sampling options in extra_request_body
• do not include the final assistant answer in the prompt when that answer is the target completion
• keep multimodal or tool-call payloads only if all requested frameworks support the chosen endpoint shape

For synthetic bring-up, use the shipped two-scenario shape:

dataset:
  kind: random
  num_prompts: 80
  scenario_names: [chat, summarization]
  input_len: [1000, 8000]
  output_len: [1000, 1000]

Each aligned input_len / output_len pair is one scenario. Do not take the cartesian product unless the user asks for that. Name each scenario and keep the aligned pair in the artifacts. For custom datasets, compute or record representative input_len and output_len buckets, at least p50 and p95 when possible, so later profiler runs can match the slow bucket rather than profiling an unrelated synthetic shape.

Before searching any sequence-length limit, compute the largest input_len + output_len in the dataset. SGLang context_length, vLLM max_model_len, and TensorRT-LLM max_seq_len must be at least that value for every candidate that is expected to run all scenarios.

3. Pick A Search Tier

Use the smallest tier that can answer the user's question:

• Tier 1: smoke and sanity. One baseline plus a few high-impact knobs.
• Tier 2: default. A bounded sweep over the most likely server settings.
• Tier 3: exhaustive. Only when the search space is already tight and the user accepts a long run.

Default budget:

• num_prompts: 80 for the default cross-framework comparison; num_prompts: 20 per scenario is acceptable for a smoke/flow check and must be labeled as such in the artifact (not as a performance result).
• search.max_candidates_per_framework: 10 for the first useful pass
• candidate generation: baseline first, then a bounded product or ordered candidate list from search_space
• at most 5 QPS search rounds unless the user asks for more
• stop early when every candidate in one framework is clearly OOM or fails the basic health check

Keep these in base_server_flags unless the user specifically wants a capacity or memory study:

• SGLang mem_fraction_static
• SGLang schedule_policy
• vLLM gpu_memory_utilization
• TensorRT-LLM kv_cache_free_gpu_memory_fraction

These are real knobs, but they widen the search quickly and often turn a serving comparison into a memory-limit study.

4. Tune SGLang

Prefer the SGLang auto-benchmark runner when the target checkout supports it:

python -m sglang.auto_benchmark run --config /path/to/sglang.yaml

Otherwise launch the server manually and benchmark with:

python -m sglang.bench_serving \
  --backend sglang \
  --dataset-name random \
  --random-input-len 1024 \
  --random-output-len 256 \
  --num-prompts 80 \
  --request-rate 8 \
  --output-file /path/to/sglang/results.json \
  --output-details

Version-sensitive SGLang knob families to verify:

• tp_size, pp_size, dp_size, ep_size
• attention_backend, prefill_attention_backend, decode_attention_backend
• sampling_backend
• max_running_requests, max_queued_requests
• chunked_prefill_size, prefill_max_requests, max_prefill_tokens
• max_total_tokens, page_size
• CUDA graph and piecewise CUDA graph settings
• speculative or EAGLE settings only after the non-speculative baseline is tuned

Keep mem_fraction_static and schedule_policy pinned in the default pass, matching the shared cookbook config style.

For quick smoke tests, it is reasonable to disable CUDA graph and piecewise CUDA graph startup work if the goal is only to prove the framework flow. Record those flags in the artifact. Do not carry that smoke setting into a performance winner unless the user asked to tune eager-mode serving.

5. Tune vLLM

Use vLLM's sweep runner when available:

vllm bench sweep serve \
  --serve-cmd 'vllm serve <model> --port 8000' \
  --bench-cmd 'vllm bench serve --backend vllm --model <model> --port 8000 --dataset-name random --num-prompts 80' \
  --serve-params /path/to/vllm_serve_params.json \
  --bench-params /path/to/vllm_bench_params.json \
  --output-dir /path/to/vllm_results

If sweep support is unavailable, run vllm serve for each candidate and measure with vllm bench serve.

Version-sensitive vLLM knob families to verify:

• tensor, pipeline, data, decode-context, and expert parallelism
• gpu_memory_utilization
• max_num_seqs
• max_num_batched_tokens
• max_model_len
• enable_chunked_prefill, partial prefill limits, and DBO thresholds
• KV cache dtype and block size
• dtype and quantization settings
• CUDA graph capture sizes or eager-mode toggles when relevant
• prefix cache and speculative decoding settings only when the workload needs those features

vLLM should get a normal sweep, not one baseline command. See references/framework-reference.md for native command templates and cross-framework knob families. Confirm each flag on the target image's --help before a run.

Keep gpu_memory_utilization in the baseline for the default pass. Search it only when the question is explicitly about fitting the model or trading capacity against throughput.

Keep DBO and all2all backend settings out of the default pass unless the target vLLM environment is already set up for them. They are real tuning knobs, but a candidate can fail at startup if the required all2all backend is not available. Also preflight concurrent partial prefill before raising max_num_partial_prefills above 1; some model/runtime combinations reject it at startup.

6. Tune TensorRT-LLM

Use trtllm-serve serve as the server entrypoint when the target environment supports it:

trtllm-serve serve <model> \
  --backend pytorch \
  --tp_size <tp> \
  --pp_size <pp> \
  --kv_cache_free_gpu_memory_fraction 0.75 \
  --host 0.0.0.0 \
  --port 8000

Then benchmark the OpenAI-compatible endpoint with the TensorRT-LLM serving benchmark client or with the same OpenAI-compatible client used for the other frameworks.

In the historical TensorRT-LLM 1.0.0 validation image, benchmark_serving --dataset-name random sampled from ShareGPT unless either --download-path or --random-ids was passed. For a fast synthetic smoke test, pass --random-ids, then confirm the behavior on the target TensorRT-LLM image.

TensorRT-LLM flag names are especially version-sensitive. In the validated TensorRT-LLM 1.0.0 image, the KV-cache memory flag accepted by trtllm-serve serve was --kv_cache_free_gpu_memory_fraction, not --free_gpu_memory_fraction. TensorRT-LLM 1.2.1 is the latest stable GitHub release as of 2026-04-28, with 1.3.0 release candidates also published; verify the current flag with trtllm-serve serve --help before running a search on any GPU target.

TensorRT-LLM backend policy for this skill:

• launch the server with --backend pytorch
• keep backend: pytorch in base_server_flags
• do not add backend to search_space
• reject trt, engine-backed serving, or any other non-PyTorch TensorRT-LLM server backend as unsupported for this skill

Version-sensitive TensorRT-LLM knob families to verify:

• tp_size, pp_size, and ep_size
• max batch size, max sequence length, max number of tokens, and KV-cache budget
• inflight batching and scheduler options
• extra LLM API options YAML used by trtllm-serve with the PyTorch backend

The trtllm-serve serve CLI exposes fewer direct runtime knobs than SGLang or vLLM. Use direct flags when they exist, then use --extra_llm_api_options for PyTorch-backend settings that are not top-level CLI flags. Keep unsupported backend or engine requests in the failure table instead of translating them.

Keep kv_cache_free_gpu_memory_fraction in the baseline for the default pass. Search max_batch_size, max_num_tokens, max_seq_len, and validated PyTorch-backend config options first. The server backend remains fixed to pytorch.

7. Normalize Results

Write one JSONL row per candidate using the schema in references/result-schema.md. Then run:

python .claude/skills/llm-serving-auto-benchmark/scripts/compare_benchmark_results.py \
  --input /path/to/candidates.jsonl \
  --output /path/to/summary.md

Rank candidates in this order:

1. SLA passed
2. highest request throughput or goodput
3. highest output token throughput
4. lower mean TTFT
5. lower mean TPOT/ITL
6. lower GPU count or simpler deployment if performance is close

Keep the SLA gate itself unchanged. In the cookbook configs and normalized result schema, TTFT SLA still uses max_p99_ttft_ms and TPOT SLA still uses max_p99_tpot_ms; only the default cross-candidate comparison order switches to mean TTFT and mean TPOT.

Output Contract

Return a compact report with workload/SLA, hardware and framework versions, best deployment-command tables per framework/scenario, one cross-framework comparison table, exact launch and benchmark commands for winners, and artifact paths for workload, raw/normalized results, CSV or markdown summary, and server logs.

When SGLang is not the winner, include a profiler handoff note with the slow SGLang scenario name and the exact input/output lengths or percentile bucket to pass to llm-torch-profiler-analysis.

Include failed or excluded candidates with reasons. Explain that this table is a record of tried configs that were not selected: candidates that failed, were skipped by policy, or completed but missed the SLA. Add caveats for synthetic workloads, incomplete fair searches, or framework-specific parameter substitutions.

Use references/framework-reference.md when you need command templates, source links, or knob-family mappings. Use references/example-plan.yaml as the starting point for a full cross-framework run plan.