Llm Inference Benchmark

Benchmark OpenAI-compatible LLM inference servers

One tool, one methodology, engine-agnostic: sglang.bench_serving as a pure HTTP load generator, always hitting the OpenAI /v1/completions endpoint (every serious engine serves it), always running as a standalone dockerized client in its own net namespace — never docker exec into the server, never --network host. The server's identity (engine, quant, flags) is measured into the log, not assumed: every run opens with a PROVENANCE header.

The point of a sweep is the saturation curve's two knees. Read them off a wide concurrency sweep on a non-binding server: the min knee (highest concurrency where TTFT/TPOT is still flat — the latency-optimal cap) and the max knee (where output throughput goes flat — the throughput ceiling). A production concurrency cap belongs between them — see Finding the saturation knees below.

The reference dataset (Kimi-K2.6 on 8× RTX PRO 6000 Blackwell SE, deployed by deploy-kimi-k26-on-rtx-pro-6000) lives in [REFERENCE.md](REFERENCE.md) — use it as the comparison anchor when re-benchmarking that hardware after an image bump, config change, or engine swap.

Prerequisites

• A server exposing /v1/completions on TARGET_HOST:PORT (local LAN IP or a peer host's).
• The tokenizer's model repo cached on the client host under $HF_HOME (random-ids needs only

the vocab; any same-tokenizer repo works — see cross-host notes in REFERENCE.md).

• Docker + the SGLang image for the client (lmsysorg/sglang:v0.5.12.post1-cu130 by default — the

client is CPU-only; vLLM's image ships no bench tool, so even vLLM servers are benched with this).

Run

bash scripts/bench_sweep.sh                          # local server, conc {1,8,16,32,64,128}, 1024in/256out
TARGET_HOST=192.168.55.227 MODEL_NAME=kimi-k2.6 \
  MODEL_REPO=nvidia/Kimi-K2.6-NVFP4 bash scripts/bench_sweep.sh    # cross-host (peer LAN IP)
CONC="1 8 16 64 128" PROMPTS_PER=8 LOG=./bench.log bash scripts/bench_sweep.sh

| Knob | Default | Meaning | |---|---|---| | TARGET_HOST | this host's LAN IP | server address (a bridged client can't use the server-host's 127.0.0.1) | | PORT | 30000 | server port | | MODEL_NAME | kimi-k2.6 | the request model field — MUST match the server's served name | | MODEL_REPO | moonshotai/Kimi-K2.6 | tokenizer source, resolved offline from the client host's $HF_HOME | | CONC | 1 8 16 32 64 128 | concurrency sweep points (dense enough to locate the knee — c16/c32 matter) | | PROMPTS_PER | 8 | numprompts = PROMPTSPER × concurrency (sustained load — see Methodology) | | IN / OUT | 1024 / 256 | random-ids input/output lengths | | SERVER_NAME | (empty) | LOCAL server container name — stamps its launch flags + KV pool into the header (auto-skipped for remote targets) | | LOG | ./bench.log | output log (PROVENANCE header + per-point metrics + parsed summary table) | | BENCH_IMG | the SGLang image | client image |

Methodology rules (violating these produced wrong numbers — see REFERENCE.md)

1. Sustained load: keep num_prompts = PROMPTS_PER × concurrency (uniform prompts_per=8).

Short bursts never reach steady state and over-report high concurrency (a 192-prompt c128 run read 613 tok/s where the sustained number is 377.7).

1. Identical grid for any comparison — same CONC, PROMPTS_PER, IN/OUT, same endpoint.
2. **Server-side --max-num-seqs (MAX_SEQS) changes what a concurrency column means** (c64 against

a MAX_SEQS=16 server is 16-wide with a 48-deep queue). Never read across tables with different caps.

1. One endpoint, one client network path — the script pins /v1/completions (`--backend

sglang-oai, byte-identical to the vllm` backend) and a bridged (own-netns) client. Client placement/network mode alone moves c1 readings by up to ~10% (see REFERENCE.md); never mix client paths or endpoints within a comparison.

1. Every log opens with a PROVENANCE header (date, tool, server /v1/models, launch flags + KV

pool when local, grid) — a number without its header is not a baseline.

Finding the saturation knees (the point of the sweep)

A wide concurrency sweep on a non-binding server (uncapped, or one fixed cap above your top sweep point with a KV pool that holds it) exists to surface two knees:

• min knee — highest concurrency where TTFT/TPOT is still flat (whichever lifts first; TTFT

usually does) → the latency-optimal cap.

• max knee — where output throughput goes flat → the throughput ceiling (past it, more

concurrency buys only TTFT).

They needn't coincide: TTFT (prefill + queue) usually degrades before output throughput (decode) plateaus, so min knee ≤ max knee (equal only when decode-bound). Put a production cap (--max-num-seqs / SGLang --max-running-requests) in [min knee, max knee] — toward min for latency, max for throughput.

Keep the cap non-binding, or you measure the cap, not the box. Throughput/TPOT are set by the running concurrency (the server cap); --max-concurrency is only the offered concurrency. A cap below your top sweep point fakes a knee at the cap (every offered ≥ cap runs cap-wide → tput + TPOT flat, only TTFT climbs). Never set cap = per-point concurrency (resizes the server and its KV pool each point, erasing the signal — that measures throughput-vs-batch-width, not saturation).

Real knee vs false knee — three checks:

1. TTFT signature — flat-tput + flat-TPOT + linearly rising TTFT = pure queueing (a cap or KV

binding running below offered). A real compute knee instead has tput flatten while TTFT and TPOT rise together, gently — a genuinely wider batch with the GPU as bottleneck, not a queue.

1. running vs offered (engine num_running_reqs/num_waiting_reqs, or vLLM running/pending):

running plateaus at MAX_SEQS → cap binding (raise it); running plateaus below cap and below offered → KV pool exhausted (e.g. marlin's 139K pool caps c128 at ~108 → raise fp8/util); running keeps tracking offered but tput is flat → real compute/comm knee.

1. Raise-and-re-measure — bump MAX_SEQS (and/or KV) at the suspected knee and re-run it; if tput

climbs the knee was an artifact, so keep raising until tput stops responding. The concurrency past which a bigger cap buys no throughput is the real knee.

Picking the production cap — the running batch sets throughput + TPOT; offered load above it just queues, surfacing as TTFT (at a cap of 64: c64 = 64 running / 0 queued, c128 = 64 running / 64 queued — same throughput + TPOT, far worse TTFT). Choose by goal:

• Throughput / batch (no tight SLA) → cap at the max knee (or leave uncapped — past it

throughput is flat anyway; capping there only avoids non-productive running requests and bounds TPOT).

• Interactive / SLA-bound → cap where p95/p99 TTFT (or TPOT) still meets SLA, usually below

the max knee. (E.g. SGLang INT4's throughput knee is ~c32 at 345 tok/s / ~2.3 s TTFT; by c64 throughput is flat but TTFT is ~24 s — an SLA of TTFT cap` point is just cap + queue).

Prefill- vs decode-bound (read it off the same sweep). Two tells: (1) knee gap — min knee ≪ max knee (TTFT degrades well before throughput plateaus) ⇒ prefill/queue is the first bottleneck at this shape; min ≈ max ⇒ decode-bound (combo A: min≈max≈c32, decode-bound; combo B: min≈c32 ≪ max > c128, prefill-contended). (2) c1 rates — prefill rate ≈ IN/TTFT, decode rate ≈ 1/TPOT; compare per-request prefill time (IN/prefill_rate) vs decode time (OUT × TPOT). Boundedness is shape-dependent (the default 1024-in/256-out is input-heavy, 4:1), so to classify the box directly sweep two shapes — prefill-heavy (IN=4096 OUT=16) vs decode-heavy (IN=128 OUT=2048) — and see which one saturates first.

Variants

• Cross-host: TARGET_HOST=; MODEL_REPO must be cached client-side (it's only the

tokenizer — an NVFP4 host benching a remote INT4 server uses its local NVFP4 repo).

• Through a TLS+auth proxy (measures the proxy, e.g. Caddy): run the client manually with

--base-url https:// and -e OPENAI_API_KEY= (auto-sent as Authorization: Bearer). Measured Caddy overhead on the reference host: ~1% throughput, +15 ms TTFT @ c1 — REFERENCE.md.

Troubleshooting

PROVENANCE header prints but zero metrics + instant SWEEP_DONE → a host INPUT firewall is dropping the bridged client (src 172.17.x); " not in HF cache" → cache the tokenizer repo on the client host; empty /v1/models in the header → wrong TARGET_HOST/PORT or server not up; all-? summary rows → requests failing, check MODEL_NAME matches the server. More in [REFERENCE.md](REFERENCE.md).

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: soulmachine
• Source: soulmachine/skills
• License: MIT

Install and usage instructions live in the source repository linked above.