SPEED-Bench server benchmark

A lightweight SPEED-Bench client for benchmarking an already-running llama-server through its OpenAI-compatible API. It is primarily meant to evaluate speculative decoding (draft model, n-gram, MTP, EAGLE3, ...) by reporting per-category throughput, latency, and draft acceptance.

The dataset handling follows the aiperf SPEED-Bench tutorial, which also documents the dataset layout in more detail.

Install

pip install -r tools/server/bench/speed-bench/requirements.txt

Start a server

The client does not launch the server, so start llama-server yourself first. If you care about throughput numbers, set the client --concurrency to the server's slot count (--np):

llama-server \
  -m target.gguf \
  -c 8192 \
  --port 8080 \
  -ngl 99 -fa on \
  --np 1 \
  --jinja

For speculative decoding, start the server with the appropriate flags for your setup (e.g. a draft model with -md, or --spec-type ngram-mod). See the speculative decoding doc for details.

Run

python tools/server/bench/speed-bench/speed_bench.py \
  --url localhost:8080 \
  --bench qualitative \
  --category coding \
  --osl 1024 \
  --concurrency 1

Options

A few common ones:

• --category all runs every category in the bench.
• --category coding,math runs just those two.
• --bench throughput_8k runs a fixed-input-length throughput split.
• --limit 8 keeps at most 8 samples per category, which is enough for a quick check.

The throughput_{ISL} splits use fixed input lengths (1k - 32k), so they are handy for long-context testing and for comparing different llama-server batching settings (e.g. sweeping -ub / --ubatch-size) on prompts of a known size. Make sure the server -c is large enough for the chosen split. When raising -ub, also raise -b to at least the same value, since the physical ubatch cannot exceed the logical batch.

When --output is given, the JSON file holds the run config, the selected_samples / completed_samples / failed_samples counts, the per-category summary rows, and the per-sample results.

Metrics

The summary prints one row per category plus an overall row:

• samples - how many samples finished successfully.
• avg_prompt_t/s - prefill throughput from llama.cpp (timings.prompt_per_second), averaged over the category's samples.
• avg_pred_t/s - decode throughput from llama.cpp (timings.predicted_per_second), averaged over the category's samples.
• avg_latency - average end-to-end request latency seen by the client.
• accept_rate - accepted / draft_n over the category, or n/a if nothing was drafted (draft_n == 0).

Baseline vs speculative decoding

Save a run from each server with --output, then diff the two JSON files with speed_bench_compare.py.

First, start a plain llama-server (no speculative decoding) and save a baseline:

python tools/server/bench/speed-bench/speed_bench.py \
  --url localhost:8080 \
  --bench qualitative \
  --category all \
  --osl 1024 \
  --concurrency 1 \
  --output baseline.json

Then restart llama-server with speculative decoding enabled and save another run:

python tools/server/bench/speed-bench/speed_bench.py \
  --url localhost:8080 \
  --bench qualitative \
  --category all \
  --osl 1024 \
  --concurrency 1 \
  --output spec.json

Finally compare the two:

python tools/server/bench/speed-bench/speed_bench_compare.py \
  --baseline baseline.json \
  --speculative spec.json

The comparison table adds:

• decode_speedup = spec_avg_pred_t/s / base_avg_pred_t/s
• latency_speedup = base_avg_latency / spec_avg_latency

Keep --bench, --category, --osl, and --limit the same across both runs, otherwise they won't be using the same prompts.