capyctl/bench-results

CapyCTL bench results

Every point measured by capyctl-bench for the 38 recipes and 12 comparisons in capyctl-recipes, in one table: decode speed, time to first token, prompt processing and memory against prompt length and concurrent streams for chat models, and latency, throughput, calibration and accuracy for decision models. vLLM, SGLang, TensorFold, llama.cpp and a custom engine, on an NVIDIA GB10, an RTX 4090 Laptop GPU and two RTX A6000 cards.

Every request went through CapyCTL's inference endpoint, not to the engine directly. Built from capyctl-recipes at 1829ae0. The reports themselves, with their charts, are in the benchmarks Space.

from datasets import load_dataset

points = load_dataset("capyctl/bench-results", "points", split="train")
entries = load_dataset("capyctl/bench-results", "entries", split="train")

Configs

points (11,268 rows)

The data.csv of each of the 53 capyctl-bench reports, stacked, with the recipe or comparison it belongs to.

Column	Meaning
entry	The recipe or comparison directory, such as vllm/gpt-oss-120b-gb10. Joins to entries.
report	The report's directory inside entry: bench for the main one, others for extra studies (bench/overhead, bench-sweetspot).
kind	recipe or comparison.
report_title	The report's title.
series	One results file: an engine, version and setting. Compare series only within one report.
section	context (one stream, prompt length growing), concurrency (streams or users), latency, cache, accuracy and reliability (decision models).
point, x	The point on the x axis: a prompt size such as 32k, a stream count, a question count and state size, or a quality set.
metric, unit	What was measured, such as decode_tps in tok/s or ttft_p50_s in s.
median, min, max, n	The median over n samples and its range. Empty where nothing was measured (n 0).

The chat metrics:

Metric	Definition
ttft_s, ttft_p50_s, ttft_p95_s	Request sent to the first token chunk.
decode_tps	(completion tokens - 1) / (last token chunk - first token chunk): the decode rate one stream sees, drafts included.
tpot_ms	The inverse of decode_tps.
prompt_tps	Prompt tokens / time to first token. Includes queueing and the first decode step, so it understates raw prefill at short prompts.
total_s	Request sent to the end of the stream.
output_bytes_per_s, output_chars_per_s	Content and reasoning over the generation window; compares engines whose tokenizers differ.
aggregate_tps	Concurrency: a round's completion tokens / (first request sent to last stream finished), median over rounds.
per_stream_tps	Concurrency: median decode_tps over every request at that stream count.
draft_acceptance	Accepted / drafted tokens, when the engine reports draft counts.
memory_peak_gib	The highest memory sample taken while the request or round ran (machine RAM in use on unified memory).

The decision-model metrics (latency, requests_per_s, questions_per_s, accuracy, macro_f1, brier, nll, ece, kl, and the reliability bins) are defined in Measure a decision model.

entries (50 rows)

One row per recipe or comparison, from the tables of the capyctl-recipes README: engine, model, hardware, the headline result, the date it was measured, the number of reports, the GitHub url and the main report_url. The native TP2 and concurrency comparisons used their own harness, so they have no rows in points; their evidence is on GitHub.

Reading the numbers

• Compare series within one report. A report puts series side by side only when they ran on the same machine with the same prompts and settings; across reports, prompts, settings, CapyCTL and engine versions can differ.
• Each recipe's README names the exact CapyCTL, engine, model and drafter revisions, the deployment file and the settings. Read it before quoting a number.
• One machine per result, on the date given. These are measurements, not a qualification of an engine on your hardware.
• Each recipe states its thinking setting and sampling parameters. Reasoning tokens count as output tokens, and many runs cap replies with max_tokens.

License

Apache-2.0, as capyctl-recipes. The model names refer to their own repositories and licenses.