[shrisha108]

Standard-benchmark result, results-only pull request per docs/BENCHMARK_PROTOCOL.md (Submit and review).

Report: bench/results/2026-10-20-standard-titan-rtx-015453d3/README.md

Protocol strata-qwen38-thinking-v1-draft.1 (draft, labelled as such). Status: complete. 21 of 21 required requests, 0 failed, 0 skipped. 12 of 12 sanity checks passed (instruction 4/4, retrieval 4/4, tool calls 4/4). The optional concurrency stage was not run.

Case	Prompt tok/s	Decode tok/s	TTFT s
short	106.1 (104.5-106.9) n=3	86.3 (82.2-86.8) n=3	0.75 (0.74-0.76) n=3
prompt-4k	955.3 (952.8-962.5) n=3	76.4 (76.1-84.2) n=3	4.31 (4.28-4.32) n=3
prompt-16k	1474.6 (1473.1-1480.5) n=3	79.5 (68.8-81.1) n=3	11.15 (11.11-11.16) n=3

Hardware. 2x NVIDIA TITAN RTX 24 GB (Turing, sm_75) on an HP Z440, Xeon E5-2696 v4 without AVX-512, 125 GiB RAM, NVMe. nvidia-smi topo -m reports NV2 between the cards, but the engine does not use NVLink or peer-to-peer access, so the second card adds expert cache and pipeline depth rather than bandwidth.

Software. Strata v0.2.1, engine 0.1.46.1, commit dea6920b818424c8972ae2c0daf5676bbddde214, built from source with CMAKE_CUDA_ARCHITECTURES=75 (the release ships no Linux engine for Turing). AlmaLinux 9.8, driver 615.71.09.

Model. Original Flash-Next IQ3_S, ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF at revision ed59f92082b1e93c0e96d60a8b11aab089b52f09, source revision de4b8e4d43b917e7706784d8bb445c9af86a3540, at the model's native 262,144-token context.

Track: custom, as the report states. The engine is a source build for sm_75, the vision encoder was built locally for sm_75 as well, and --kv-resident 65536, --ple-io ram, --spec 4 and STRATA_STAGE_TRIM=1 are deliberate choices. The full run config is in the report folder with the API key redacted.

Evidence. The folder holds the runner's own README.md and results.json, outputs.jsonl (every request with its raw usage and timings), engine.log (the start-up and the whole run), server-config.json (the run config, key redacted), config.json (the runner's view of the server), provenance/ (the chat template and the corpus manifest) and artifact-hashes.json over all of them.

Limitations, as the report lists them: the expert cache stays warm across the run, so these are not cold-model measurements; the prompt-prefix reuse is the shared template prefix only; the runner cannot see what else ran on the machine; and the run used a server that was already listening on loopback, so it could not confirm the engine had not answered anything before the warm-up (it had not - the engine was started for this run alone).

[Niko1221]

Thank you for measuring this and writing it up. We are building a Strata website with a page for community benchmarks, and results like these will be added there when it launches, so we are closing this pull request here; the numbers are not lost. If your numbers change with a new version, you are welcome to send them again.