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Community Bench: 2026-10-11-community-rtx-3090-qwen3-8-flash-next-unsloth-ud-iq4-xs

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Assessment

Difficulty
1/5
Estimated time
Under an hour
Newbie friendliness
62/100
Issue type
Documentation
Clarity
Clearly specified
Activity status
Active
Tech stack
cpp, linux, python
Domain
performance

Research direction

Start with the attached README.md, results.json, and engine-log-excerpt.txt, then compare the reported setup and measurements with the existing community benchmark entries. This issue contributes a benchmark report; done means the results and their limitations are represented consistently with the project’s benchmark format.

Written by the indexing model from the issue text.

Description

engine-log-excerpt.txt
README.md
results.json

Community benchmark on NVIDIA GeForce RTX 3090

Measured on 2026-10-11 with tools/strata_bench.py (suite 1, 3 runs per test, medians with ranges). Speeds are the engine's own timings; first-token times are measured by the client over HTTP. Speed only: no correctness check was run.

Hardware and software

  • GPU and VRAM: NVIDIA GeForce RTX 3090, 24 GiB VRAM
  • CPU: 13th Gen Intel(R) Core(TM) i7-13700K (16 cores, 24 threads); installed RAM: 62.5 GiB; storage: not measured
  • OS: Linux 7.2.9-2-cachyos; GPU driver: 615.78.08; CUDA: 13.4
  • Strata: project 0.1.43, commit 7695859a; engine 0.1.43
  • Background workloads and power limits: not recorded

Model and configuration

  • Model and quantization: qwen3.8-flash-next-unsloth-ud-iq4_xs
  • Context: 40000 tokens; parallel slots: 1
  • Settings: the install's own configuration, unchanged; thinking off for the suite; temperature 0
strata --pack unsloth-ud-iq4_xs --native Qwen3.8-Flash-Next-UD-IQ4_XS-00001-of-00003.gguf --expert-profile expert-profile.bin --expert-cache auto --prefill auto --spec 4 --mtp rt --max-context 40000 --kv int8 --resident-budget-gib 39 --spec-min-p 0.5

Method

Run by python tools/strata_bench.py --long against the HTTP API of a Strata server (started by the benchmark from the install configuration); model loading is not timed. One warm-up chat and a 1K prompt are discarded first. Every prompt starts with a unique tag, so the conversation cache cannot help the fresh-prompt tests.

  • short chat: the three questions of tools/ab_engine.py, up to 100 new tokens, 3 runs
  • 4K / 16K prompts: generated word lists sized by a calibration request, 24 new tokens, 3 fresh prompts each
  • agent session: system prompt and four tools, then 5 turns that each add about 1500 words of a project file and continue from the model's own earlier answers (the way agent clients resend a conversation), 64 new tokens per turn, 3 sessions; the cache numbers come from the answer's usage (cached_tokens) and timings (prompt_n)
  • concurrency (when run): N requests sent at the same moment, total tokens over the wall time of the burst, latency over all requests

Results

Configuration Actual prompt tokens Reused tokens Generated tokens Runs Prompt tok/s median and range Decode tok/s median and range TTFT seconds median and range
short chat 46 (43-51) 0 100 (100-100) 9 not measured 64.4 (50.1-73.0) 0.45 (0.35-0.50)
4K prompt, fresh 4019 (4019-4019) 0 (0-0) 24 cap 3 1396.5 (1202.7-1407.4) 66.5 (55.0 - 71.7) 2.89 (2.87-3.36)
16K prompt, fresh 16058 (16058-16058) 0 (0-0) 24 cap 3 2042.9 (2025.9-2089.8) 74.7 (66.7 - 76.3) 7.88 (7.70-7.95)
32K prompt, fresh 32049 (32049-32049) 0 (0-0) 24 cap 3 2123.3 (2111.1-2148.8) 57.9 (51.9 - 68.9) 15.12 (14.94-15.21)
agent session, follow-up turns 14485 (14482-14523) at the last turn see below up to 64 3 see below 72.3 (65.4-89.2) 2.59 (2.15-2.78)

Ranges are the lowest and highest of the runs; Runs counts requests that returned a token.

Agent session (conversation cache)

Cache hit over the follow-up turns: 68.7 % (68.6-68.8); earlier-turn tokens read again per session: 73 (3-73) (earlier turns that the cache did not hold; the 13634 (13633-13634) new tokens of the files are read once); first turn (nothing cached) first token: 1.55 s (1.52-1.63).

Turn (run 1) Prompt tokens Reused Read Prompt tok/s First token (s) Decode tok/s
0 609 0 609 378.6 1.632 43.1
1 3713 633 3080 1147.5 2.717 72.3
2 5246 3776 1470 677.3 2.191 65.7
3 8690 5309 3381 1227.6 2.776 65.7
4 11614 8683 2931 1100.3 2.687 74.4
5 14523 11679 2844 1106.8 2.591 71.1

Per-request data: results.json. Engine log lines from the start: engine-log-excerpt.txt (when this run started the server).

Correctness and limitations

Not measured: this is a speed suite. The conversation uses the model's own replies, so token counts differ a little between runs. Machine details come from the server, nvidia-smi/rocm-smi and the OS; background load and power limits are not recorded. Total time of the suite: 162 s.

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Getting set up

This project ships no dev container, Dockerfile or contributing guide, so setting up is up to you: start from its README, and see our first-contribution guide for the general steps.

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

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