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Community benchmark on rtx-3050-plus-quadro-p6000-qwen3-8-flash-next-q2-0

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Assessment

Difficulty
1/5
Estimated time
Under an hour
Newbie friendliness
90/100
Issue type
Documentation
Clarity
Clearly specified
Activity status
Active
Tech stack
cpp, linux
Domain
documentation

Research direction

This issue contributes benchmark results and environment details; start by reading the repository's benchmark documentation and the linked README.md, then compare the report with the benchmark format used by tools/strata_bench.py. Done means the RTX 3050 and Quadro P6000 results, measurement method, and limitations are recorded in the project's documented location.

Written by the indexing model from the issue text.

Description

Community benchmark on NVIDIA GeForce RTX 3050 + Quadro P6000

Measured on 2026-10-12 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 3050 + Quadro P6000, 30 GiB VRAM
  • CPU: Intel(R) Xeon(R) CPU X5670 @ 2.93GHz (12 cores, 24 threads); installed RAM: 94.4 GiB; storage: not measured
  • OS: Linux 6.8.0-146-generic; GPU driver: 580.178.04; CUDA: 13.0
  • Strata: project 0.1.43.1, commit a0116b27; engine 0.1.43.1
  • Background workloads and power limits: not recorded

Model and configuration

  • Model and quantization: qwen3.8-flash-next-q2_0
  • Context: 131072 tokens; parallel slots: 1
  • Settings: the install's own configuration, unchanged; thinking off for the suite; temperature 0
strata --pack q2_0 --native Qwen3.8-Flash-Next-GSQ-RCO-Q2_0-00001-of-00002.gguf --ple-gguf Qwen3.8-Flash-Next-GSQ-RCO-Q2_0-00002-of-00002.gguf --expert-profile expert-profile.bin --expert-cache auto --prefill auto --spec 4 --mtp rt --max-context 131072 --kv int8 --kv-resident 32768 --vision --vram-reserve-mib 700 --control-vector-scaled Qwen3.8-Flash-Next-experimental-speed-projection.gguf:1.0 --control-vector-layer-range 4 44 --cvec-mode project --cvec-dir per-layer --pcie-frac 0.55 --spec-min-p 0.70 --adapt-every 1 --adapt-swaps 80 --adapt-decay 0.97

Method

Run by python tools/strata_bench.py 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 33.4 (27.1-40.7) 1.06 (0.90-1.60)
4K prompt, fresh 4019 (4019-4019) 0 (0-0) 24 cap 3 253.6 (252.7-256.1) not measured 15.90 (15.74-15.95)
16K prompt, fresh 16058 (16058-16058) 0 (0-0) 24 cap 3 244.8 (242.3-249.0) not measured 65.65 (64.57-66.34)
agent session, follow-up turns 14464 (14416-14475) at the last turn see below up to 64 3 see below 33.8 (22.1-39.4) 17.67 (9.14-21.18)

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: 68.6 % (68.6-68.8); earlier-turn tokens read again per session: 74 (4-74) (earlier turns that the cache did not hold; the 13633 (13633-13633) new tokens of the files are read once); first turn (nothing cached) first token: 5.22 s (5.16-6.45).

Turn (run 1) Prompt tokens Reused Read Prompt tok/s First token (s) Decode tok/s
0 609 0 609 95.2 6.45 18.7
1 3701 621 3080 163.0 18.963 28.2
2 5234 3764 1470 135.8 10.889 34.7
3 8678 5227 3451 163.5 21.18 31.8
4 11584 8724 2860 162.3 17.69 33.8
5 14464 11618 2846 160.0 17.855 22.1

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: 589 s.

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

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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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