#2211 Community benchmark on quadro-p6000-qwen3-8-flash-next-q2-0

Beschreibung

# Community benchmark on 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: Quadro P6000, 24 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

```text
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 0.90 --spec-min-p 0.70
```

## 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 | 33.6 (24.8-41.9) | 0.99 (0.83-1.17) |
| 4K prompt, fresh | 4019 (4019-4019) | 0 (0-0) | 24 cap | 3 | 304.8 (221.3-308.0) | not measured | 13.23 (13.09-18.21) |
| 16K prompt, fresh | 16058 (16058-16058) | 0 (0-0) | 24 cap | 3 | 296.2 (284.0-299.1) | not measured | 54.29 (53.77-56.61) |
| agent session, follow-up turns | 14419 (14410-14447) at the last turn | see below | up to 64 | 3 | see below | 32.6 (20.5-43.1) | 11.33 (6.34-21.76) |

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 % (68.6-68.7); earlier-turn tokens read again per session: 73 (4-73) (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: 3.80 s (3.73-5.92).

| Turn (run 1) | Prompt tokens | Reused | Read | Prompt tok/s | First token (s) | Decode tok/s |
| ---: | ---: | ---: | ---: | ---: | ---: | ---: |
| 0 | 609 | 0 | 609 | 104.1 | 5.917 | 16.3 |
| 1 | 3701 | 621 | 3080 | 142.1 | 21.757 | 26.6 |
| 2 | 5234 | 3765 | 1469 | 140.5 | 10.514 | 32.6 |
| 3 | 8678 | 5227 | 3451 | 164.9 | 20.993 | 31.1 |
| 4 | 11582 | 8722 | 2860 | 160.2 | 17.92 | 28.3 |
| 5 | 14447 | 11601 | 2846 | 188.7 | 15.169 | 20.5 |

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: 490 s.


[engine-log-excerpt.txt](https://github.com/user-attachments/files/33363935/engine-log-excerpt.txt)
[README.md](https://github.com/user-attachments/files/33363936/README.md)
[results.json](https://github.com/user-attachments/files/33363937/results.json)