#2411 Community benchmark: on NVIDIA Quadro P6000 / 96GB RAM DDR3 / 2CPU X5670 / Power 150W / engine 0.1.44.1

Description

# Community benchmark on Quadro P6000

Measured on 2026-10-14 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 6.8.0-147-generic; GPU driver: 580.178.04; CUDA: 13.0
- Strata: project 0.1.44.1, commit 296b59d2; engine 0.1.44.1
- Power Limit on GPU 150W from 250W
- SSD disk WDgreen for read/write
- nvcc --version: Cuda compilation tools, release 12.8, V12.8.93 Build cuda_12.8.r12.8/compiler.35583870_0

## Model and configuration

- Model and quantization: qwen3.8-flash-next-iq3_xxs
- Context: 131072 tokens; parallel slots: 1
- Settings: the install's own configuration, unchanged; thinking off for the suite; temperature 0

```text
strata --pack iq3_xxs --native Qwen3.8-Flash-Next-GSQ-RCO-IQ3_XXS-00001-of-00002.gguf --ple-gguf Qwen3.8-Flash-Next-GSQ-RCO-IQ3_XXS-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 1.00 --spec-min-p 0.70 --adapt-every 1 --adapt-swaps 160 --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 | 27.9 (23.1-30.2) | 1.16 (1.04-1.34) |
| 4K prompt, fresh | 4019 (4019-4019) | 0 (0-0) | 24 cap | 3 | 320.9 (228.8-322.4) | not measured | 12.57 (12.51-17.61) |
| 16K prompt, fresh | 16058 (16058-16058) | 0 (0-0) | 24 cap | 3 | 310.4 (299.3-313.2) | not measured | 51.81 (51.34-53.72) |
| agent session, follow-up turns | 14587 (14580-14593) at the last turn | see below | up to 64 | 3 | see below | 35.5 (23.5-45.0) | 10.97 (6.36-20.82) |

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: 68.8 % (68.8-68.8); earlier-turn tokens read again per session: 2 (2-4) (earlier turns that the cache did not hold; the 13729 (13729-13731) new tokens of the files are read once); first turn (nothing cached) first token: 4.49 s (4.13-5.95).

| Turn (run 1) | Prompt tokens | Reused | Read | Prompt tok/s | First token (s) | Decode tok/s |
| ---: | ---: | ---: | ---: | ---: | ---: | ---: |
| 0 | 609 | 0 | 609 | 103.4 | 5.953 | 18.6 |
| 1 | 3750 | 627 | 3123 | 150.5 | 20.823 | 27.1 |
| 2 | 5283 | 3814 | 1469 | 139.9 | 10.56 | 31.0 |
| 3 | 8727 | 5347 | 3380 | 179.9 | 18.857 | 31.4 |
| 4 | 11653 | 8791 | 2862 | 171.7 | 16.736 | 32.9 |
| 5 | 14593 | 11694 | 2899 | 198.5 | 14.678 | 23.5 |

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: 485 s.

[engine-log-excerpt.txt](https://github.com/user-attachments/files/33431500/engine-log-excerpt.txt)
[README.md](https://github.com/user-attachments/files/33431502/README.md)
[results.json](https://github.com/user-attachments/files/33431501/results.json)