#2608 Community benchmark on NVIDIA GeForce RTX 3060 + NVIDIA GeForce RTX 3060

Descripción

# Community benchmark on NVIDIA GeForce RTX 3060 + NVIDIA GeForce RTX 3060

Measured on 2026-10-15 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 3060 + NVIDIA GeForce RTX 3060, 24 GiB VRAM
- CPU: AMD Ryzen 7 5700X 8-Core Processor (8 cores, 16 threads); installed RAM: 62.7 GiB; storage: not measured
- OS: Linux 7.0.0-38-generic; GPU driver: 595.99.02; CUDA: 13.2
- Strata: project 0.1.44.1, commit 296b59d2; engine 0.1.44.1
- Background workloads and power limits: not recorded

## Model and configuration

- Model and quantization: qwen3.8-flash-next-iq3_s
- Context: 65536 tokens; parallel slots: 1
- Settings: the install's own configuration, unchanged; thinking off for the suite; temperature 0

```text
strata --pack iq3_s --native Qwen3.8-Flash-Next-GSQ-RCO-IQ3_S-00001-of-00002.gguf --ple-gguf Qwen3.8-Flash-Next-GSQ-RCO-IQ3_S-00002-of-00002.gguf --expert-profile expert-profile.bin --expert-cache auto --prefill auto --spec 4 --mtp rt --max-context 65536 --kv int8 --pcie-frac 0.35 --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 (already running); 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 | 48.9 (46.4-61.1) | 0.73 (0.66-0.83) |
| 4K prompt, fresh | 4019 (4019-4019) | 0 (0-0) | 24 cap | 3 | 676.2 (674.8-682.5) | not measured | 5.97 (5.92-5.99) |
| 16K prompt, fresh | 16058 (16058-16058) | 0 (0-0) | 24 cap | 3 | 947.3 (944.2-949.8) | not measured | 16.99 (16.94-17.04) |
| agent session, follow-up turns | 14559 (14555-14594) at the last turn | see below | up to 64 | 3 | see below | 51.7 (33.2-58.6) | 5.10 (3.49-5.68) |

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 % (68.4-68.8); earlier-turn tokens read again per session: 73 (3-144) (earlier turns that the cache did not hold; the 13729 (13728-13729) new tokens of the files are read once); first turn (nothing cached) first token: 2.37 s (2.37-2.38).

| Turn (run 1) | Prompt tokens | Reused | Read | Prompt tok/s | First token (s) | Decode tok/s |
| ---: | ---: | ---: | ---: | ---: | ---: | ---: |
| 0 | 609 | 0 | 609 | 258.5 | 2.385 | 38.5 |
| 1 | 3743 | 623 | 3120 | 589.0 | 5.329 | 44.1 |
| 2 | 5276 | 3736 | 1540 | 434.1 | 3.579 | 51.7 |
| 3 | 8720 | 5269 | 3451 | 611.6 | 5.679 | 52.5 |
| 4 | 11632 | 8770 | 2862 | 568.3 | 5.073 | 56.9 |
| 5 | 14559 | 11660 | 2899 | 573.6 | 5.09 | 41.6 |

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: 199 s.

[results.json](https://github.com/user-attachments/files/33486059/results.json)