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[Benchmark] 2x RTX 3060 + Xeon E5-2683 v4 + 64 GB DDR4 2400Mhz ECC

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Assessment

Difficulty
1/5
Estimated time
Under an hour
Newbie friendliness
82/100
Issue type
Documentation
Clarity
Clearly specified
Activity status
Active
Tech stack
cpp, linux
Domain
machine-learning, performance

Research direction

This issue contributes a community benchmark report using tools/strata_bench.py; start by reviewing the benchmark format and the attached results.json and engine-log-excerpt.txt. Check that the reported hardware, configuration, method, results, and limitations are included. Done means the benchmark data is available in the issue for readers to compare.

Written by the indexing model from the issue text.

Description

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

Measured on 2026-10-22 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: Intel(R) Xeon(R) CPU E5-2683 v4 @ 2.10GHz (16 cores, 32 threads); installed RAM: 61.6 GiB; storage: not measured
  • OS: Linux 7.0.0-38-generic; GPU driver: 615.71.09; CUDA: not read
  • Strata: project 0.1.47, commit 011c9e60; engine 0.1.47
  • Background workloads and power limits: not recorded

Model and configuration

  • Model and quantization: qwen3.8-flash-next-iq3_s
  • Context: 262144 tokens; parallel slots: 1
  • Settings: the install's own configuration, unchanged; thinking off for the suite; temperature 0
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-profile-save expert-profile-iq3s-learned.bin --expert-cache auto --prefill auto --spec 4 --mtp rt --max-context 262144 --kv int8 --vision --vram-reserve-mib 768 --aux-cpus auto --pcie-frac 0.20 --spec-min-p 0.70 --adapt-every 1 --adapt-swaps 160 --adapt-decay 0.97 --prefill-pipe 1

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 50.9 (46.6-68.1) 0.66 (0.59-0.71)
4K prompt, fresh 4019 (4019-4019) 0 (0-0) 24 cap 3 830.9 (811.2-837.5) not measured 4.87 (4.83-4.99)
16K prompt, fresh 16058 (16058-16058) 0 (0-0) 24 cap 3 1172.1 (1169.8-1173.3) not measured 13.75 (13.73-13.77)
agent session, follow-up turns 14560 (14543-14562) at the last turn see below up to 64 3 see below 57.5 (49.8-66.5) 4.56 (4.17-5.00)

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: 69.4 % (69.4-69.6); earlier-turn tokens read again per session: 73 (3-74) (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: 3.39 s (3.38-3.39).

Turn (run 1) Prompt tokens Reused Read Prompt tok/s First token (s) Decode tok/s
0 609 0 609 181.7 3.386 36.4
1 4091 623 3468 699.6 4.999 53.3
2 5624 4155 1469 345.7 4.285 56.4
3 9057 5617 3440 694.3 4.995 54.7
4 11797 9089 2708 589.3 4.64 64.4
5 14543 11826 2717 601.1 4.559 57.4

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: 182 s.

engine-log-excerpt.txt
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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