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Community Benchmark: RTX 4050 Laptop (6GB VRAM) + 48GB RAM

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Assessment

Difficulty
1/5
Estimated time
Under an hour
Newbie friendliness
65/100
Issue type
Documentation
Clarity
Clearly specified
Activity status
Active
Tech stack
cpp, python
Domain
machine-learning, performance

Research direction

Read the benchmark details and inspect tools/strata_bench.py to see how community results are collected and presented; the issue includes results.json and engine-log-excerpt.txt as supporting data. Confirm the reported hardware, configuration, timings, and failed request are represented accurately in the project's benchmark records. Done when this RTX 4050 Laptop result is recorded with its speed-only caveat and limitations.

Written by the indexing model from the issue text.

Description

Community benchmark on NVIDIA GeForce RTX 4050 Laptop GPU

Measured on 2026-10-19 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 4050 Laptop GPU, 6 GiB VRAM
  • CPU: 13th Gen Intel(R) Core(TM) i7-13620H (10 cores, 16 threads); installed RAM: 47.7 GiB; storage: not measured
  • OS: Windows 11 (10.0.26200); GPU driver: 610.74; CUDA: not read
  • Strata: project 0.1.45, commit not a git checkout; engine 0.1.45
  • Background workloads and power limits: not recorded

Model and configuration

  • Model and quantization: qwen3.8-flash-next-q2_0
  • Context: 32000 tokens; parallel slots: 1
  • Settings: the install's own configuration, unchanged; thinking off for the suite; temperature 0
strata.exe --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 --spec-min-p 0.5 --mtp rt --max-context 32000 --vision --vram-reserve-mib 0 --kv q4_0

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) 8 not measured 21.4 (17.4-30.0) 1.55 (1.32-24.24)
4K prompt, fresh 3939 (3939-3939) 0 (0-0) 24 cap 3 391.5 (380.7-393.0) not measured 10.15 (10.08-10.43)
16K prompt, fresh 15716 (15716-15716) 0 (0-0) 24 cap 3 392.7 (391.1-393.6) not measured 40.13 (40.02-40.27)
agent session, follow-up turns 14524 (14499-14560) at the last turn see below up to 64 3 see below 17.0 (12.6-30.3) 14.75 (7.18-17.16)

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 % (68.5-68.6); earlier-turn tokens read again per session: 73 (73-74) (earlier turns that the cache did not hold; the 13717 (13717-13720) new tokens of the files are read once); first turn (nothing cached) first token: 4.04 s (4.01-4.09).

Turn (run 1) Prompt tokens Reused Read Prompt tok/s First token (s) Decode tok/s
0 609 0 609 154.3 4.012 16.3
1 3741 621 3120 202.4 15.5 17.9
2 5274 3804 1470 207.2 7.18 15.2
3 8707 5267 3440 205.7 16.819 14.8
4 11584 8722 2862 200.9 14.331 21.2
5 14499 11600 2899 198.2 14.709 12.6
Skipped and failed
  • failed chat: {'type': 'server_error', 'message': 'the engine stopped unexpectedly (exit code 1); the next request restarts it'}

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: 531 s.

Dominant language
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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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