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[Benchmark] Community benchmark on AMD Radeon RX 6800 XTX + 48gb of RAM for 256k tokens of context

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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, linux, python
Domain
machine-learning, performance

Research direction

Read the benchmark artifacts mentioned in the issue, especially results.json and engine-log-excerpt.txt, alongside tools/strata_bench.py to understand how the measurements were collected. Check that the hardware, model configuration, methodology, and limitations are recorded consistently; the benchmark report is complete when its reported measurements and caveats can be traced to those artifacts.

Written by the indexing model from the issue text.

Description

Community benchmark on AMD Radeon

Measured on 2026-10-20 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: AMD Radeon, 16 GiB VRAM
  • CPU: AMD Ryzen 7 3700X 8-Core Processor (8 cores, 16 threads); installed RAM: 46.9 GiB; storage: not measured
  • OS: Linux 7.2.9-2-cachyos; GPU driver: 7.2.9-2-cachyos; CUDA: not read
  • Strata: project 0.1.46.1, commit 9ae93f7b; engine 0.1.46.1
  • Background workloads and power limits: not recorded

Model and configuration

  • Model and quantization: swift-1.5-iq3_xxs
  • Context: 262144 tokens; parallel slots: 1
  • Settings: the install's own configuration, unchanged; thinking off for the suite; temperature 0

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 32.0 (24.9-34.8) 0.96 (0.86-1.05)
4K prompt, fresh 4019 (4019-4019) 0 (0-0) 24 cap 3 344.5 (340.5-344.7) not measured 11.71 (11.70-11.84)
16K prompt, fresh 16058 (16058-16058) 0 (0-0) 24 cap 3 430.7 (429.3-431.2) not measured 37.33 (37.28-37.45)
agent session, follow-up turns 14698 (14683-14704) at the last turn see below up to 64 3 see below 37.1 (30.3-46.9) 9.71 (7.26-10.78)

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: 69.1 % (69.1-69.4); earlier-turn tokens read again per session: 143 (4-144) (earlier turns that the cache did not hold; the 13827 (13826-13828) new tokens of the files are read once); first turn (nothing cached) first token: 5.60 s (5.55-5.62).

Turn (run 1) Prompt tokens Reused Read Prompt tok/s First token (s) Decode tok/s
0 609 0 609 110.8 5.548 22.6
1 4085 620 3465 323.9 10.761 30.6
2 5618 4078 1540 207.9 7.453 31.9
3 9051 5611 3440 320.6 10.775 33.7
4 11821 9113 2708 285.0 9.547 43.4
5 14704 11885 2819 291.2 9.724 30.3

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: 390 s.

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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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