Claim
Qwen3.6-35B-A3B Q4_K_M on Ryzen AI Max+ 395 (llama.cpp Vulkan) — decode-pace drift over three hours, 4 concurrent requests: 1.6 %
1.6%
The measurement
On a Beelink GTR9 Pro — AMD Ryzen AI Max+ 395, Radeon 8060S (gfx1151), 128 GB LPDDR5X-8000 unified running llama.cpp (server, Vulkan backend), b9049 (server_fingerprint b9049-2496f9c14) with ggml-org/Qwen3.6-35B-A3B-GGUF @ baec3ebee244 (Q4_K_M), the measured decode-pace drift over three hours, 4 concurrent requests was 1.6 % (last window vs first window, pooled over both units). The measurement was taken under the frozen endurance-30m-v1@2026-08-03 workload; evidence level lab_single_run, 2 valid runs across 2 physical units. The value is re-derived from the raw run records on every CI build.
Statement
Relative change of the median inter-token latency between the first five minutes and minutes 175-180 of a continuous 3-hour closed-loop pass, across both units. Evidence
- System
- Beelink GTR9 Pro — AMD Ryzen AI Max+ 395, Radeon 8060S (gfx1151), 128 GB LPDDR5X-8000 unified
- Runtime
- llama.cpp (server, Vulkan backend), b9049 (server_fingerprint b9049-2496f9c14)
- Model artifact
- ggml-org/Qwen3.6-35B-A3B-GGUF @ baec3ebee244 (Q4_K_M)
- Scope
- endurance-30m-v1@2026-08-03
- Aggregation
- last window vs first window, pooled over both units
- Evidence level
- lab_single_run
- Status
- active
- Published
- August 3, 2026
- Limitations
- One 3-hour pass per unit — the pattern reproduced on both commercially identical units, but repeats within a unit are pending. Closed-loop concurrency 4, reasoning disabled, this corpus — not a general thermals verdict. Ambient not instrumented; die temperatures documented in the run notes.
- Runs
Derivation
The value is re-derived from the raw run records by this query on every CI build — a published number cannot silently drift from its evidence.
Cited on
Pages on this site that render this number:
Reports
- Beelink GTR9 Pro for local LLMs: what two units measured over a month of serving
- How many tokens per second is enough for a local LLM?
- Local LLM homelab hardware, measured: what to buy for what workload
- Three hours under load: does a Strix Halo mini PC slow down over a workday?
- What 128 GB of unified memory actually runs: local LLMs, measured
Tested configurations
Measured pairs
Cite this claim
AGmind Systems Lab. Qwen3.6-35B-A3B Q4_K_M on Ryzen AI Max+ 395 (llama.cpp Vulkan): decode-pace drift over three hours, 4 concurrent requests — 1.6 % (last window vs first window, pooled over both units; 2 runs on 2 units; evidence level lab_single_run; workload endurance-30m-v1@2026-08-03). Claim strix.qwen36.endurance.c4.itl-drift-180m. https://agmind.ai/claims/strix.qwen36.endurance.c4.itl-drift-180m/ @misc{agmind_strix_qwen36_endurance_c4_itl_drift_180m,
author = {{AGmind Systems Lab}},
title = {Relative change of the median inter-token latency between the first five minutes and minutes 175-180 of a continuous 3-hour closed-loop pass, across both units.},
howpublished = {\url{https://agmind.ai/claims/strix.qwen36.endurance.c4.itl-drift-180m/}},
note = {Claim strix.qwen36.endurance.c4.itl-drift-180m: 1.6 \%; evidence level lab\_single\_run; scope endurance-30m-v1@2026-08-03},
year = {2026}
} Machine-readable: /claims/strix.qwen36.endurance.c4.itl-drift-180m.json · BibTeX · CSL-JSON · full registry · changes feed
Own comparable hardware? This claim can be reproduced: /reproduce/
Corrections to published results are logged publicly: errata
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