MI50 power curve tests

tests done power limiting the GPU on LACT - real power usage varies wildy

at 20W it ranges from 25W to 56W
same behavior happens on every setting

prompt for the test runs:

https://github.com/lukesdevlab/youtube/blob/main/prompts/agent-maze.txt

analysis by mimo 2.5

Key Findings:
• Generation speed is remarkably resilient to power throttling — 100W delivers 97.5% of 190W gen speed (31.98 vs 32.79 t/s), since decode is memory-bandwidth bound, not compute bound.
• At 50W you get 70% of peak gen speed at only 26% of peak power — 3.6× better energy efficiency (0.458 vs 0.173 t/s/W).
• At 20W the card is 6.0× more energy efficient than 190W, though prompt processing drops to 53% of peak.
• Graph reuse correlates inversely with power — 190W reuses 44,790 graphs vs 11,669 at 100W, but 20W reuses 38,248. Lower power limits cause more partial graph reuse as the scheduler compensates for throttled compute.
• Prompt processing degrades faster than gen under power limits — 190W→20W: prompt drops to 53% (691→366 t/s), gen drops to 63% (32.8→20.8 t/s). Prompt processing is more compute-bound than memory-bound.
• For inference-heavy deployments, 50W is the optimal operating point on MI50 — near-peak gen speed with dramatically lower power draw and cooling requirements.

Avarage of 3 runs:

190W config consistently processed a lot less total tokens than everyone else and didnt produce a working file in 1 out of 3 runs

TDP	Prompt Speed	Gen Speed	Total Time	Total Tokens	Gen t/s per Watt	Graphs Reused	Relative Perf
190W	691.28 t/s	32.79 t/s	212.4 s	14,892	0.173 t/s/W	44,790	100%
100W	603.08 t/s	31.98 t/s	244.9 s	21,529	0.320 t/s/W	11,669	97.5%
50W	401.14 t/s	22.92 t/s	315.1 s	20,861	0.458 t/s/W	31,967	70.0%
20W	366.05 t/s	20.80 t/s	319.9 s	20,295	1.040 t/s/W	38,248	63.4%

llama.cpp parameters:

[+] Model: qwen/Qwen3.6-35B-A3B-UD-IQ4_NL_XL.gguf
[+] Context: 262144 (256K tokens)
[+] Target KV: K=q8_0 / V=q8_0
[+] MoE placement: PARTIAL (21 MoE layers on CPU, rest on GPU)
[+] MTP: OFF (non-MTP model)
[+] Port: 8882
[+] Container: llama-gfx906-qwen35b-no-mtp
[+] Parallel: 2 slot(s)
[+] GPU layers: 99
[+] Threads: 6 / 6 (batch)
[+] Batch/Ubatch: 2048 / 1024
[+] Ctx checkpoints: 0

hardware used:

Ryzen 5 5600

2x16Gb DDR4 2667

MI50 16Gb

software:

harness used: pi.dev

Arch Linux with Kernel 7.1.4-arch1-1

docker.io/mixa3607/llama.cpp-gfx906:b10087-rocm-6.3.3