Did anyone do a full bench of e.g. Qwen Flash Next IQ4 and Qwen 27b FP8? Here are some
Mirrored from r/LocalLLaMA for archival readability. Support the source by reading on the original site.
I let Codex do some eval on Qwen 3.8 Flash Next IQ4_XS (served via vllm and r9v) and Qwen 3.8 FP8 (served via vllm and radiance). Here are the results:
| Benchmark | Flash-Next IQ4_XS | Qwen3.8 27B FP8 | Result |
|---|---|---|---|
| MMLU-Pro | 83.8% | 75.0% | Flash-Next |
| GPQA Diamond | 42.5% | 27.5% | Flash-Next |
| GSM8K | 97.5% | 90.0% | Flash-Next |
| German MGSM | 92.5% | 90.0% | Flash-Next |
| IFEval | 89.6% | 89.6% | Tie |
It took 2 h, next iteration would be >8 hours, sadly no time for that yet. FP8 27B completed the 250-case "fast" trial in 36m 27s, versus 2h 05m 47s for Flash-Next IQ4_XS.
I am running 64 GB DDR5 and 2x R9700 for reference. Recent updates in R9V were not implemented here. With 128 GB DDR5, you could use the mxfp4 quant of flash next from tcclaviger which would accelerate the test by approx 2x for QFN.
Any suggestions for more indicative benchmarks? Anyone did more? There are so many comments every week saying FP8 27b is better than low quants of QFN, but I would consider IQ4_XS already "low", however, in my daily work and this eval, there is no indication of those "comments". For single user, I would prefer even the low quant, but concurrency of FP8 27b on my system is like 4x as fast as QFN.
[link] [comments]
More from r/LocalLLaMA
-
The AI race: China's open-weight models trail US closed models by just 7 months
Oct 14
-
Anthropic Executive Says Open-Source AI Models Are Higher Risk
Oct 14
-
ComfyUI v0.40.0 released
Oct 14
-
So this is how they do it --> RTX 5090, once a consumer gaming card, is now on smugglers’ menu alongside cigarettes
Oct 14
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.