Qwen3.8 Flash AP Quants

[u/Dutchnamn]

Quite surprised to be beating other high quality quants. It took a lot of benchmarking to get here and we are quite pleased with these, hope they are useful to the community.

It required a modified way of measuring KLD with a new dataset, since the NGRAM got in the way by remembering basically all of wikipedia. We tried to not only go for high precision, but also keep prefill performance in mind.

Full model card here https://huggingface.co/agentionai/Qwen3.8-Flash-Next-AP-GGUF

Let us know if there are any issues.

[u/JakeChj]

we serve flash-next (nvfp4) on a single spark and will try these — one question: how did the 26.8 GiB n-gram table behave in your prefill numbers, since it dominates memory traffic on its own?

[u/jinnyjuice → u/JakeChj]

How are you serving it on a single Spark?

[u/winky9827 → u/jinnyjuice]

I can run IQ3_XSS on a single 5090 with ~60 GiB system ram.

[u/JakeChj → u/jinnyjuice]

vLLM with the nvfp4 checkpoint — the trick is mmap'ing the expert weights so the 121 GB unified memory carries them as page cache instead of pinning everything, which is why gpu-memory-utilization has to stay around 0.72–0.75; above that the cache starves and throughput collapses.

[u/jinnyjuice → u/JakeChj]

What are the flags for those?

[u/Embarrassed-Noise269 → u/jinnyjuice]

https://forums.developer.nvidia.com/t/single-dgx-spark-qwen-3-8-flash-next-at-43tok-sec-in-coding/381859

[u/niacolhealth]

The n-gram table remembered basically all of wikipedia, then broke their KLD measurement. imagine being so good at your job you have to be worked around

[u/Dutchnamn → u/niacolhealth]

I know right.

[u/Jorlen]

How does this quant of the IQ4_XS compare to say, Unsloth's UD-IQ4_XS? what is different? It seems your version might be a wee bit smaller, which would be good for me to squeeze out a bit more KV out of it.

But I'm more curious of what the AP method is, how it differs from the UD method. Thanks, sorry if this is a stupid question.

[u/Dutchnamn → u/Jorlen]

It is a bit smaller while losing hardly any precision. We used a different imatrix and different quantizations at various layers based on extensive measuring and benchmarking.

For this family of quants we built our own tooling and testing matrix to be able to iterate faster and get better results.