model roundup

Qwen 3.5

3 items · started 2026-06-11 · closed 2026-06-14

  1. Been testing DiffusionGemma 26B A4B for the last few days and the bottleneck profile is completely different from autoregressive models. With autoregressive models you are compute-bound during prefill and memory-bandwidth-bound during deco…

  2. I have tried preventing this issue by using llama.cpp flags. However, I still have the issue: whenever I'm close to my 96GB of RAM, llama-server / llama.cpp decides to offload the KV cache onto my swap.

  3. Lets clarify all things related to NVFP4 in this thread. Sharing few questions & links here.

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