Breaking the VRAM Barrier

·2026.04.22 03:07

Key point

Even with 32GB VRAM, llama.cpp's `--fit` ran Qwen3.6 Q8 at a 256k context at 57 t/s.

Details

With llama.cpp --fit enabled in a 32GB VRAM environment, even models whose weights are larger than VRAM can run at fairly high speed.

Model tested: Qwen3.6 Q8
Context: 256k
Run option: --fit
Hardware: 32GB VRAM, 5090, Oculink connection
Result: 57 t/s

The author noted that they previously assumed "if it doesn't fully fit in VRAM, you're looking at around 2 t/s," but the actual performance turned out to be far better than that. This shows that even when a model doesn't fit entirely in VRAM, using --fit can help run large models at a practically usable speed.

inference benchmark qwen quantization local-llm vram llamacpp

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