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The main thing that has stopped me from running models like this so far is VRAM. My server has a RTX 4060 with 8GB, and not sure that can reasonably run a model like this.
Edit:
This calculator seems pretty useful: https://apxml.com/tools/vram-calculator
According to this, I can run Qwen3 14B with 4B quant and 15-20% CPU/NVMe offloading and get 41 tokens / s. It seems 4B quant reduces accuracy by 5-15%.
The calculator even says I can run the flagship model with 100% NVMe offloading and get 4 tokens / s.
I didn’t realize NVMe offloading was even a thing and not sure if it actually is supported or works well in practice. If so, it’s a game changer.
Edit:
The llama.cpp docs do mention that models are memory mapped by default and loaded into memory as needed. Not sure if that means that a MoE model like qwen3 235b can run with 8GB of VRAM and 16GB of RAM, albeit at a speed that is an order of magnitude slower like the calculator suggests is possible.