> llama.cpp via Vulkan (AMD / Intel / NVIDIA) or CPU fallback
I got excited about someone paying attention to intel. Oh well.
> llama.cpp via Vulkan (AMD / Intel / NVIDIA) or CPU fallback
I got excited about someone paying attention to intel. Oh well.
llama.cpp sycl and vllm xmx work is pretty incredible right now - you just gotta build it with some extra flags
Llama would be nice for the ggufs. Any specific flags or tutorials I should look at?
The docs are a great start.
https://github.com/ggml-org/llama.cpp/blob/master/docs/backe...
What hardware do you have? I’ve been playing with a 258V and OpenVINO has come a longggggg way.
Two arc b60s. The intel vllm build is getting me ~15t/s decode with heavy context using qwen3.8 27b.
Those cards should be able to do a lot more. They support int8 and they have very good processing speed. For reference I have a single V100 running around 900t/s prefill and 100 t/s decode during DeepSWE runs on 27B (4 bit quant). Those cards have half the bandwidth so a realistic decode is going to stop around 50 t/s, but those cards can support NVFP4 more easily than the V100. Combined with a TP build you should be able to get 100 t/s. And you should be able to 2x the prefill of a V100. I have had Claude optimizing llama.cpp for about a week and am at around 300 t/s decode so far on two ancient V100s @ 64 GB RAM.
Also for the 3 people that ever read this and are curious about local models still, Qwen 27B 3.8 matched Sonnet 5 in the 17 DeepSWE tasks I have run so far, solving the exact same 7 it has. Caveat: datacurve combined low/medium/high Sonnet 5 data.
Very interesting! Will you be sharing the optimizations ?
I will make sure they get out there soon :)