B70: Quick and Early Benchmarks & Backend Comparison
llama.cpp: f1f793ad0 (8657) This is a quick attempt to just get it up and running. Lots of oneapi runtime still using "stable" from Intels repo. Kernel 6.19.8+deb13-amd64 with an updated xe firmware built. Vulkan is Debian but using latest Mesa compiled from source. Openvino is 2026.0. Feels like everything is "barely on the brink of working" (which is to be expected). sycl: $ build/bin/llama-bench -hf unsloth/Qwen3.5-27B-GGUF:UD-Q4_K_XL -p 512,16384 -n 128,512 | model | size | params | backend | ngl | test | t/s | | ------------------------------ | ---------: | ---------: | ---------- | --: | --------------: | -------------------: | | qwen35 27B Q4_K - Medium | 16.40 GiB | 26.90 B | SYCL | 99 | pp512 | 798.07 ± 2.72 | | qwen35 27B Q4_K - Medium | 16.40 GiB | 26.90 B | SYCL | 99 | pp16384
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Why APEX Matters for MoE Coding Models and why it's NOT the same as K quants
I posted about my APEX quantization of QWEN Coder 80B Next yesterday and got a ton of great questions. Some people loved it, some people were skeptical, and one person asked "what exactly is the point of this when K quants already do mixed precision?" It's a great question. I've been deep in this for the last few days running APEX on my own hardware and I want to break down what I've learned because I think most people are missing the bigger picture here. So yes K quants like Q4_K_M already apply different precision to different layers. Attention gets higher precision, feed-forward gets lower. That's been in llama.cpp for a while and it works. But here's the thing nobody is talking about. MoE models have a coherence problem. I was reading this article last night and it clicked for me. When
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