Ryzen AI Halo

https://github.com/kyuz0/amd-strix-halo-vllm-toolboxesUse this. Its easy to use and well maintained but vllm is not as performant as llama.cpp with vulkan

AMD Gorgon Halo Gorgon Halo is confirmed LPDDR5X-only. Gorgon Halo, sadly, is just Strix Halo (Zen5) rebranded. No new silicon honestly.

Yeah, AMD still does not have their shit together. They've tried to refocus the development efforts in the ecosystem into a more cohesive, stable process, titled "TheRock". https://github.com/ROCm/TheRock But look at the CI and Nightlies on any given day, and they're bound to be red. That is unaccep

Finally! Guys, give some passionate focus for AMD RYZEN AI MAX+ 395 w/ Radeon 8060S. Still it feels like too much moving parts, specially setting anything on Ubuntu 26. NPU sw support is almost non existent

[hal0] Installation on CachyOS (Arch-based) – detailed failure report I attempted to install hal0 v0.9.2 on a CachyOS system (Arch-based) and encountered several blockers. This post is to document the issues for the developers and for other Arch/CachyOS users considering this platform. Hardware: HP

I was torn between pulling the trigger on the Nvidia DGX and the Strix Halo. Both have flaws, to be honest... I think it depends on your use case. As you mentioned, the software support isn't even close to NVIDIA. On my NVIDIA desktop everything works out of the box. It has an RTX 4090 and I don't n

Ive found the atrributing slowdown in Vulkan for AMD, im pretty sure it effects Intel GPUs too, Id be keen for someone to test an experimental branch I have, The slowdown ive found is caused by Memory channel camping, not all memory channels engage causing the prefill slowdown. https://github.com/Na

Don't be surprised if it's still in the toilet... Apple is focused on a general workstation... LARPing as a serious purpose built AI computer. The truth is, IT'S NOT. If it was, it's compute would be substantially higher and it could do concurrency. More and more reports from people who shelled out

Yeah, something is weird with llama.cpp and DSV4 0731. I get a device lost error pretty regularly on some llama.cpp builds, always at the same number of tokens processed too. I've gone back to Donato's toolboxes for stability. Toolbox llama-vulkan-radv-perfromance is proving to be stable with DSV4.

I have a similar RAG setup as well via Mastra, Qwen3-Embedding/Reranker is fairly easy to get going in llama-server with https://github.com/kyuz0/amd-strix-halo-toolboxes and the Vulkan version didn't cause too much trouble though as expected it's not the fastest especially if you have to run throug