GitHub - kyuz0/amd-strix-halo-toolboxes
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Video Tutorial
Summary
The video shows how to install and run the large language models Qwen‑3 235B (using Q3_K_XL) and GLM 4.5‑Air‑106B (using Q6_K_XL) on AMD Ryzen AI MAX “Strix Halo” hardware. It walks through kernel configuration, unified memory tuning, and provides benchmark results, with all scripts shared via a GitHub repo.
Highlights
- Step‑by‑step guide to enable the new AMD Zen 4C‑based Strix Halo AI acceleration on Linux
- Kernel build flags and driver settings required for Q6_K_XL / Q3_K_XL inference
- Unified memory (up to 128 GB) configuration and performance tuning tips
- Benchmark comparison of Qwen‑3 235B vs GLM 4.5‑Air‑106B on the same hardware
- All scripts, container images and benchmark logs posted publicly on GitHub
auto-generated
Context
Audience
AI engineers, Linux system administrators, and hobbyists who want to run state‑of‑the‑art large language models on AMD Ryzen AI MAX hardware
DomainMachine Learning / AI Inference
Formatvideo tutorial with accompanying code repository
Accessfree online (YouTube + open‑source GitHub repo)
Topics
Related
AMD Strix Halo developer documentationHugging Face Transformers inference guideFedora container tooling for AI workloadsLLM quantization techniques (Q3_K_XL, Q6_K_XL)HP Z2 G1a Mini workstation specifications