Qwen 27B LLM Achieves Impressive Local Performance on AMD Strix Halo APU
A user demonstrated impressive local LLM performance running a Qwen 27B Q8_0 model on AMD's new Ryzen AI Max+ 395 (Strix Halo) platform with 128GB unified memory. Using llama.cpp with ROCm and native Multi-Token Prediction (MTP) speculative decoding, the setup achieved speeds of 9 to 19 tokens per second with a large 142k context window. This benchmark showcases the viability of AMD's high-bandwidth unified memory APUs as a powerful, cost-effective alternative to dedicated GPUs for running large, quantized local AI models. It highlights how consumer-grade hardware with unified memory can handle massive context windows and complex agentic workflows locally. The system allocated a 64 GB VRAM and 64 GB RAM split from the 128 GB unified memory to run the model. The native MTP speculative decoding achieved an acceptance rate of 97-99%, enabling the agent to generate a complete HTML-based flight simulator in about 20 minutes.
## BACKGROUND
AMD's Strix Halo (Ryzen AI Max+) is an APU architecture featuring high-performance CPU cores and integrated graphics sharing a massive, high-bandwidth unified memory pool. Multi-Token Prediction (MTP) is a speculative decoding technique that speeds up inference by predicting multiple tokens at once without requiring a separate draft model. ROCm is AMD's open-source software platform for GPU computing, serving as their alternative to Nvidia's CUDA ecosystem.