Turbo3 + gfx906 + 4 mi50 16gb running qwen3.5 122b 🤯

In a notable technical achievement shared on the r/LocalLLaMA subreddit, a developer with the handle Exact-Cupcake-2603 has successfully merged two critical software modifications to run a massive AI model on AMD hardware. The developer combined the 'Turbo3' fork, which contains performance optimizations for llama.cpp, with the 'gfx906' fork, which provides essential support for AMD's GFX9 GPU architecture. This merged codebase was then used to successfully execute the Qwen3.5 122B model—a 122-billion-parameter large language model from Alibaba's Qwen team—on a system equipped with four AMD Radeon Instinct MI50 accelerators, each with 16GB of memory.

The accomplishment is a significant milestone for the open-source AI hardware community, which often seeks alternatives to expensive, proprietary NVIDIA systems. The MI50, based on AMD's older Vega 20 architecture (codenamed gfx906), represents a more accessible hardware option, frequently available on the secondary market. Successfully running a model of this scale requires not just raw GPU power but sophisticated software to manage memory allocation and computation across multiple cards. This successful test proves the viability of using such AMD-based clusters for inference with frontier-scale open-weight models, potentially lowering the entry barrier for researchers and enthusiasts.

While the post is a brief technical update, its implications are broad. It showcases the rapid pace of community-driven development in optimizing the ubiquitous llama.cpp project for diverse hardware. This progress directly enables more developers to experiment with state-of-the-art models without reliance on cloud APIs or specific vendor ecosystems. The work on forks like gfx906 and their integration with performance enhancements like Turbo3 is crucial for building a truly open and competitive AI hardware landscape.