Local Quantization and Multi-Backend Deployment with AMD Quark on Strix Halo
AMD documents a workflow to quantize a 35B-parameter Mixture-of-Experts model on Strix Halo hardware. The process reduces weight size from 70 GB to 21 GB and exports formats for llama.cpp and vLLM. This extends local deployment options for developers using AMD unified memory systems.
AMD’s Quark tool was used on a Strix Halo system to quantize the 35B‑parameter Qwen3.6‑35B‑A3B model from BF16 to a W4A16 weight‑only format. The conversion cut the model’s weight storage from roughly 70 GB to about 21 GB and then exported two artifacts: a GGUF file for llama.cpp and a safetensors package for vLLM. All steps were performed on a single laptop‑class device equipped with 128 GB of unified LPDDR5X memory. Reducing the model size enables local inference on hardware that would otherwise be unable to hold the full checkpoint, expanding deployment options for developers who rely on AMD unified memory platforms. The dual‑path export demonstrates that the same quantized checkpoint can serve both the GGUF ecosystem and the vLLM runtime, simplifying workflow integration across open‑source LLM tools. It remains unclear how the quantized model’s latency and accuracy compare to the original BF16 version under varied workloads; the authors only report that thermal and power limits of the Strix Halo laptop may affect throughput. Additionally, the long‑term stability of the exported formats on future ROCm releases has not been demonstrated.