New Quantized Inference Optimizations for AMD
huggingface · x · 2026-07-14
Hugging Face shared an update on new models and quantization schemes for AMD Strix Halo users.
Key points include:
- The 298B parameter model Hy3 uses a new 2-bit FPX codebook, aiming to map more efficiently to AMD hardware's INT8 channels.
- Compared to IQ2M, the new quantization scheme is 2.55% smaller in size.
- End-to-end latency without MTP drops by roughly 2.4% on short coding prompts, and by about 6.2% on a 19,654-token coding prompt.
- 14.05GB of reusable state can be offloaded to SSD instead of taking up scarce UMA cache memory.
- Unweighted control scores are 81 for HermesAgent-20 and 88 for the full Tool-Eval, which the author claims outperforms most Qwen models.
- Run speed is about 17–25 tok/s, with prefill at roughly 200 tok/s; unlocking these optimizations requires the latest FTX inference-engine (llama fork) update.
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