Final Qwen3.5 Unsloth GGUF Update with Improved Size/Quality Tradeoffs

Unsloth has released what appears to be the final GGUF quantization update for the Qwen3.5 model family, focusing on optimal size-to-quality tradeoffs. The new quantizations for both the 122B-A10B and 35B-A3B variants maintain 99.9% KL divergence, meaning minimal quality loss despite aggressive compression.

This is crucial for local LLM practitioners because it enables running state-of-the-art large language models on consumer hardware. The 122B model can now run on high-end consumer machines with reasonable VRAM requirements, while the 35B variant becomes accessible to mid-range setups. These optimized quantizations represent months of refinement work balancing inference speed, memory footprint, and output quality—the three core constraints of edge deployment.

Read the full article on r/LocalLLaMA.

Source: r/LocalLLaMA · Relevance: 10/10