(Very) High-Quality Attention Coder-Next GGUFs
New quantization method copies attention tensors bit-for-bit, preserving quality for BF16 GPU users.
An independent developer known as dinerburger has published a novel quantization approach for Alibaba's Qwen3-Coder-Next, a 3.5B parameter Mixture-of-Experts (MoE) model specialized for coding. The key innovation involves analyzing the model's architecture and selectively preserving certain layers at full precision. The developer discovered that attention tensors in these MoE models are surprisingly small (16-32MB per layer) compared to the massive 3GB expert tensors, making quantization of these critical components yield diminishing returns. By copying attention, State Space Model (SSM), and shared expert layers bit-for-bit from the original safetensors file, the method maintains the model's reasoning quality where it matters most.
This technique is specifically designed for users with modern GPUs that support the BF16 data format (like NVIDIA's Ampere architecture and newer). These users can load the high-precision attention and SSM layers onto the GPU for fast inference while offloading the quantized expert tensors to system RAM, creating an efficient quality/speed trade-off. The developer has released both IQ3_S and IQ4_XS quantized versions on Hugging Face for memory-constrained setups. This work highlights the growing sophistication of the open-source AI community in optimizing cutting-edge models like Qwen3-Coder-Next for practical, local deployment on consumer hardware.
- Selective quantization copies attention & SSM layers bit-for-bit from source, preserving critical reasoning quality.
- Targets BF16 GPU users (e.g., RTX 30/40 series) who can run high-quality layers on GPU, offloading experts to CPU.
- Released IQ3_S and IQ4_XS GGUF variants on Hugging Face, with full quantization scripts provided for transparency.
Why It Matters
Enables higher-quality local execution of advanced coding models by preserving their most sensitive components, pushing the boundary of what's possible on consumer hardware.