Unsloth Provides GGUFs for Qwen3-Coder-Next

    
        By vramkickedin    
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            March 15, 2026 at 3:08 pm        
    
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        2 min read

Qwen3-Coder-Next is an open-weight language model built specifically for coding agents and local development workflows. The model uses a mixture-of-experts (MoE) architecture with 80 billion total parameters, but cleverly only activates 3 billion during inference, making it run much faster than its full size suggests.

Developed by the Qwen Team with optimized GGUF quantizations available from Unsloth, this release aims to bring high-performance coding assistance to users running local hardware. It handles complex tasks like long reasoning chains and tool usage while fitting on consumer equipment.

Model Size: 18.9GB & VRAM GPU: requirements vary

What it features and coding capabilities

Activates only 3B of its 80B total parameters during inference.
Supports context lengths up to 256K tokens.
Excels at tool calling and recovering from execution failures.
Integrates with popular development tools like Claude Code, Cline, and LMStudio.
Unsloth Dynamic 2.0 quantization provides improved accuracy over other compression methods.

Developers and coder working on complex projects with limited hardware can run this model locally without any cloud services. The architecture allows it to punch above its weight class, delivering performance comparable to much larger models while keeping resource demands manageable.

Practical setup notes from Unsloth

Unsloth recommends having at least 45GB of unified memory to run 4-bit quantized versions smoothly. For best results with their 2-bit XL quant, users need a minimum of 30GB combined RAM and VRAM.

The team recently fixed a bug in llama.cpp that previously caused the model to loop and produce poor outputs, so users should update both the model files and their inference software. Recent updates also improved tool-calling accuracy after parser fixes were merged.