To Run Qwen3 Coder Next locally Read our Guide! Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants. Feb 19 update : Tool calling should now be even better after llama.cpp fixes parsing. Quantization benchmarks : See third party Aider, LiveCodeBench v6, MMLU Pro, GPQA benchmarks for GGUFs here. Feb 4 update : llama.cpp fixed a bug that caused Qwen to loop and have poor outputs. We updated GGUFs please re download and update llama.cpp for improved outputs. Qwen3 Coder Next Usage Guidelines It is recommended to have 45GB unified memory or RAM/VRAM to run 4 bit quants. For best results, use any 2 bit XL quant or above (requires 30GB unified memory /combined RAM + VRAM). See how to run the model via Claude Code & OpenAI Codex. For complete detailed instructions (sampling parameters etc.), see our guide: docs.unsloth.ai/models/qwen3 coder next Qwen3 Coder Next Highlights Today, we're announcing Qwen3 Coder Next , an open weight language model designed specifically for coding agents and local development. It features the following key enhancements: Super Efficient with Significant Performance : With only 3B activated parameters (80B total parameters), it ach…
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