qwen3-coder-30b-a3b-GGUF

<center> <div style="display:flex; justify-content:center; align-items:center; gap:2%; max-width:560px; margin:0 auto;"> <a href="https://atomic.chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AlexAtomic/qwen3-coder-30b-a3b-GGUF/resolve/main/pill_atomic_v3.png" alt="Atomic Chat" style="width:100%; height:auto; max-width:186px;"></a> <a href="https://discord.gg/8wGSsvmg4V" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AlexAtomic/qwen3-coder-30b-a3b-GGUF/resolve/main/pill_discord_v3.png" alt="Join Discord" style="width:100%; height:auto; max-width:184px;"></a> <a href="https://github.com/AtomicBot-ai/Atomic-Chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AlexAtomic/qwen3-coder-30b-a3b-GGUF/resolve/main/pill_github_v3.png" alt="GitHub" style="width:100%; height:auto; max-width:141px;"></a> </div> <br/> <img src="https://huggingface.co/AlexAtomic/qwen3-coder-30b-a3b-GGUF/resolve/main/hero.png" alt="Qwen3 Coder 30B A3B" style="width:100%; max-width:100%; height:auto; margin-bottom:0.6em;"/> <div style="display:flex; justify-content:center; gap:0.5em;"> <a href="https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct"><strong>Base model: Qwen/Qwen3-Coder-30B-A3B-Instruct</strong></a> </div> </center>

Qwen3 Coder 30B A3B, self-quantized to GGUF by Atomic Chat. Built straight from Qwen's original weights with a per-tensor importance matrix. Runs fully offline.

Highlights

• Agentic coding specialist with significant performance among open models on agentic coding, agentic browser-use, and other foundational coding tasks.
• Efficient MoE: 30.5B total parameters, only 3.3B activated per token (128 experts, 8 activated).
• 256K native context (262,144 tokens), extendable up to ~1M tokens with Yarn, optimized for repository-scale understanding.
• Tool calling built in with a specially designed function-call format, supporting platforms such as Qwen Code and CLINE.
• Non-thinking mode only — does not emit <think></think> blocks; no enable_thinking flag required.
• Full quant ladder with an importance matrix on every quant over calibration_datav3.

[!NOTE] These GGUFs are self-quantized from the original weights, not a repack. The importance matrix keeps low-bit quants closer to the full-precision model.

[!IMPORTANT] Always pass --jinja so the Qwen3 Coder 30B A3B chat template is applied. Without it the model can emit malformed turns.

Model Overview

See the official model card for Qwen's published benchmark results.

Choosing a quant

[!TIP] Pick the largest file that fits your (V)RAM with room for context. Q4_K_M or UD-Q4_K_XL is the sweet spot for most setups; Q6_K or Q8_0 for maximum fidelity.

Get started

Run Qwen3 Coder 30B A3B locally with:

• Atomic Chat: the easiest path. Open the app, search AlexAtomic/qwen3-coder-30b-a3b-GGUF, pick a quant, hit Use this model.
• llama.cpp: llama-server -hf AlexAtomic/qwen3-coder-30b-a3b-GGUF:Q4_K_M --jinja -c 8192
• Ollama: ollama run hf.co/AlexAtomic/qwen3-coder-30b-a3b-GGUF:Q4_K_M
• LM Studio / Jan: search the repo id, download any quant.

Best practices

Qwen's recommended settings for this model (non-thinking); recommended output length 65,536 tokens.

Run in llama.cpp

git clone https://github.com/ggerganov/llama.cpp
cmake llama.cpp -B llama.cpp/build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON
cmake --build llama.cpp/build --config Release -j --target llama-cli llama-server

./llama.cpp/build/bin/llama-server \
    -hf AlexAtomic/qwen3-coder-30b-a3b-GGUF:UD-Q4_K_XL \
    --jinja -ngl 99 -c 8192 -fa on

How these were made

1. Download Qwen/Qwen3-Coder-30B-A3B-Instruct (original weights).
2. Convert to f16 GGUF with llama.cpp.
3. Build an importance matrix over calibration_datav3 (100 chunks).
4. Quantize the full ladder with --imatrix.
5. UD-Q4_K_XL additionally pins the token-embedding and output tensors to Q8_0.

License

Released by Qwen under the Apache 2.0 license. Quantized by Atomic Chat.