AlexAtomic/qwen3-coder-30b-a3b-GGUF overview

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license: apache-2.0

license_link: https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct/blob/main/LICENSE

thumbnail: https://huggingface.co/AlexAtomic/qwen3-coder-30b-a3b-GGUF/resolve/main/hero.png

base_model:

• Qwen/Qwen3-Coder-30B-A3B-Instruct

base_model_relation: quantized

quantized_by: AlexAtomic

pipeline_tag: text-generation

library_name: gguf

tags:

• atomic-chat
• qwen
• qwen3
• qwen3-coder
• gguf
• imatrix
• quantized
• llama.cpp

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<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;"/>

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<a href="https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct"><strong>Base model: Qwen/Qwen3-Coder-30B-A3B-Instruct</strong></a>

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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

| Property | Value |

|---|---|

| Base model | Qwen/Qwen3-Coder-30B-A3B-Instruct |

| Total / active parameters | 30.5B total, 3.3B activated (128 experts, 8 activated) |

| Layers | 48 |

| Context length | 262,144 native (extendable to ~1M with Yarn) |

| Architecture | Causal LM, Mixture-of-Experts; GQA (32 Q heads, 4 KV heads) |

| This repo | GGUF quants (imatrix) |

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

Choosing a quant

| Quant | Size | Notes |

|---|---|---|

| Q2_K | 11.3 GB | Smallest. Minimal RAM, clear quality drop. |

| IQ3_M | 13.5 GB | Beats Q3 at similar size thanks to imatrix. Best low-RAM pick. |

| Q3_K_M | 14.7 GB | Low quality but usable. |

| Q3_K_L | 15.9 GB | A step above Q3_K_M. |

| IQ4_XS | 16.4 GB | Excellent quality for size. Recommended low-bit. |

| Q4_K_S | 17.5 GB | Compact Q4, fast. |

| Q4_K_M | 18.6 GB | Recommended default. Best balance of size, speed and quality. |

| UD-Q4_K_XL | 18.8 GB | Dynamic. Embeddings and output kept at Q8_0 for higher quality at a Q4 footprint. |

| Q5_K_S | 19.7 GB | Higher quality. |

| Q5_K_M | 12.1 GB | Higher quality, low loss. |

| Q6_K | 17.4 GB | Near lossless. |

| Q8_0 | 20.3 GB | Effectively lossless, reference quality. |

> [!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

| Parameter | Value |

|---|---|

| temperature | 0.7 |

| top_p | 0.8 |

| top_k | 20 |

| repetition_penalty | 1.05 |

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.