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

---

license: apache-2.0

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

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

base_model:

• Qwen/Qwen3-Coder-30B-A3B-Instruct

base_model_relation: quantized

quantized_by: AtomicChat

pipeline_tag: text-generation

library_name: gguf

tags:

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

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<img src="https://huggingface.co/AtomicChat/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, so this is not a repack of somebody else's files. Runs fully offline.

Highlights

• 30.5B parameters: the weights this repo quantizes.
• Context length: 262,144 tokens (256K), as published by Qwen.
• 48 layers: Mixture-of-Experts.
• Full imatrix ladder: every quant is calibrated with an importance matrix.
• Significant Performance: among open models on Agentic Coding, Agentic Browser-Use, and other foundational coding tasks.
• Agentic Coding: supporting for most platform such as Qwen Code, CLINE, featuring a specially designed function call format.

> [!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 |

| Parameters | 30.5B |

| Layers | 48 |

| Experts | 128 routed (top-8) |

| Context length | 262,144 tokens (256K) |

| Vocabulary | 151,936 |

| Modalities | Text |

| Architecture | Mixture-of-Experts, 128 experts (top-8), 32 attention heads over 4 KV heads, Qwen3MoeForCausalLM |

| This repo | GGUF quants (imatrix). Quants: Q2_K, Q5_K_M, IQ3_M, Q3_K_M, Q3_K_L, IQ4_XS, Q6_K, Q4_K_S, Q4_K_M, UD-Q4_K_XL, Q5_K_S, Q8_0 |

Choosing a quant

| Quant | Size | Notes |

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

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

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

| IQ3_M | 13.5 GB | Beats Q3 at a 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. |

| Q6_K | 17.4 GB | Near lossless, noticeably lighter than Q8_0. |

| Q4_K_S | 17.5 GB | Compact 4-bit, 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, slightly more compact than Q5_K_M. |

| 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 AtomicChat/qwen3-coder-30b-a3b-GGUF, pick a quant, hit Use this model.
• llama.cpp: llama-server -hf AtomicChat/qwen3-coder-30b-a3b-GGUF:Q4_K_M --jinja -c 8192
• Ollama: ollama run hf.co/AtomicChat/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 sampling configuration for Qwen/Qwen3-Coder-30B-A3B-Instruct.

Run in llama.cpp

git clone https://github.com/ggml-org/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 AtomicChat/qwen3-coder-30b-a3b-GGUF:Q4_K_M \
    --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 our calibration corpus.
4. Quantize the ladder with --imatrix.
5. UD-Q4_K_XL additionally pins the token-embedding and output tensors to Q8_0.

License

Original model by Qwen, released under the Apache 2.0 license. Full terms: Apache 2.0. Quantized by Atomic Chat.