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AtomicChat/
Qwen3-Coder-30B-A3B-GGUF

Name: Qwen3-Coder-30B-A3B-GGUF
Availability: InStock

Qwen/Qwen3-Coder-30B-A3B-Instruct

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

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

bash

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