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

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. 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

Download Qwen/Qwen3-Coder-30B-A3B-Instruct (original weights).
Convert to f16 GGUF with llama.cpp.
Build an importance matrix over calibration_datav3 (100 chunks).
Quantize the full ladder with --imatrix.
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.