Instructions to use steampunque/Qwen3-30B-A3B-MP-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use steampunque/Qwen3-30B-A3B-MP-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf steampunque/Qwen3-30B-A3B-MP-GGUF
# Run inference directly in the terminal:
llama cli -hf steampunque/Qwen3-30B-A3B-MP-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf steampunque/Qwen3-30B-A3B-MP-GGUF
# Run inference directly in the terminal:
llama cli -hf steampunque/Qwen3-30B-A3B-MP-GGUF

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf steampunque/Qwen3-30B-A3B-MP-GGUF
# Run inference directly in the terminal:
./build/bin/llama cli -hf steampunque/Qwen3-30B-A3B-MP-GGUF

Use Docker

docker model run hf.co/steampunque/Qwen3-30B-A3B-MP-GGUF

Llama new
LM Studio
Jan
Ollama
How to use steampunque/Qwen3-30B-A3B-MP-GGUF with Ollama:
```
ollama run hf.co/steampunque/Qwen3-30B-A3B-MP-GGUF
```
Unsloth Desktop

How to use steampunque/Qwen3-30B-A3B-MP-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf steampunque/Qwen3-30B-A3B-MP-GGUF

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "steampunque/Qwen3-30B-A3B-MP-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use steampunque/Qwen3-30B-A3B-MP-GGUF with Docker Model Runner:
```
docker model run hf.co/steampunque/Qwen3-30B-A3B-MP-GGUF
```

Lemonade

How to use steampunque/Qwen3-30B-A3B-MP-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull steampunque/Qwen3-30B-A3B-MP-GGUF

Run and chat with the model

lemonade run user.Qwen3-30B-A3B-MP-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use steampunque/Qwen3-30B-A3B-MP-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf steampunque/Qwen3-30B-A3B-MP-GGUF

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default steampunque/Qwen3-30B-A3B-MP-GGUF

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use steampunque/Qwen3-30B-A3B-MP-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf steampunque/Qwen3-30B-A3B-MP-GGUF

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "steampunque/Qwen3-30B-A3B-MP-GGUF" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Mixed Precision GGUF layer quantization of Qwen3-30B-A3B by Qwen

Original model: https://huggingface.co/Qwen/Qwen3-30B-A3B

The hybrid quant employs different quantization levels on a per layer basis to increased flexibility of trading off performance vs file size. Less parameter bits are used at deep layers and more bits at cortex layers to simulultaneously optimize quantized size and model performance. All K-quants are used for faster CPU processing. For this file the layer quants are as follows: (refreshed 4/21/2026)

Q4_K_L : attn_v = q6_k attn_o = q6_k ffn_d = q6_k
Q5_K_L : attn_v = q8_0 attn_o = q6_k ffn_d = q6_k
Q6_K_S : Q6_K

   LAYER_TYPES='[
   [0 ,"Q5_K_L"],[1 ,"Q4_K_S"],[2 ,"Q3_K_L"],[3 ,"Q4_K_S"],[4 ,"Q3_K_L"],[5 ,"Q4_K_S"],[6 ,"Q3_K_L"],[7 ,"Q4_K_S"],
   [8 ,"Q3_K_L"],[9 ,"Q3_K_L"],[10,"Q3_K_L"],[11,"Q3_K_L"],[12,"Q3_K_L"],[13,"Q3_K_L"],[14,"Q3_K_L"],[15,"Q3_K_L"],
   [16,"Q4_K_S"],[17,"Q3_K_L"],[18,"Q4_K_S"],[19,"Q3_K_L"],[20,"Q4_K_S"],[21,"Q3_K_L"],[22,"Q4_K_S"],[23,"Q3_K_L"],
   [24,"Q4_K_S"],[25,"Q4_K_S"],[26,"Q4_K_S"],[27,"Q4_K_S"],[28,"Q4_K_S"],[29,"Q4_K_S"],[30,"Q4_K_S"],[31,"Q4_K_S"],
   [32,"Q4_K_M"],[33,"Q4_K_S"],[34,"Q4_K_M"],[35,"Q4_K_S"],[36,"Q4_K_M"],[37,"Q4_K_S"],[38,"Q4_K_M"],[39,"Q4_K_S"],
   [40,"Q4_K_M"],[41,"Q4_K_M"],[42,"Q4_K_M"],[43,"Q4_K_L"],[44,"Q5_K_S"],[45,"Q5_K_M"],[46,"Q5_K_L"],[47,"Q6_K_S"]
   ]'
   FLAGS="--token-embedding-type Q4_K --output-tensor-type Q6_K --layer-types-high"

The layer quants were optimized for high reasoning performance across a curated set of test prompts.

Comparison:

Quant	size	PPL	Comment
IQ4_XS	16.6e9	9.15	default embed and output
Q4_K_H	17.8e9	9.0	Q4_K embed Q6_K output

Usage:

This is a RL moe model. By default it will crank out a think block delimited by

THINK_START="<think>\n"
THINK_STOP="\n</think>\n\n"

To bypass thinking inject the think block delimiters following the assistant prompt template. The model is strong with think blocked bypassed but less accurate on harder prompts. It was found to be prone to overthinking on many of the eval prompts, but did solve most of the test prompts correctly in think mode.

The model can be efficiently run by offloading expert tensors to CPU via -ot exps=CPU to open up very large context space on even limited GPU VRAM platforms (8-12G GPUs). The smaller size of the optimally quantized parameters will give an effective boost in CPU processing speed due to reducing the memory BW needed to repeatedly copy them from main memory to SIMD regs. It can also run fully offloaded on GPU via RPC or high VRAM GPU where gen rates should be very high due to only 3B active parameters.

High context yarn config is as follows: Arbitrarily set base context for yarn rope scale compute to 35840 (vs a training context of 40960), then with a context of NCTX tokens the rope scale = NCTX / 35840. Note: this factor can be tweaked on a per application or even per prompt basis. For instance testing with other qwen3 128k yarn models showed 40000 worked best on a long context prompt, but this model appears to work well with using 34840 together with the Q4_K_H quant.

Example: on 12G VRAM, F16 KV, NCTX maxes out at 107520 tokens with -ot exps=CPU. Then rope scale = 107520.0 / 35840.0 = 3.0.

Then on model start pass -c 107520 --rope-scaling yarn --yarn-orig-ctx 35840 --rope_scale 3.0

Later versions of llama.cpp have a bug which soft caps context length to the training context, effectively disabling yarn context extension. Patch server-context.cpp according to https://github.com/ggml-org/llama.cpp/issues/22140 to fix it.

Long context tests:

These tests were done with -ot exps=CPU and greedy sampling with no speculation.

With Q8 KV, the model successfully answers the long context test prompt: https://thireus.com/REDDIT/Qwen3_Runescape_Massive_Prompt.txt

With F16KV on a 12G VRAM GPU, the model is limited to 105k tokens and succesfully answers a reduced 85k tokens version of the above prompt: https://huggingface.co/datasets/steampunque/benchlm/blob/main/Qwen3_Runescape_Massive_Prompt_85k.txt

Benchmarks:

Partial evals for the model (original, not refreshed quant) are given here: https://huggingface.co/spaces/steampunque/benchlm.

Download the file from below:

Link	Type	Size/e9 B	Notes
Qwen3-30B-A3B.Q4_K_H.gguf	Q4_K_H	17.8e9 B	~IQ4_XS size

A discussion thread about the hybrid layer quant approach can be found here on the llama.cpp git repository:

https://github.com/ggml-org/llama.cpp/discussions/13040

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GGUF

Model size

31B params

Architecture

qwen3moe

Hardware compatibility

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