Instructions to use ubergarm/Qwen3-30B-A3B-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 ubergarm/Qwen3-30B-A3B-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 ubergarm/Qwen3-30B-A3B-GGUF
# Run inference directly in the terminal:
llama cli -hf ubergarm/Qwen3-30B-A3B-GGUF

Install from WinGet (Windows)

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

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf ubergarm/Qwen3-30B-A3B-GGUF
# Run inference directly in the terminal:
./llama-cli -hf ubergarm/Qwen3-30B-A3B-GGUF

Build from source code

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

Use Docker

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

LM Studio
Jan

vLLM

How to use ubergarm/Qwen3-30B-A3B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ubergarm/Qwen3-30B-A3B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ubergarm/Qwen3-30B-A3B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

Ollama
How to use ubergarm/Qwen3-30B-A3B-GGUF with Ollama:
```
ollama run hf.co/ubergarm/Qwen3-30B-A3B-GGUF
```

Unsloth Studio

How to use ubergarm/Qwen3-30B-A3B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ubergarm/Qwen3-30B-A3B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ubergarm/Qwen3-30B-A3B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ubergarm/Qwen3-30B-A3B-GGUF to start chatting

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

Start the llama.cpp server

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

Configure the model in Pi

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

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

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

Start the llama.cpp server

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

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

How to use ubergarm/Qwen3-30B-A3B-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 ubergarm/Qwen3-30B-A3B-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 ubergarm/Qwen3-30B-A3B-GGUF

Run Hermes

hermes

Atomic Chat

`ik_llama.cpp` imatrix Quantizations of Qwen/Qwen3-30B-A3B

This quant collection REQUIRES ik_llama.cpp fork to support advanced non-linear SotA quants. Do not download these big files and expect them to run on mainline vanilla llama.cpp, ollama, LM Studio, KoboldCpp, etc!

NOTE ik_llama.cpp can also run your existing GGUFs from bartowski, unsloth, mradermacher, etc if you want to try it out before downloading my quants.

These quants provide best in class quality for the given memory footprint.

Big Thanks

Shout out to Wendell and the Level1Techs crew, the community Forums, YouTube Channel! BIG thanks for providing BIG hardware expertise and access to run these experiments and make these great quants available to the community!!!

Also thanks to all the folks in the quanting and inferencing community here and on r/LocalLLaMA for tips and tricks helping each other run all the fun new models!

Excited to share and learn together. Thanks!

Quant Collection

So far these are my best recipes offering the great quality in good memory footprint breakpoints.

ubergarm/Qwen3-30B-A3B-mix-IQ4_K

This quant is provides the best in class quality while providing good speed performance. This quant is designed to run with over 32k context using GPU performant f16 KV-Cache in under 24GB VRAM GPU. You could also try offload to CPU using -nkvo -ctk q8_0 -ctv q8_0 and use -rtr for RAM optimized tensor packing on startup (without mmap() support) taking ~18396MiB of VRAM or less by offloading repeating layers to CPU as well at decreased speed.

17.679 GiB (4.974 BPW)

  f32:  241 tensors
 q8_0:    6 tensors
iq4_k:   96 tensors
iq5_k:   48 tensors
iq6_k:  188 tensors

Final estimate: PPL = 9.1184 +/- 0.07278 (wiki-test.raw, compare to BF16 at 9.0703 +/- 0.07223)
*NOTE*: Benchmarks including PPL with `wiki.test.raw` and KLD with `ubergarm-kld-test-corpus.txt` are looking interesting! Will publish soon!

Quick Start

`ik_llama.cpp` API server for GPU inferencing

# This example for ~21468MiB VRAM Usage
./build/bin/llama-server
  --model ubergarm/Qwen3-30B-A3B-GGUF/Qwen3-30B-A3B-mix-IQ4_K \
  --alias ubergarm/Qwen3-30B-A3B-mix-IQ4_K \
  -fa \
  -ctk f16 -ctv f16 \
  -c 32768 \
  -fmoe \
  -ngl 99 \
  --threads 1
  --host 127.0.0.1 \
  --port 8080

If you want more context and/or less VRAM usage, you can try:

Smaller KV Cache quantization -ctk q4_0 -ctv q4_0

If you want more throughput you could try:

Increase context to max limit for your VRAM
use --parallel N to have (context / N) available per slot
use an asyncio client and keep the queue full

Quantization

👈Secret Recipe

#!/usr/bin/env bash

custom="
# Attention (give Layer 0 a little extra as it scores lowest on cosine-similarity score)
blk\.0\.attn_k.*=q8_0
blk\.0\.attn_q.*=q8_0
blk\.0\.attn_v.*=q8_0
blk\.0\.attn_output.*=q8_0

blk\..*\.attn_k.*=iq6_k
blk\..*\.attn_q.*=iq6_k
blk\..*\.attn_v.*=iq6_k
blk\..*\.attn_output.*=iq6_k

# Token Embedding (put these second so attn_output regex doesn catch too early)
token_embd\.weight=q8_0
output\.weight=q8_0

# Experts
blk\..*\.ffn_down_exps\.weight=iq5_k
blk\..*\.ffn_(gate|up)_exps\.weight=iq4_k
"

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-30B-A3B-GGUF/imatrix-Qwen3-30B-A3B.dat \
    /mnt/raid/models/Qwen/Qwen3-30B-A3B/Qwen3-30B-A3B-BF16-00001-of-00002.gguf \
    /mnt/raid/models/ubergarm/Qwen3-30B-A3B-GGUF/Qwen3-30B-A3B-mix-IQ4_K.gguf \
    IQ4_K \
    24

Discussion

TODO: Discuss some about comparing quants e.g. bartowski, unsloth, and mradermacher including "quality" and "speed".

Benchmarks

In first tests with llama-sweep-bench I'm getting over 1600 tok/sec PP and 105 tok/sec TG on my 3090TI FE 24GB VRAM. It does slow down of course as it gets deeper into the full 32k context. Check the linked Benchmarks Discussion for updates as this is all pretty fresh right now. Pretty amazing performance both in terms of generation quality and speed for a model this size!

References

Downloads last month: 38

GGUF

Model size

31B params

Architecture

qwen3moe

Hardware compatibility

Model tree for ubergarm/Qwen3-30B-A3B-GGUF

Base model

Qwen/Qwen3-30B-A3B-Base

Finetuned

Qwen/Qwen3-30B-A3B

Quantized

(132)

this model

ik_llama.cpp imatrix Quantizations of Qwen/Qwen3-30B-A3B

Big Thanks

Quant Collection

ubergarm/Qwen3-30B-A3B-mix-IQ4_K

Quick Start

ik_llama.cpp API server for GPU inferencing

Quantization

Discussion

Benchmarks

References

Model tree for ubergarm/Qwen3-30B-A3B-GGUF

`ik_llama.cpp` imatrix Quantizations of Qwen/Qwen3-30B-A3B

`ik_llama.cpp` API server for GPU inferencing