Instructions to use ubergarm/Qwen3-235B-A22B-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/Qwen3-235B-A22B-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/Qwen3-235B-A22B-GGUF
# Run inference directly in the terminal:
llama cli -hf ubergarm/Qwen3-235B-A22B-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ubergarm/Qwen3-235B-A22B-GGUF
# Run inference directly in the terminal:
llama cli -hf ubergarm/Qwen3-235B-A22B-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/Qwen3-235B-A22B-GGUF
# Run inference directly in the terminal:
./llama-cli -hf ubergarm/Qwen3-235B-A22B-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/Qwen3-235B-A22B-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ubergarm/Qwen3-235B-A22B-GGUF

Use Docker

docker model run hf.co/ubergarm/Qwen3-235B-A22B-GGUF

LM Studio
Jan

vLLM

How to use ubergarm/Qwen3-235B-A22B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ubergarm/Qwen3-235B-A22B-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/Qwen3-235B-A22B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ubergarm/Qwen3-235B-A22B-GGUF

Ollama
How to use ubergarm/Qwen3-235B-A22B-GGUF with Ollama:
```
ollama run hf.co/ubergarm/Qwen3-235B-A22B-GGUF
```

Unsloth Studio

How to use ubergarm/Qwen3-235B-A22B-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/Qwen3-235B-A22B-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/Qwen3-235B-A22B-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/Qwen3-235B-A22B-GGUF to start chatting

How to use ubergarm/Qwen3-235B-A22B-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/Qwen3-235B-A22B-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/Qwen3-235B-A22B-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use ubergarm/Qwen3-235B-A22B-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/Qwen3-235B-A22B-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/Qwen3-235B-A22B-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/Qwen3-235B-A22B-GGUF with Docker Model Runner:
```
docker model run hf.co/ubergarm/Qwen3-235B-A22B-GGUF
```

Lemonade

How to use ubergarm/Qwen3-235B-A22B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ubergarm/Qwen3-235B-A22B-GGUF

Run and chat with the model

lemonade run user.Qwen3-235B-A22B-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use ubergarm/Qwen3-235B-A22B-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/Qwen3-235B-A22B-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/Qwen3-235B-A22B-GGUF

Run Hermes

hermes

Atomic Chat

`ik_llama.cpp` imatrix Quantizations of Qwen/Qwen3-235B-A22B

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/Qwen3-235B-A22B-mix-IQ3_K.gguf

This quant is designed to run at max speed with just under ~110GiB (V)RAM combinations e.g. 24GB VRAM + 96GB RAM (perfect for AM5 or LGA 1700 gamer rigs with 2x48GiB DDR5 DIMMs for max performance). This will allow for -rtr run-time repacking for maximum CPU throughput. You can still omit -rtr and use default mmap() behavior to run in less RAM at a penalty to speed. Or you can also "offline repack" to fit your exact setup and get the best of both worlds with quicker startup with mmap() and max CPU throughput. However, you might have to --no-mmap anyway depending on how Transparent Hugepages (THPs) are configured and effect performance on your rig.

106.830 GiB (3.903 BPW)

  f32:  471 tensors
 q8_0:    2 tensors
iq3_k:  188 tensors
iq4_k:   94 tensors
iq6_k:  376 tensors

Final estimate: PPL = 5.4403 +/- 0.03421 (wiki.test.raw, compare to Q8_0 at 5.3141 +/- 0.03321) (*TODO*: more benchmarking)

Quick Start

`ik_llama.cpp` API server for hybrid GPU+CPU inferencing

# This example for 24GB VRAM + 96 GB RAM + 16 physical core CPU
# Offload first ffn layers  0-9 on GPU VRAM.
# Leave remaining ffn layers on CPU RAM.
./build/bin/llama-server \
  --model ubergarm/Qwen3-235B-A22B-GGUF/Qwen3-235B-A22B-mix-IQ3_K-00001-of-00003.gguf \
  --alias ubergarm/Qwen3-235B-A22B-mix-IQ3_K \
  -fa -fmoe \
  -ctk q8_0 -ctv q8_0 \
  -c 32768 \
  -ngl 99 \
  -ot blk\.[0-9]\.ffn.*=CUDA0 \
  -ot "blk.*\.ffn.*=CPU \
  -ngl 99 \
  --threads 16 \
  -ub 4096 -b 4096 \
  --host 127.0.0.1 \
  --port 8080

Model Architechture

The original model architechture has 94 repeating layers/blocks with the unquantized bf16 version being 448501.04 MB total:

Tensor	Dimension	Data Type	Size
token_embd.weight	[ 4096, 151936, 1, 1]	bf16	1187.00 MiB

blk.1.attn_k_norm.weight	[ 128, 1, 1, 1]	f32	0.000 MiB
blk.1.attn_q_norm.weight	[ 128, 1, 1, 1]	f32	0.000 MiB
blk.1.attn_norm.weight	[ 4096, 1, 1, 1]	f32	0.016 MiB
blk.1.ffn_gate_inp.weight	[ 4096, 128, 1, 1]	f32	2.000 MiB
blk.1.ffn_norm.weight	[ 4096, 1, 1, 1]	f32	0.016 MiB

blk.1.attn_k.weight	[ 4096, 512, 1, 1]	bf16	4.00 MiB
blk.1.attn_q.weight	[ 4096, 8192, 1, 1]	bf16	64.00 MiB
blk.1.attn_v.weight	[ 4096, 512, 1, 1]	bf16	4.00 MiB
blk.1.attn_output.weight	[ 8192, 4096, 1, 1]	bf16	64.00 MiB

blk.1.ffn_down_exps.weight	[ 1536, 4096, 128, 1]	bf16	1536.00 MiB
blk.1.ffn_gate_exps.weight	[ 4096, 1536, 128, 1]	bf16	1536.00 MiB
blk.1.ffn_up_exps.weight	[ 4096, 1536, 128, 1]	bf16	1536.00 MiB

output.weight	[ 4096, 151936, 1, 1]	bf16	1187.00 MiB
output.norm_weight	[ 4096, 1, 1, 1]	f32	0.016MiB

TODO: Compare this and other popular quants tensor choices.

Quantization

👈Secret Recipe

#!/usr/bin/env bash

custom="
# Attention
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't become q8_0)
token_embd\.weight=q8_0
output\.weight=q8_0

# Experts
blk\..*\.ffn_down_exps\.weight=iq4_k
blk\..*\.ffn_(gate|up)_exps\.weight=iq3_k
"

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

    #--token-embedding-type q8_0 \
    #--output-tensor-type q8_0 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-235B-A22B-GGUF/imatrix-Qwen3-235B-A22B.dat \
    /mnt/raid/models/Qwen/Qwen3-235B-A22B/Qwen3-235B-A22B-BF16-00001-of-00011.gguf \
    /mnt/raid/models/ubergarm/Qwen3-235B-A22B-GGUF/Qwen3-235B-A22B-mix-IQ3_K.gguf \
    IQ3_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 up to 140 tok/sec PP and 10 tok/sec TG on my 3090TI FE 24GB VRAM + AMD 9950X 2x48GB DDR5-6400 96GB RAM with OC infinity fabric. 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 for a high quality LLM on a high-end gaming rig though!

References

Downloads last month: 27

GGUF

Model size

235B params

Architecture

qwen3moe

Hardware compatibility

Model tree for ubergarm/Qwen3-235B-A22B-GGUF

Base model

Qwen/Qwen3-235B-A22B

Quantized

(55)

this model

ik_llama.cpp imatrix Quantizations of Qwen/Qwen3-235B-A22B

Big Thanks

Quant Collection

ubergarm/Qwen3-235B-A22B-mix-IQ3_K.gguf

Quick Start

ik_llama.cpp API server for hybrid GPU+CPU inferencing

Model Architechture

Quantization

Discussion

Benchmarks

References

Model tree for ubergarm/Qwen3-235B-A22B-GGUF

`ik_llama.cpp` imatrix Quantizations of Qwen/Qwen3-235B-A22B

`ik_llama.cpp` API server for hybrid GPU+CPU inferencing