Instructions to use ubergarm/Qwen3-Coder-30B-A3B-Instruct-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-Coder-30B-A3B-Instruct-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-Coder-30B-A3B-Instruct-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q2_K

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-Coder-30B-A3B-Instruct-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q2_K

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-Coder-30B-A3B-Instruct-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q2_K

Use Docker

docker model run hf.co/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q2_K

LM Studio
Jan

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ubergarm/Qwen3-Coder-30B-A3B-Instruct-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-Coder-30B-A3B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q2_K

Ollama
How to use ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF with Ollama:
```
ollama run hf.co/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q2_K
```

Unsloth Studio

How to use ubergarm/Qwen3-Coder-30B-A3B-Instruct-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-Coder-30B-A3B-Instruct-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-Coder-30B-A3B-Instruct-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-Coder-30B-A3B-Instruct-GGUF to start chatting

How to use ubergarm/Qwen3-Coder-30B-A3B-Instruct-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-Coder-30B-A3B-Instruct-GGUF:Q2_K

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-Coder-30B-A3B-Instruct-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ubergarm/Qwen3-Coder-30B-A3B-Instruct-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-Coder-30B-A3B-Instruct-GGUF:Q2_K

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-Coder-30B-A3B-Instruct-GGUF:Q2_K

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use ubergarm/Qwen3-Coder-30B-A3B-Instruct-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-Coder-30B-A3B-Instruct-GGUF:Q2_K

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-Coder-30B-A3B-Instruct-GGUF:Q2_K" \
  --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-Coder-30B-A3B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q2_K
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q2_K

Run and chat with the model

lemonade run user.Qwen3-Coder-30B-A3B-Instruct-GGUF-Q2_K

List all available models

lemonade list

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

This quant collection REQUIRES ik_llama.cpp fork to support the ik's latest SOTA quants and optimizations! 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.

Some of ik's new quants are supported with Nexesenex/croco.cpp fork of KoboldCPP.

These quants provide best in class perplexity 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 on BeaverAI Club Discord and on r/LocalLLaMA for tips and tricks helping each other run, test, and benchmark all the fun new models!

Quant Collection

Perplexity computed against wiki.test.raw.

These first three are just test quants for baseline perplexity comparison:

bf16 56.894 GiB (16.007 BPW)
- Final estimate: PPL = 9.5334 +/- 0.07560
Q8_0 30.247 GiB (8.510 BPW)
- Final estimate: PPL = 9.5317 +/- 0.07551 (NOTE lower than BF16 but didn't use it for "baseline"...)
Q4_0 16.111 GiB (4.533 BPW)
- Final estimate: PPL = 9.7225 +/- 0.07712

`IQ5_K` 21.324 GiB (5.999 BPW)

Final estimate: PPL = 9.5930 +/- 0.07614

👈 Secret Recipe

#!/usr/bin/env bash

custom="
# 48 Repeating Layers [0-47]

# Attention
blk\.(0)\.attn_q.*=q8_0
blk\.(0)\.attn_k.*=q8_0
blk\.(0)\.attn_v.*=q8_0
blk\.(0)\.attn_output.*=q8_0

blk\..*\.attn_q.*=iq5_k
blk\..*\.attn_k.*=iq6_k
blk\..*\.attn_v.*=iq6_k
blk\..*\.attn_output.*=iq5_k

# Routed Experts
blk\.(0|47)\.ffn_down_exps\.weight=q8_0
blk\.(0|47)\.ffn_(gate|up)_exps\.weight=q8_0

blk\..*\.ffn_down_exps\.weight=iq6_k
blk\..*\.ffn_(gate|up)_exps\.weight=iq5_k

# Non-Repeating Layers
token_embd\.weight=iq6_k
output\.weight=iq6_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-Coder-30B-A3B-Instruct-GGUF/imatrix-Qwen3-Coder-30B-A3B-Instruct-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-BF16-00001-of-00002.gguf \
    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-IQ5_K.gguf \
    IQ5_K \
    192

`IQ4_K` 17.878 GiB (5.030 BPW)

Final estimate: PPL = 9.6023 +/- 0.07613

👈 Secret Recipe

#!/usr/bin/env bash

custom="
# 48 Repeating Layers [0-47]

# Attention
blk\.(0)\.attn_q.*=q8_0
blk\.(0)\.attn_k.*=q8_0
blk\.(0)\.attn_v.*=q8_0
blk\.(0)\.attn_output.*=q8_0

blk\..*\.attn_q.*=iq5_k
blk\..*\.attn_k.*=iq6_k
blk\..*\.attn_v.*=iq6_k
blk\..*\.attn_output.*=iq5_k

# Routed Experts
blk\.(0|47)\.ffn_down_exps\.weight=q8_0
blk\.(0|47)\.ffn_(gate|up)_exps\.weight=q8_0

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

# Non-Repeating Layers
token_embd\.weight=iq4_k
output\.weight=iq6_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-Coder-30B-A3B-Instruct-GGUF/imatrix-Qwen3-Coder-30B-A3B-Instruct-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-BF16-00001-of-00002.gguf \
    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-IQ4_K.gguf \
    IQ4_K \
    192

`IQ4_KSS` 15.531 GiB (4.370 BPW)

Final estimate: PPL = 9.6441 +/- 0.07648

👈 Secret Recipe

#!/usr/bin/env bash

custom="
# 48 Repeating Layers [0-47]

# Attention
blk\.(0)\.attn_q.*=q8_0
blk\.(0)\.attn_k.*=q8_0
blk\.(0)\.attn_v.*=q8_0
blk\.(0)\.attn_output.*=q8_0

blk\..*\.attn_q.*=iq5_k
blk\..*\.attn_k.*=iq6_k
blk\..*\.attn_v.*=iq6_k
blk\..*\.attn_output.*=iq5_k

# Routed Experts
blk\.(0|47)\.ffn_down_exps\.weight=q8_0
blk\.(0|47)\.ffn_(gate|up)_exps\.weight=q8_0

blk\..*\.ffn_down_exps\.weight=iq4_ks
blk\..*\.ffn_(gate|up)_exps\.weight=iq4_kss

# Non-Repeating Layers
token_embd\.weight=iq4_k
output\.weight=iq6_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-Coder-30B-A3B-Instruct-GGUF/imatrix-Qwen3-Coder-30B-A3B-Instruct-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-BF16-00001-of-00002.gguf \
    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-IQ4_KSS.gguf \
    IQ4_KSS \
    192

`IQ3_K` 14.509 GiB (4.082 BPW)

Final estimate: PPL = 9.6849 +/- 0.0768

👈 Secret Recipe

#!/usr/bin/env bash

custom="
# 48 Repeating Layers [0-47]

# Attention
blk\.(0)\.attn_q.*=q8_0
blk\.(0)\.attn_k.*=q8_0
blk\.(0)\.attn_v.*=q8_0
blk\.(0)\.attn_output.*=q8_0

blk\..*\.attn_q.*=iq5_k
blk\..*\.attn_k.*=iq6_k
blk\..*\.attn_v.*=iq6_k
blk\..*\.attn_output.*=iq5_k

# Routed Experts
blk\.(0|47)\.ffn_down_exps\.weight=q8_0
blk\.(0|47)\.ffn_(gate|up)_exps\.weight=q8_0

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

# Non-Repeating Layers
token_embd\.weight=iq4_k
output\.weight=iq6_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-Coder-30B-A3B-Instruct-GGUF/imatrix-Qwen3-Coder-30B-A3B-Instruct-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-BF16-00001-of-00002.gguf \
    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-PR735-IQ3_K.gguf \
    IQ3_K \
    192

`IQ3_KS` 13.633 GiB (3.836 BPW)

Final estimate: PPL = 9.7940 +/- 0.07795

👈 Secret Recipe

#!/usr/bin/env bash

custom="
# 48 Repeating Layers [0-47]

# Attention
blk\.(0)\.attn_q.*=q8_0
blk\.(0)\.attn_k.*=q8_0
blk\.(0)\.attn_v.*=q8_0
blk\.(0)\.attn_output.*=q8_0

blk\..*\.attn_q.*=iq4_ks
blk\..*\.attn_k.*=iq5_ks
blk\..*\.attn_v.*=iq5_ks
blk\..*\.attn_output.*=iq4_ks

# Routed Experts
blk\.(0|47)\.ffn_down_exps\.weight=q8_0
blk\.(0|47)\.ffn_(gate|up)_exps\.weight=q8_0

blk\..*\.ffn_down_exps\.weight=iq4_ks
blk\..*\.ffn_(gate|up)_exps\.weight=iq3_ks

# Non-Repeating Layers
token_embd\.weight=iq4_k
output\.weight=iq6_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-Coder-30B-A3B-Instruct-GGUF/imatrix-Qwen3-Coder-30B-A3B-Instruct-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-BF16-00001-of-00002.gguf \
    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-IQ3_KS.gguf \
    IQ3_KS \
    192

`IQ2_KL` 11.516 GiB (3.240 BPW)

Final estimate: PPL = 10.0475 +/- 0.08016

👈 Secret Recipe

#!/usr/bin/env bash

custom="
# 48 Repeating Layers [0-47]

# Attention
blk\.(0)\.attn_q.*=q8_0
blk\.(0)\.attn_k.*=q8_0
blk\.(0)\.attn_v.*=q8_0
blk\.(0)\.attn_output.*=q8_0

blk\..*\.attn_q.*=iq5_k
blk\..*\.attn_k.*=iq6_k
blk\..*\.attn_v.*=iq6_k
blk\..*\.attn_output.*=iq5_k

# Routed Experts
blk\.(0|47)\.ffn_down_exps\.weight=q8_0
blk\.(0|47)\.ffn_(gate|up)_exps\.weight=q8_0

blk\..*\.ffn_down_exps\.weight=iq3_ks
blk\..*\.ffn_(gate|up)_exps\.weight=iq2_kl

# Non-Repeating Layers
token_embd\.weight=iq4_k
output\.weight=iq6_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-Coder-30B-A3B-Instruct-GGUF/imatrix-Qwen3-Coder-30B-A3B-Instruct-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-BF16-00001-of-00002.gguf \
    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-IQ2_KL.gguf \
    IQ2_KL \
    192

`IQ2_KT` 9.469 GiB (2.664 BPW)

Final estimate: PPL = 10.1352 +/- 0.08007

👈 Secret Recipe

#!/usr/bin/env bash

custom="
# 48 Repeating Layers [0-47]
blk\.(0)\.attn_q.*=iq5_ks
blk\.(0)\.attn_k.*=iq6_k
blk\.(0)\.attn_v.*=iq6_k
blk\.(0)\.attn_output.*=iq5_ks

# Attention
blk\..*\.attn_q.*=iq4_kt
blk\..*\.attn_k.*=iq5_ks
blk\..*\.attn_v.*=iq5_ks
blk\..*\.attn_output.*=iq4_kt

# Routed Experts
blk\.(0|47)\.ffn_down_exps\.weight=iq4_kt
blk\.(0|47)\.ffn_(gate|up)_exps\.weight=iq4_kt

blk\..*\.ffn_down_exps\.weight=iq3_kt
blk\..*\.ffn_(gate|up)_exps\.weight=iq2_kt

# Non-Repeating Layers
token_embd\.weight=iq4_kt
output\.weight=iq6_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-Coder-30B-A3B-Instruct-GGUF/imatrix-Qwen3-Coder-30B-A3B-Instruct-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-BF16-00001-of-00002.gguf \
    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-IQ2_KT.gguf \
    IQ2_KT \
    192

`IQ1_KT` 7.583 GiB (2.133 BPW)

Final estimate: PPL = 11.0592 +/- 0.08760

👈 Secret Recipe

#!/usr/bin/env bash

custom="
# 48 Repeating Layers [0-47]
blk\.(0)\.attn_q.*=iq5_ks
blk\.(0)\.attn_k.*=iq6_k
blk\.(0)\.attn_v.*=iq6_k
blk\.(0)\.attn_output.*=iq5_ks

# Attention
blk\..*\.attn_q.*=iq4_kt
blk\..*\.attn_k.*=iq5_ks
blk\..*\.attn_v.*=iq5_ks
blk\..*\.attn_output.*=iq4_kt

# Routed Experts
blk\.(0|47)\.ffn_down_exps\.weight=iq4_kt
blk\.(0|47)\.ffn_(gate|up)_exps\.weight=iq4_kt

blk\..*\.ffn_down_exps\.weight=iq2_kt
blk\..*\.ffn_(gate|up)_exps\.weight=iq1_kt

# Non-Repeating Layers
token_embd\.weight=iq4_kt
output\.weight=iq6_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-Coder-30B-A3B-Instruct-GGUF/imatrix-Qwen3-Coder-30B-A3B-Instruct-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-BF16-00001-of-00002.gguf \
    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-IQ1_KT.gguf \
    IQ1_KT \
    192

Quick Start

Full GPU Offload with CUDA

# Compile CUDA backend
cmake -B ./build -DCMAKE_BUILD_TYPE=Release -DGGML_CUDA=ON -DGGML_SCHED_MAX_COPIES=1 -DGGML_CUDA_F16=ON
cmake --build ./build --config Release -j $(nproc)

# Run Server
./build/bin/llama-server \
    --model Qwen3-Coder-30B-A3B-Instruct-IQ3_KS.gguf \
    --alias ubergarm/Qwen3-Coder-30B-A3B-Instruct \
    --ctx-size 32768 \
    -ctk q8_0 -ctv q8_0 \
    -fa -fmoe \
    -ngl 99 \
    --parallel 1 \
    --threads 1 \
    --host 127.0.0.1 \
    --port 8080

CPU-only Backend

# Compile
cmake -B build -DCMAKE_BUILD_TYPE=Release -DGGML_CUDA=0 -DGGML_VULKAN=0
cmake --build build --config Release -j $(nproc)

# Run Server
./build/bin/llama-server \
    --model Qwen3-Coder-30B-A3B-Instruct-IQ3_KS.gguf \
    --alias ubergarm/Qwen3-Coder-30B-A3B-Instruct \
    --ctx-size 32768 \
    -ctk q8_0 -ctv q8_0 \
    -fa -fmoe \
    -ub 4096 -b 4096 \
    --parallel 1 \
    --threads 8 \
    --host 127.0.0.1 \
    --port 8080 \
    --no-mmap

References

Downloads last month: 194

GGUF

Model size

31B params

Architecture

qwen3moe

Hardware compatibility

2-bit

View +1 variant

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

Base model

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

Quantized

(155)

this model

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

Big Thanks

Quant Collection

IQ5_K 21.324 GiB (5.999 BPW)

IQ4_K 17.878 GiB (5.030 BPW)

IQ4_KSS 15.531 GiB (4.370 BPW)

IQ3_K 14.509 GiB (4.082 BPW)

IQ3_KS 13.633 GiB (3.836 BPW)

IQ2_KL 11.516 GiB (3.240 BPW)

IQ2_KT 9.469 GiB (2.664 BPW)

IQ1_KT 7.583 GiB (2.133 BPW)

Quick Start

Full GPU Offload with CUDA

CPU-only Backend

References

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

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

`IQ5_K` 21.324 GiB (5.999 BPW)

`IQ4_K` 17.878 GiB (5.030 BPW)

`IQ4_KSS` 15.531 GiB (4.370 BPW)

`IQ3_K` 14.509 GiB (4.082 BPW)

`IQ3_KS` 13.633 GiB (3.836 BPW)

`IQ2_KL` 11.516 GiB (3.240 BPW)

`IQ2_KT` 9.469 GiB (2.664 BPW)

`IQ1_KT` 7.583 GiB (2.133 BPW)