Instructions to use lmstudio-community/Qwen3-Coder-Next-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use lmstudio-community/Qwen3-Coder-Next-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="lmstudio-community/Qwen3-Coder-Next-GGUF", filename="Qwen3-Coder-Next-Q4_K_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use lmstudio-community/Qwen3-Coder-Next-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf lmstudio-community/Qwen3-Coder-Next-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf lmstudio-community/Qwen3-Coder-Next-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf lmstudio-community/Qwen3-Coder-Next-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf lmstudio-community/Qwen3-Coder-Next-GGUF:Q4_K_M
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 lmstudio-community/Qwen3-Coder-Next-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf lmstudio-community/Qwen3-Coder-Next-GGUF:Q4_K_M
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 lmstudio-community/Qwen3-Coder-Next-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf lmstudio-community/Qwen3-Coder-Next-GGUF:Q4_K_M
Use Docker
docker model run hf.co/lmstudio-community/Qwen3-Coder-Next-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use lmstudio-community/Qwen3-Coder-Next-GGUF with Ollama:
ollama run hf.co/lmstudio-community/Qwen3-Coder-Next-GGUF:Q4_K_M
- Unsloth Studio
How to use lmstudio-community/Qwen3-Coder-Next-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 lmstudio-community/Qwen3-Coder-Next-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 lmstudio-community/Qwen3-Coder-Next-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for lmstudio-community/Qwen3-Coder-Next-GGUF to start chatting
- Pi
How to use lmstudio-community/Qwen3-Coder-Next-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf lmstudio-community/Qwen3-Coder-Next-GGUF:Q4_K_M
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": "lmstudio-community/Qwen3-Coder-Next-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use lmstudio-community/Qwen3-Coder-Next-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf lmstudio-community/Qwen3-Coder-Next-GGUF:Q4_K_M
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 lmstudio-community/Qwen3-Coder-Next-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use lmstudio-community/Qwen3-Coder-Next-GGUF with Docker Model Runner:
docker model run hf.co/lmstudio-community/Qwen3-Coder-Next-GGUF:Q4_K_M
- Lemonade
How to use lmstudio-community/Qwen3-Coder-Next-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull lmstudio-community/Qwen3-Coder-Next-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Qwen3-Coder-Next-GGUF-Q4_K_M
List all available models
lemonade list
Check in here for tok/s and benchmarks for local gguf models π
#2
by ykarout - opened
π Performance Benchmark: Qwen3-Coder-Next (GGUF Q4_K_M)
Model: lmstudio-community/Qwen3-Coder-Next-GGUF (Q4_K_M)
Backend: LM Studio 0.4.1 (CUDA 12)
π» Hardware Specifications
| Component | Details |
|---|---|
| GPU | NVIDIA GeForce RTX 5080 (16GB GDDR7) |
| Driver | NVIDIA 590 Linux Driver (Latest Branch) |
| CPU | Intel Core Ultra 9 285K |
| RAM | 64GB DDR5 @ 6800 MT/s |
| OS | Fedora 43 Workstation Latest Kernel and Updates |
βοΈ Inference Settings
- Context Length: 60,000 Tokens
- Layer Offloading: 35 MoE Layers to CPU (Rest on GPU)
- KV Cache: Offloaded to GPU (Q8_0 Precision)
- CPU Threads: 8 Cores
- Features: Flash Attention ON
- Max Concurrency: 10
π Results
Testing performed with medium-sized coding prompts.
- Single Request:
40 - 45 tok/s - Concurrent (10 Requests):
- Per Request:
9 - 10 tok/s - Total Throughput:
~70 tok/s