Qwen3-Coder-30B Local AI Model: Setup & Performance
Practical guide to running Qwen3-Coder-30B locally. Setup, hardware, quantization, benchmarks and agentic coding with native 256K context.
Overview
Qwen3-Coder-30B is a local AI coding model designed for practical use. It natively supports a 256K token context window and works with agentic coding platforms like CLINE and Qwen Code. This guide covers setup, hardware, performance, and real-world use cases with steps and links to sources.
What is Qwen3-Coder-30B?
Architecture and key features
- Model size: 30.5 billion parameters with an active set of ~3.3B parameters engaged per token to reduce compute while keeping performance high.
- Long-context: Native support for 256K tokens, extendable toward 1M tokens with Yarn for repository-scale tasks.
- Agentic coding: Built for function calls and agent workflows. Compatible with common integrations and agent platforms.
- Quantization options: 4-bit, 6-bit MLX, 8-bit, and experimental 1-bit setups to trade memory for speed.
Read the model page on Hugging Face and hands-on observations in community writeups such as Simon Willison's writeup.
Hardware requirements
Memory requirements
- Quantized download sizes (approx): 4-bit ~17.2GB, 6-bit MLX ~24.82GB, 8-bit ~32.46GB. Use these as a baseline for RAM/VRAM requirements.
- Optimal performance: For 1-bit quant targeting ~6+ tokens/sec we recommend ~150GB unified memory (VRAM+RAM) or 150GB system RAM. Available memory should match or exceed the chosen model size.
GPU and Apple Silicon guidance
- High-end single GPUs like RTX 3090/4090 provide the best single-card throughput.
- Apple M-series (Pro/Max/Ultra) with 24GB+ unified memory are competitive due to high memory bandwidth. See run guides for M2/M3/M4 settings.
CPU-only setups
Community reports show usable CPU-only performance for certain quantizations. One report noted ~22 tokens/sec generation and ~160 tokens/sec prompt processing using Q8 quant on a DDR5 6000MHz system. See hardware writeups at Hardware Corner and practical tips at ArsTurn.
Installation: LM Studio + CLINE (recommended)
This path provides a friendly UI and agent integrations. It is the quickest way to run a local AI coding model.
- Open the model page on Hugging Face and click "Use model in LM Studio" or download the quant you prefer.
- Adjust LM Studio settings per the UnsloTh run guide. On high-memory Macs, increase the context window and consider KV cache quantization.
- Load the model in LM Studio. Platform notes and example configs are available in community gists such as CodingCanuck's gist.
Practical performance expectations
Performance depends on quantization, memory, and CPU/GPU bandwidth. Use these community-derived checkpoints as guidance.
- 1-bit quant (very high memory): Target ~6+ tokens/sec on systems with ~150GB unified memory.
- 6-bit MLX: Balanced option for 64GB-class machines; download ~24.82GB and leave headroom for other apps.
- CPU Q8 reports: ~22 tokens/sec generation and ~160 tokens/sec prompt processing on fast DDR5 systems.
These numbers indicate a properly quantized Qwen3-Coder-30B can be competitive with larger dense models on code tasks. For more benchmarks and examples, see this writeup.
Common use cases
- Deep code completion across large monorepos where standard contexts fail.
- Local code review automation and secure static analysis for privacy-sensitive teams.
- Agentic workflows that interact with browser automation and CI tooling (CLINE integrations).
- On-premise AI assistants for enterprises that cannot send code to cloud APIs.
Optimization and best practices
- Match memory to the quantized model size. For 6-bit MLX, allocate ~24 GB+ free memory for the model plus headroom.
- Use KV cache quantization for long sessions to reduce working memory pressure.
- Prefer chunked scans of repositories rather than always sending the full 256K context when only a subset is needed.
- Measure token throughput for your tasks. Benchmark generation and prompt processing separately.
Troubleshooting checklist
When issues occur, follow a methodical checklist. Common causes include memory misconfiguration and incorrect model files.
- Check RAM and VRAM; out-of-memory is the most frequent cause.
- Confirm you downloaded the matching quant file and that LM Studio or your runtime points to the correct path.
- If an LM Studio deploy fails, re-run with recommended settings from the UnsloTh guide and check LM Studio logs for allocation errors.
- Open an issue or share a short repro in a gist and include links to the Hugging Face model page or relevant community gists.
Where to learn more and next steps
Start with the model page on Hugging Face, then follow the run guide at UnsloTh docs.
For hands-on notes and benchmarks see Simon Willison and hardware writeups at Hardware Corner and ArsTurn. A quick path: try the 6-bit MLX quant on a 64GB machine with LM Studio, measure token rates for your repo, then iterate.
Qwen3-Coder-30B makes local, long-context code assistance practical. It is hardware-hungry but worth testing for deep repo understanding or on-prem privacy. If you hit issues, roll back, check logs, and consult the community; update runbooks as new findings appear.
Morgan specializes in keeping systems running. Great at explaining complex infrastructure concepts through real incident stories.(AI-generated persona)
