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Local LLM Guide

Model

Qwen3-Coder 30B A3B Instruct

Start with a supported compressed build and short context. 32GB RAM estimate. Best use: Developers wanting local repo help, code completion, debugging, and lightweight agent workflows on serious laptops.

30.5B total / 3.3B activeCode

Best use

Developers wanting local repo help, code completion, debugging, and lightweight agent workflows on serious laptops.

Memory planning

32GB RAM estimate

Expected pace

Depends on your hardware, app, and conversation length

License

Apache-2.0; read the model’s terms before use

Who should use this model?

  • Developers wanting local repo help, code completion, debugging, and lightweight agent workflows on serious laptops.

Choose something else when

  • You need tiny RAM usage, fast CPU only responses, or reliable full 256K contexts locally.

What hardware makes sense?

The download is only part of the memory needed. Leave room for the app, the input it processes, and other open programs. For chat models, context includes the instructions, conversation, and answer. Longer context needs more memory.

CPU only
Memory or response time may limit use. Try a smaller model first.
8GB system memory
Use a smaller model for this memory limit.
16GB system memory
Memory or response time may limit use. Try a smaller model first.
32GB system memory
A possible starting point with a compressed model and short conversation; check the exact file size.
8GB GPU memory
Use a smaller model for this memory limit.
12GB GPU memory
Use a smaller model for this memory limit.
24GB GPU memory
Memory or response time may limit use. Try a smaller model first.
Apple unified memory
Memory or response time may limit use. Try a smaller model first.

Model facts that change the decision

Parameters
30.5B total / 3.3B active
Input
Text · Code
Published context limit
262,144 tokens
Model format
safetensors · GGUF · Ollama · MLX
Suggested compression
Q4_K_M for first local attempts unless a cited runtime recommends a different default.
Typical Q4 file
18.6GB before runtime and context overhead
Languages
Unknown

First setup

Ollama

ollama run qwen3-coder:30b

LM Studio: Check that your installed app supports the exact model format and task before downloading it.

llama.cpp

Use the cited GGUF repository (unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF) with current llama.cpp-compatible tooling.

Conditions that change the answer

  • Full 256K context can require far more memory than the model file alone.
  • Function calling depends on current runtime support for the qwen3_coder tool parser or compatible chat templates.
  • Use updated llama.cpp, Ollama, LM Studio, vLLM, or SGLang builds if tool calling behaves incorrectly.
  • Non-thinking model; users should not expect visible chain-of-thought style output.
  • Commercial use: Permitted under Apache-2.0 terms
  • Qwen3 Coder 30B is the practical local coding choice: it fits high memory laptops, understands large repositories, supports completion and tool workflows, and is much easier to run than 80B or 480B alternatives locally today.

Read the controlling license text →