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Model

Qwen3-Coder-Next 80B A3B

Start with a supported compressed build and short context. 64GB RAM estimate. Best use: Coding agents, terminal automation, repo edits, and tool calling on 64GB plus workstations or Macs.

80B total / 3B activeCode

Best use

Coding agents, terminal automation, repo edits, and tool calling on 64GB plus workstations or Macs.

Memory planning

64GB 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?

  • Coding agents, terminal automation, repo edits, and tool calling on 64GB plus workstations or Macs.

Choose something else when

  • You have 32GB RAM, expect laptop speed, or need frictionless beginner setup immediately without tuning.

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
Use a smaller model for this memory limit.
8GB system memory
Use a smaller model for this memory limit.
16GB system memory
Use a smaller model for this memory limit.
32GB system memory
Use a smaller model for this memory limit.
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
Use a smaller model for this memory limit.
Apple unified memory
Use a smaller model for this memory limit.

Model facts that change the decision

Parameters
80B total / 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
48.5GB before runtime and context overhead
Languages
Unknown

First setup

Ollama

ollama run qwen3-coder-next

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

llama.cpp

Use the cited GGUF repository (Qwen/Qwen3-Coder-Next-GGUF) with current llama.cpp-compatible tooling.

Conditions that change the answer

  • Although only 3B parameters are active, memory requirements are driven by total model storage, quantization, runtime overhead, and context length.
  • Full 256K context may require reducing context length on local systems to avoid out-of-memory errors.
  • Best used with current SGLang, vLLM, llama.cpp, Ollama, or LM Studio builds that understand Qwen3-Coder tool formatting.
  • Non-thinking model; it is intended for coding-agent behavior, not visible reasoning traces.
  • Commercial use: Permitted under Apache-2.0 terms
  • Qwen3 Coder Next targets local coding agents: an 80B MoE with only 3B active parameters, strong tool use, and practical workstation deployment when 30B feels underpowered but 480B is impossible for most local builders today.

Read the controlling license text →