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-nextLM 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 →