Who should use this model?
- Small local reasoning model for laptops and constrained machines.
- Fast experimentation with Qwen3 thinking/non-thinking behavior.
- Users who need a compact model with modern Qwen3 architecture.
Choose something else when
- You need consistently strong reasoning on difficult math or code tasks.
- You want a model that never emits thinking traces.
- You need the best quality available on 16GB+ hardware.
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
- A possible starting point with a compressed model and short conversation; check the exact file size.
- 8GB system memory
- A possible starting point with a compressed model and short conversation; check the exact file size.
- 16GB system memory
- A possible starting point with a compressed model and short conversation; check the exact file size.
- 32GB system memory
- A possible starting point with a compressed model and short conversation; check the exact file size.
- 8GB GPU memory
- A possible starting point with a compressed model and short conversation; check the exact file size.
- 12GB GPU memory
- A possible starting point with a compressed model and short conversation; check the exact file size.
- 24GB GPU memory
- A possible starting point with a compressed model and short conversation; check the exact file size.
- Apple unified memory
- Good 16GB plus
Model facts that change the decision
- Parameters
- 4B
- Input
- Text
- Published context limit
- 32,768 tokens
- Model format
- Safetensors · GGUF · Ollama
- Suggested compression
- Q4_K_M for practical local reasoning; Q5_K_M if memory allows.
- Typical Q4 file
- 2.5GB before runtime and context overhead
- Languages
- Chinese · English · French · Spanish · Portuguese · German · Italian · Russian
First setup
Ollama
ollama run qwen3:4bLM Studio: Search for `Qwen/Qwen3-4B-GGUF` in LM Studio and select Q4_K_M or Q5_K_M.
llama.cpp
Use llama-server -hf Qwen/Qwen3-4B-GGUF:Q4_K_M; use Qwen3 chat template settings and add /think or /no_think when you need to control thinking mode.
Conditions that change the answer
- RAM and VRAM figures are estimates from GGUF file size plus conservative runtime and conversation memory overhead; exact memory depends on context length, batch size, backend, and GPU offload.
- Qwen3 model cards state 32,768 native context and 131,072 tokens with YaRN. Use the native limit unless you configure the longer setting.
- Reasoning mode may emit thinking content unless disabled by the chat template or `/no_think` prompt convention.
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