01What it can do
- Very lightweight: ~0.6 GB of VRAM in Q4, fits on a smartphone or modest laptop
- High local throughput (~220 tok/s in Q4)
- Step-by-step reasoning and code
- Bilingual EN/ZH, Apache 2.0 license
- —Community fine-tune: less predictable quality than an official model
- —1B parameters: limited capabilities on complex tasks
- —No Ollama tag — install via HuggingFace
04Install
Install Ollama for your OS. Check the model and its quantization before downloading. Start with 4096 tokens of context, then check placement with ollama ps. A command below is not proof that a test was run on your machine.
02Required memory
Approximate GPU VRAM required to run this model, including 4k tokens of context overhead. For a longer context, add ~1 GB per 8k-token increment.
What hardware do you need for MiniCPM5 1B Fable Thinking?
To run MiniCPM5 1B Fable Thinking locally with Q4 quantization, you need about 0.6 GB of VRAM. An option to compare: RTX 5060 Ti 16GB (ASUS Prime) — leave some headroom for the system and context; check engine compatibility with the GPU.
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On the go: MiniCPM5 1B Fable Thinking also runs on a RTX laptop PC (16 GB of VRAM) →
03Expected speed
Tokens generated per second in Q4_K_M, 4k context. Beyond 20 t/s, reading is comfortable. Below 10 t/s, that's just for testing.