Lesson 91 of 1570
Mistral Small — edge deployment
Mistral Small is the right open-weights model when you need to run on a laptop, a phone, or an on-prem CPU box.
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What this lesson covers
Learning path
The main moves in order
- 1Small enough to ship
- 2Mistral Small
- 3edge
- 4quantization
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Section 1
Small enough to ship
Mistral Small fits comfortably on a modern laptop when quantized to 4-bit. That unlocks private, offline deployments where you cannot send data to the cloud at all.
- Runs at usable speed on Apple Silicon, consumer NVIDIA, and AMD
- 4-bit GGUF quant fits in ~14GB RAM
- Strong instruction following for its size
- Open license suitable for commercial use
Compare the options
| Deployment | Mistral Small (4-bit) | Llama 4 Scout | Cloud API |
|---|---|---|---|
| RAM needed | ~14GB | ~40GB+ | N/A |
| Offline | Yes | Yes | No |
| Cost per token | Electricity | Electricity | Metered |
| Best for | Laptops, kiosks | Small servers | Anywhere |
One command and you have a local frontier-ish model.
ollama pull mistral-small
ollama run mistral-small "Draft a meeting agenda for tomorrow"Good fits
Field sales tablets, healthcare clinics with no reliable internet, factory floor terminals, and privacy-first consumer apps. Any case where 'the data must not leave the device' is a real constraint.
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