Autor: Jakub Rusinowski · Ostatnia aktualizacja: 20 listopada 2024
Najmniejsze produkcyjne LLM-y. Modele SmolLM2 HuggingFace są zaprojektowane do działania na mikrokontrolerach, telefonach i przeglądarkach przez WebAssembly. Mimo małych rozmiarów wykazują zaskakującą inteligencję dzięki starannemu doborowi danych.
| Licence | What it permits | Applies to |
|---|---|---|
Apache-2.0 | Commercial use permitted Commercial use permitted. No usage restrictions beyond attribution. | SmolLM2 1.7B Instruct, SmolLM2 360M Instruct |
| SmolLM2 1.7B Instruct | Min 2 GB VRAM · Q4_K_M · 8,192 ctx · ollama run smollm2:1.7b |
| SmolLM2 360M Instruct | Min 1 GB VRAM · Q4_K_M · 8,192 ctx · ollama run smollm2:360m |
The cheapest GPU that runs SmolLM2 locally (min 1 GB VRAM) is the Intel Arc B570 (10 GB).
Install Ollama then run: ollama run smollm2:1.7b
Minimum VRAM: 1 GB. For best results use Q4_K_M quantization.
SmolLM2 needs about 1 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: SmolLM2 1.7B Instruct (2 GB, Q4_K_M); SmolLM2 360M Instruct (1 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.
Yes — SmolLM2 runs on an RTX 4090 (24 GB) and other 24 GB cards such as the RTX 3090. Smaller variants also fit comfortably on 8–16 GB GPUs at Q4_K_M.
Q4_K_M is the best balance of quality and VRAM for SmolLM2 in most cases. Choose Q8_0 for near-lossless quality if you have spare VRAM, or smaller quants (Q3/Q2) only when memory is tight.
Install Ollama, then run: ollama run smollm2:1.7b. This downloads SmolLM2 and starts a local, OpenAI-compatible endpoint — no internet connection is needed after the initial download.