SmolLM2 — Local AI Model by HuggingFace

作者： Jakub Rusinowski · 最后更新： 2024年11月20日

最小的生产质量LLM。HuggingFace的SmolLM2模型专为微控制器、手机和通过WebAssembly的浏览器设计。尽管体积极小，通过精心的数据整理展现出令人惊讶的智能。

Licence

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

Hardware Requirements

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`

Recommended GPU

The cheapest GPU that runs SmolLM2 locally (min 1 GB VRAM) is the Intel Arc B570 (10 GB).

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Intel Arc B570 10GB

10 GB VRAM · 150 W board power

2026年价格波动较大——请以当前商品页价格为准。

在亚马逊查看价格

How to Run Locally

Install Ollama then run: ollama run smollm2:1.7b

Minimum VRAM: 1 GB. For best results use Q4_K_M quantization.

SmolLM2 — Frequently Asked Questions

How much VRAM does SmolLM2 need?

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.

Can I run SmolLM2 on an RTX 4090 (24 GB)?

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.

What quantization should I use for SmolLM2?

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.

How do I run SmolLM2 with Ollama?

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.