Text model · SmolLM2
SmolLM2 1.7B requirements
SmolLM2 family · 1.7B params · released Nov 2024 · 2.6M Ollama pulls · LMArena Elo 1114. Minimum to run at Q4_K_M: Apple M1 (8GB).
Quantization sizes
| Quantization | Size on disk |
|---|---|
| Q2_K | 0.7 GB est |
| Q3_K_M | 0.8 GB est |
| Q4_K_M (default) | 1.06 GB |
| Q5_K_M | 1.2 GB est |
| Q6_K | 1.4 GB est |
| Q8_0 | 1.82 GB |
| FP16 | 3.4 GB est |
Lower quant = smaller and faster, slightly lower quality. Q4_K_M is the common default.
Run it
ollama run smollm2:1.7b llama-cli -hf bartowski/SmolLM2-1.7B-Instruct-GGUF:Q4_K_M lms get bartowski/SmolLM2-1.7B-Instruct-GGUF Which devices can run SmolLM2 1.7B?
Apple Silicon Macs
RAM-only laptops
iPhone & iPad
Android
NVIDIA GPUs
AMD GPUs
FAQ
How much VRAM or RAM does SmolLM2 1.7B need?
At Q4_K_M, SmolLM2 1.7B needs about 2.2 GB (weights ~1.06 GB + KV cache + overhead) at a 4k context. At Q8_0 budget ~2.9 GB.
Can SmolLM2 1.7B run on a laptop?
Yes, SmolLM2 1.7B fits on a 16 GB machine at Q4_K_M and runs on Apple Silicon or a 12 GB+ GPU comfortably.
Can I use SmolLM2 1.7B commercially?
Yes. SmolLM2 1.7B is licensed Apache-2.0, which permits commercial use.
Released by HuggingFace 2024-11-01. Q4_K_M=1.06GB, Q8_0=1.82GB from bartowski/SmolLM2-1.7B-Instruct-GGUF HF repo, cross-confirmed with HuggingFaceTB official GGUF repo. Ollama shows 1.8GB. Context 8K. Efficient edge/on-device model.
Sources
Memory figures are estimates. See methodology.