text model · SmolLM2 · macOS
Can I run SmolLM2 360M on Apple M3 Pro (18GB)?
Yes. SmolLM2 360M runs on Apple M3 Pro (18GB) at Q4_K_M (~1.2 GB of ~12 GB usable).
Runs at Q4_K_M using ~1.2 GB of ~12 GB usable. You have room for FP16 for higher quality.
- Q4_K_M needed
- ~1.2 GB
- Usable on device
- ~12 GB
- Device memory
- 18 GB
- Best quant
- Q4_K_M
Run it
Pick your tool. All three load the same Q4_K_M weights.
ollama run smollm2:360m llama-cli -hf bartowski/SmolLM2-360M-Instruct-GGUF:Q4_K_M lms get bartowski/SmolLM2-360M-Instruct-GGUF vLLM is NOT a Mac tool, it is a CUDA/Linux serving engine. Unified memory is not a fixed VRAM slice; ~70% is usable for weights.
- Parameters
- 0.362B
- Q4_K_M size
- 0.271 GB
- Q8_0 size
- 0.386 GB
- Context
- 2k
- Ollama tag
- smollm2:360m
- Memory
- 18 GB unified
- Usable for weights
- ~12 GB
- Best runtime
- Ollama (llama.cpp Metal backend) / MLX
You could also run
Run SmolLM2 360M on other hardware
FAQ
Can Apple M3 Pro (18GB) run SmolLM2 360M?
Yes. SmolLM2 360M runs on Apple M3 Pro (18GB) at Q4_K_M (~1.2 GB of ~12 GB usable).
How much memory does SmolLM2 360M need?
Apple M3 Pro (18GB) has room to spare. At Q4_K_M the weights are ~0.271 GB; with KV cache and runtime overhead, budget ~1.2 GB at a 4k context.
What is the best tool to run SmolLM2 360M on macOS?
LM Studio for a simple setup; mlx-lm for the most speed. vLLM is NOT a Mac tool, it is a CUDA/Linux serving engine. Unified memory is not a fixed VRAM slice; ~70% is usable for weights.
Sources
Memory figures are estimates. See methodology.