text model · SmolLM2 · macOS
Can I run SmolLM2 135M on Apple M4 (16GB)?
Yes. SmolLM2 135M runs on Apple M4 (16GB) at Q4_K_M (~1 GB of ~10.5 GB usable).
Runs at Q4_K_M using ~1 GB of ~10.5 GB usable. You have room for FP16 for higher quality.
- Q4_K_M needed
- ~1 GB
- Usable on device
- ~10.5 GB
- Device memory
- 16 GB
- Best quant
- Q4_K_M
Run it
Pick your tool. All three load the same Q4_K_M weights.
ollama run smollm2:135m llama-cli -hf bartowski/SmolLM2-135M-Instruct-GGUF:Q4_K_M lms get bartowski/SmolLM2-135M-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
- 0.135B
- Q4_K_M size
- 0.105 GB
- Q8_0 size
- 0.145 GB
- Context
- 2k
- Ollama tag
- smollm2:135m
- Memory
- 16 GB unified
- Usable for weights
- ~10.5 GB
- Best runtime
- Ollama (MLX backend, preview) / MLX direct
You could also run
Run SmolLM2 135M on other hardware
FAQ
Can Apple M4 (16GB) run SmolLM2 135M?
Yes. SmolLM2 135M runs on Apple M4 (16GB) at Q4_K_M (~1 GB of ~10.5 GB usable).
How much memory does SmolLM2 135M need?
Apple M4 (16GB) has room to spare. At Q4_K_M the weights are ~0.105 GB; with KV cache and runtime overhead, budget ~1 GB at a 4k context.
What is the best tool to run SmolLM2 135M 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.