text model · SmolLM2 · Windows
Can I run SmolLM2 360M on 16GB RAM Laptop (CPU/iGPU only)?
Yes. SmolLM2 360M runs on 16GB RAM Laptop (CPU/iGPU only) 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
- 16 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 AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.
- Parameters
- 0.362B
- Q4_K_M size
- 0.271 GB
- Q8_0 size
- 0.386 GB
- Context
- 2k
- Ollama tag
- smollm2:360m
- Memory
- 16 GB ram
- Usable for weights
- ~12 GB
- Best runtime
- Ollama (llama.cpp backend)
You could also run
Run SmolLM2 360M on other hardware
FAQ
Can 16GB RAM Laptop (CPU/iGPU only) run SmolLM2 360M?
Yes. SmolLM2 360M runs on 16GB RAM Laptop (CPU/iGPU only) at Q4_K_M (~1.2 GB of ~12 GB usable).
How much memory does SmolLM2 360M need?
16GB RAM Laptop (CPU/iGPU only) 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 Windows?
LM Studio for a simple setup; Ollama (CUDA) for the most speed. AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.
Sources
Memory figures are estimates. See methodology.