text model · SmolLM2 · Windows
Can I run SmolLM2 135M on 32GB RAM Laptop (CPU/iGPU only)?
Yes. SmolLM2 135M runs on 32GB RAM Laptop (CPU/iGPU only) at Q4_K_M (~1 GB of ~28 GB usable).
Runs at Q4_K_M using ~1 GB of ~28 GB usable. You have room for FP16 for higher quality.
- Q4_K_M needed
- ~1 GB
- Usable on device
- ~28 GB
- Device memory
- 32 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 AMD GPUs run via Vulkan/ROCm at roughly half CUDA throughput. NVIDIA is the smooth path on Windows.
- Parameters
- 0.135B
- Q4_K_M size
- 0.105 GB
- Q8_0 size
- 0.145 GB
- Context
- 2k
- Ollama tag
- smollm2:135m
- Memory
- 32 GB ram
- Usable for weights
- ~28 GB
- Best runtime
- Ollama (llama.cpp backend)
You could also run
Run SmolLM2 135M on other hardware
FAQ
Can 32GB RAM Laptop (CPU/iGPU only) run SmolLM2 135M?
Yes. SmolLM2 135M runs on 32GB RAM Laptop (CPU/iGPU only) at Q4_K_M (~1 GB of ~28 GB usable).
How much memory does SmolLM2 135M need?
32GB RAM Laptop (CPU/iGPU only) 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 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.