text model · SmolLM2 · Windows
Can I run SmolLM2 135M on Nvidia GeForce RTX 4090 (24GB)?
Yes. SmolLM2 135M runs on Nvidia GeForce RTX 4090 (24GB) at Q4_K_M (~1 GB of ~23 GB usable).
Runs at Q4_K_M using ~1 GB of ~23 GB usable. You have room for FP16 for higher quality.
- Q4_K_M needed
- ~1 GB
- Usable on device
- ~23 GB
- Device memory
- 24 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
- 24 GB vram
- Usable for weights
- ~23 GB
- Best runtime
- vLLM (Linux) / Ollama (CUDA)
You could also run
Run SmolLM2 135M on other hardware
FAQ
Can Nvidia GeForce RTX 4090 (24GB) run SmolLM2 135M?
Yes. SmolLM2 135M runs on Nvidia GeForce RTX 4090 (24GB) at Q4_K_M (~1 GB of ~23 GB usable).
How much memory does SmolLM2 135M need?
Nvidia GeForce RTX 4090 (24GB) 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.