text model · SmolLM2 · Windows
Can I run SmolLM2 1.7B on Nvidia GeForce RTX 4060 Ti (16GB)?
Yes. SmolLM2 1.7B runs on Nvidia GeForce RTX 4060 Ti (16GB) at Q4_K_M (~2.2 GB of ~15 GB usable).
Runs at Q4_K_M using ~2.2 GB of ~15 GB usable. You have room for FP16 for higher quality.
That figure is at a 4k context and moves about ±15% as context length changes. Nvidia GeForce RTX 4060 Ti (16GB) leaves ~12.8 GB of headroom, room to step up to FP16 for higher quality.
- Q4_K_M needed
- ~2.2 GB
- Usable on device
- ~15 GB
- Device memory
- 16 GB
- Best quant
- Q4_K_M
Which quant fits
Running cost · estimate
- Power draw
- ~165 W
- Electricity / 1M tokens
- ~$0.04
- Pays for itself after
- ~1,085M tok
At ~$0.15/kWh and the estimated ~177 tok/s, a million generated tokens costs about $0.04 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$499 Nvidia GeForce RTX 4060 Ti (16GB) pays for itself after roughly 1,085 million tokens, so local hardware is mostly a fixed cost, not a per-token one. TDP is the peak draw, so this is an upper bound. Assumptions.
Run it
Pick your tool. All 3 load the same Q4_K_M weights.
ollama run smollm2:1.7b llama-cli -hf bartowski/SmolLM2-1.7B-Instruct-GGUF:Q4_K_M lms get bartowski/SmolLM2-1.7B-Instruct-GGUF - Parameters
- 1.7B
- Q4_K_M size
- 1.06 GB
- Q8_0 size
- 1.82 GB
- Context
- 8k
- Ollama tag
- smollm2:1.7b
- Memory
- 16 GB vram
- Usable for weights
- ~15 GB
- Power draw
- ~165 W
- Best runtime
- Ollama (CUDA) / llama.cpp CUDA
You could also run
Run SmolLM2 1.7B on other hardware
FAQ
Can Nvidia GeForce RTX 4060 Ti (16GB) run SmolLM2 1.7B?
Yes. SmolLM2 1.7B runs on Nvidia GeForce RTX 4060 Ti (16GB) at Q4_K_M (~2.2 GB of ~15 GB usable).
How much memory does SmolLM2 1.7B need?
Nvidia GeForce RTX 4060 Ti (16GB) has room to spare. At Q4_K_M the weights are ~1.06 GB; with KV cache and runtime overhead, budget ~2.2 GB at a 4k context.
What is the best tool to run SmolLM2 1.7B 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.
Embed this
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/smollm2-1.7b/nvidia-rtx-4060-ti-16gb) Sources
Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.