text model · SmolLM2 · Windows
Can I run SmolLM2 135M on Nvidia GeForce RTX 4080 (16GB)?
Yes. SmolLM2 135M runs on Nvidia GeForce RTX 4080 (16GB) at Q4_K_M (~1 GB of ~15 GB usable).
Runs at Q4_K_M using ~1 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 4080 (16GB) leaves ~14 GB of headroom, room to step up to FP16 for higher quality.
- Q4_K_M needed
- ~1 GB
- Usable on device
- ~15 GB
- Device memory
- 16 GB
- Best quant
- Q4_K_M
Which quant fits
Running cost · estimate
- Power draw
- ~320 W
- Electricity / 1M tokens
- ~$0
- Pays for itself after
- ~2,398M tok
At ~$0.15/kWh and the estimated ~4437 tok/s, a million generated tokens costs about $0 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$1,199 Nvidia GeForce RTX 4080 (16GB) pays for itself after roughly 2,398 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 smollm2:135m llama-cli -hf bartowski/SmolLM2-135M-Instruct-GGUF:Q4_K_M lms get bartowski/SmolLM2-135M-Instruct-GGUF - 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 vram
- Usable for weights
- ~15 GB
- Power draw
- ~320 W
- Best runtime
- vLLM (Linux) / Ollama (CUDA)
You could also run
Run SmolLM2 135M on other hardware
FAQ
Can Nvidia GeForce RTX 4080 (16GB) run SmolLM2 135M?
Yes. SmolLM2 135M runs on Nvidia GeForce RTX 4080 (16GB) at Q4_K_M (~1 GB of ~15 GB usable).
How much memory does SmolLM2 135M need?
Nvidia GeForce RTX 4080 (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 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/smollm2-135m/nvidia-rtx-4080-16gb) 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.