text model · SmolLM2 · Windows
Can I run SmolLM2 360M on 8GB RAM Laptop (CPU/iGPU only)?
Yes. SmolLM2 360M runs on 8GB RAM Laptop (CPU/iGPU only) at Q4_K_M (~1.2 GB of ~5 GB usable).
Runs at Q4_K_M using ~1.2 GB of ~5 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. 8GB RAM Laptop (CPU/iGPU only) leaves ~3.8 GB of headroom, room to step up to FP16 for higher quality.
- Q4_K_M needed
- ~1.2 GB
- Usable on device
- ~5 GB
- Device memory
- 8 GB
- Best quant
- Q4_K_M
Which quant fits
Running cost · estimate
- Power draw
- ~15 W
- Electricity / 1M tokens
- ~$0
- Pays for itself after
- ~700M tok
At ~$0.15/kWh and the estimated ~165 tok/s, a million generated tokens costs about $0 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$350 8GB RAM Laptop (CPU/iGPU only) pays for itself after roughly 700 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 smollm2:360m llama-cli -hf bartowski/SmolLM2-360M-Instruct-GGUF:Q4_K_M lms get bartowski/SmolLM2-360M-Instruct-GGUF - Parameters
- 0.362B
- Q4_K_M size
- 0.271 GB
- Q8_0 size
- 0.386 GB
- Context
- 2k
- Ollama tag
- smollm2:360m
- Memory
- 8 GB ram
- Usable for weights
- ~5 GB
- Power draw
- ~15 W
- Best runtime
- Ollama (llama.cpp backend)
You could also run
Run SmolLM2 360M on other hardware
FAQ
Can 8GB RAM Laptop (CPU/iGPU only) run SmolLM2 360M?
Yes. SmolLM2 360M runs on 8GB RAM Laptop (CPU/iGPU only) at Q4_K_M (~1.2 GB of ~5 GB usable).
How much memory does SmolLM2 360M need?
8GB 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.
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A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/smollm2-360m/laptop-8gb) 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.