text model · LFM · Windows
Can I run LFM2 350M on 16GB RAM Laptop (CPU/iGPU only)?
Yes. LFM2 350M runs on 16GB RAM Laptop (CPU/iGPU only) at Q4_K_M (~1.4 GB of ~12 GB usable).
Runs at Q4_K_M using ~1.4 GB of ~12 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. 16GB RAM Laptop (CPU/iGPU only) leaves ~10.6 GB of headroom, room to step up to FP16 for higher quality.
- Q4_K_M needed
- ~1.4 GB
- Usable on device
- ~12 GB
- Device memory
- 16 GB
- Best quant
- Q4_K_M
Which quant fits
Running cost · estimate
- Power draw
- ~28 W
- Electricity / 1M tokens
- ~$0.01
- Pays for itself after
- ~1,531M tok
At ~$0.15/kWh and the estimated ~100 tok/s, a million generated tokens costs about $0.01 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$750 16GB RAM Laptop (CPU/iGPU only) pays for itself after roughly 1,531 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 2 load the same Q4_K_M weights.
llama-cli -hf LiquidAI/LFM2-350M-GGUF:Q4_K_M lms get LiquidAI/LFM2-350M-GGUF How to run it
On Windows use LM Studio (Best GUI on Windows, auto-detects CUDA/Vulkan backends.).
- Parameters
- 0.354B
- Q4_K_M size
- 0.45 GB
- Q8_0 size
- 0.35 GB
- Context
- 128k
- Memory
- 16 GB ram
- Usable for weights
- ~12 GB
- Power draw
- ~28 W
- Best runtime
- Ollama (llama.cpp backend)
You could also run
Run LFM2 350M on other hardware
FAQ
Can 16GB RAM Laptop (CPU/iGPU only) run LFM2 350M?
Yes. LFM2 350M runs on 16GB RAM Laptop (CPU/iGPU only) at Q4_K_M (~1.4 GB of ~12 GB usable).
How much memory does LFM2 350M need?
16GB RAM Laptop (CPU/iGPU only) has room to spare. At Q4_K_M the weights are ~0.45 GB; with KV cache and runtime overhead, budget ~1.4 GB at a 4k context.
What is the best tool to run LFM2 350M 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/lfm2-350m/laptop-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.