text model · LFM · macOS
Can I run LFM2 350M on Apple M2 (16GB)?
Yes. LFM2 350M runs on Apple M2 (16GB) at Q4_K_M (~1.4 GB of ~10.5 GB usable).
Runs at Q4_K_M using ~1.4 GB of ~10.5 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. Apple M2 (16GB) leaves ~9.1 GB of headroom, room to step up to FP16 for higher quality.
- Q4_K_M needed
- ~1.4 GB
- Usable on device
- ~10.5 GB
- Device memory
- 16 GB
- Best quant
- Q4_K_M
Which quant fits
Running cost · estimate
- Power draw
- ~50 W
- Electricity / 1M tokens
- ~$0.01
- Pays for itself after
- ~2,447M tok
At ~$0.15/kWh and the estimated ~178 tok/s, a million generated tokens costs about $0.01 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$1,199 Apple M2 (16GB) pays for itself after roughly 2,447 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 macOS use LM Studio (Polished GUI, ships MLX on Apple Silicon, one-click model downloads.).
- Parameters
- 0.354B
- Q4_K_M size
- 0.45 GB
- Q8_0 size
- 0.35 GB
- Context
- 128k
- Memory
- 16 GB unified
- Usable for weights
- ~10.5 GB
- Power draw
- ~50 W
- Best runtime
- Ollama (llama.cpp Metal backend) / MLX
You could also run
Run LFM2 350M on other hardware
FAQ
Can Apple M2 (16GB) run LFM2 350M?
Yes. LFM2 350M runs on Apple M2 (16GB) at Q4_K_M (~1.4 GB of ~10.5 GB usable).
How much memory does LFM2 350M need?
Apple M2 (16GB) 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 macOS?
LM Studio for a simple setup; mlx-lm for the most speed. vLLM is NOT a Mac tool, it is a CUDA/Linux serving engine. Unified memory is not a fixed VRAM slice; ~70% is usable for weights.
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A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/lfm2-350m/apple-m2-16gb) 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.