text model · LFM · macOS
Can I run LFM2.5 1.2B Thinking on Apple M1 (8GB)?
Yes. LFM2.5 1.2B Thinking runs on Apple M1 (8GB) at Q4_K_M (~1.8 GB of ~5.5 GB usable).
Runs at Q4_K_M using ~1.8 GB of ~5.5 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 M1 (8GB) leaves ~3.7 GB of headroom, room to step up to FP16 for higher quality.
- Q4_K_M needed
- ~1.8 GB
- Usable on device
- ~5.5 GB
- Device memory
- 8 GB
- Best quant
- Q4_K_M
Which quant fits
Running cost · estimate
- Power draw
- ~39 W
- Electricity / 1M tokens
- ~$0.02
- Pays for itself after
- ~2,081M tok
At ~$0.15/kWh and the estimated ~80 tok/s, a million generated tokens costs about $0.02 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$999 Apple M1 (8GB) pays for itself after roughly 2,081 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 lfm2.5-thinking:1.2b llama-cli -hf LiquidAI/LFM2.5-1.2B-Thinking-GGUF:Q4_K_M lms get LiquidAI/LFM2.5-1.2B-Thinking-GGUF - Parameters
- 1.17B
- Q4_K_M size
- 0.68 GB
- Q8_0 size
- 1.16 GB
- Context
- 128k
- Ollama tag
- lfm2.5-thinking:1.2b
- Memory
- 8 GB unified
- Usable for weights
- ~5.5 GB
- Power draw
- ~39 W
- Best runtime
- Ollama (llama.cpp Metal backend)
You could also run
Run LFM2.5 1.2B Thinking on other hardware
FAQ
Can Apple M1 (8GB) run LFM2.5 1.2B Thinking?
Yes. LFM2.5 1.2B Thinking runs on Apple M1 (8GB) at Q4_K_M (~1.8 GB of ~5.5 GB usable).
How much memory does LFM2.5 1.2B Thinking need?
Apple M1 (8GB) has room to spare. At Q4_K_M the weights are ~0.68 GB; with KV cache and runtime overhead, budget ~1.8 GB at a 4k context.
What is the best tool to run LFM2.5 1.2B Thinking 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.
Embed this
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/lfm2.5-1.2b-thinking/apple-m1-8gb) 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.