text model · LFM · macOS
Can I run LFM2.5 8B-A1B on Apple M4 (24GB)?
Yes. LFM2.5 8B-A1B runs on Apple M4 (24GB) at Q4_K_M (~6.7 GB of ~16 GB usable).
Runs at Q4_K_M using ~6.7 GB of ~16 GB usable. You have room for Q8_0 for higher quality.
That figure is at a 4k context and moves about ±15% as context length changes. Apple M4 (24GB) leaves ~9.3 GB of headroom, room to step up to Q8_0 for higher quality.
- Q4_K_M needed
- ~6.7 GB
- Usable on device
- ~16 GB
- Device memory
- 24 GB
- Best quant
- Q4_K_M
Which quant fits
Run it
Pick your tool. All 3 load the same Q4_K_M weights.
ollama run lfm2.5:8b-a1b llama-cli -hf LiquidAI/LFM2.5-8B-A1B-GGUF:Q4_K_M lms get LiquidAI/LFM2.5-8B-A1B-GGUF - Parameters
- 8.3B (MoE, 1.5B active)
- Q4_K_M size
- 5.2 GB
- Q8_0 size
- 9 GB
- Context
- 128k
- Ollama tag
- lfm2.5:8b-a1b
- Memory
- 24 GB unified
- Usable for weights
- ~16 GB
- Power draw
- ~65 W
- Best runtime
- Ollama (MLX backend, preview) / MLX direct
You could also run
Run LFM2.5 8B-A1B on other hardware
FAQ
Can Apple M4 (24GB) run LFM2.5 8B-A1B?
Yes. LFM2.5 8B-A1B runs on Apple M4 (24GB) at Q4_K_M (~6.7 GB of ~16 GB usable).
How much memory does LFM2.5 8B-A1B need?
Apple M4 (24GB) has room to spare. At Q4_K_M the weights are ~5.2 GB; with KV cache and runtime overhead, budget ~6.7 GB at a 4k context. It is a Mixture-of-Experts model (8.3B total / 1.5B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run LFM2.5 8B-A1B 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/lfm2.5-8b-a1b/apple-m4-24gb) 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.