text model · LFM · macOS
Can I run LFM2 24B-A2B on Apple M2 (16GB)?
No. LFM2 24B-A2B needs ~15.4 GB even at Q4_K_M, but Apple M2 (16GB) only has ~10.5 GB usable.
Needs ~15.4 GB even at Q4_K_M, but only ~10.5 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 4.9 GB: LFM2 24B-A2B needs roughly 15.4 GB at Q4_K_M and Apple M2 (16GB) leaves only about 10.5 GB usable for a model. The lightest tracked hardware that runs LFM2 24B-A2B is the Apple M4 (24GB) at 24 GB. See LFM2 24B-A2B on Apple M4 (24GB).
- Q4_K_M needed
- ~15.4 GB
- Usable on device
- ~10.5 GB
- Device memory
- 16 GB
Which quant fits
- Parameters
- 24B (MoE, 2.3B active)
- Q4_K_M size
- 13.43 GB
- Q8_0 size
- 23.61 GB
- Context
- 32k
- Ollama tag
- lfm2:24b
- Memory
- 16 GB unified
- Usable for weights
- ~10.5 GB
- Power draw
- ~50 W
- Best runtime
- Ollama (llama.cpp Metal backend) / MLX
What you can run instead
Run LFM2 24B-A2B on other hardware
FAQ
Can Apple M2 (16GB) run LFM2 24B-A2B?
No. LFM2 24B-A2B needs ~15.4 GB even at Q4_K_M, but Apple M2 (16GB) only has ~10.5 GB usable.
How much memory does LFM2 24B-A2B need?
Apple M2 (16GB) does not have enough memory. At Q4_K_M the weights are ~13.43 GB; with KV cache and runtime overhead, budget ~15.4 GB at a 4k context. It is a Mixture-of-Experts model (24B total / 2.3B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run LFM2 24B-A2B 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/lfm2-24b-a2b/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.