text model · LFM · iOS
Can I run LFM2.5 8B-A1B on iPhone 17 Pro?
Yes. LFM2.5 8B-A1B runs on iPhone 17 Pro at Q4_K_M (~6.7 GB of ~8 GB usable).
Runs at Q4_K_M using ~6.7 GB of ~8 GB usable.
That figure is at a 4k context and moves about ±15% as context length changes. iPhone 17 Pro leaves ~1.3 GB of headroom.
- Q4_K_M needed
- ~6.7 GB
- Usable on device
- ~8 GB
- Device memory
- 12 GB
- Best quant
- Q4_K_M
Which quant fits
How to run it
On iOS use Apple Foundation Models (Built into iOS 26, ~3B on-device model, zero download, fully private.).
- 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
- 12 GB unified
- Usable for weights
- ~8 GB
- Power draw
- ~12 W
- Best runtime
- llama.cpp + Metal (via PocketPal or Off Grid app)
You could also run
Run LFM2.5 8B-A1B on other hardware
FAQ
Can iPhone 17 Pro run LFM2.5 8B-A1B?
Yes. LFM2.5 8B-A1B runs on iPhone 17 Pro at Q4_K_M (~6.7 GB of ~8 GB usable).
How much memory does LFM2.5 8B-A1B need?
iPhone 17 Pro 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 iOS?
On iPhone and iPad, Apple Foundation Models (Built into iOS 26, ~3B on-device model, zero download, fully private.) is the standard choice. Phones realistically run 1B-4B class models. Anything larger thermally throttles or OOMs.
Embed this
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/lfm2.5-8b-a1b/iphone-17-pro) 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.