text model · LFM · iOS
Can I run LFM2.5 8B-A1B on iPhone 17?
No. LFM2.5 8B-A1B needs ~6.7 GB even at Q4_K_M, but iPhone 17 only has ~4.5 GB usable.
Needs ~6.7 GB even at Q4_K_M, but only ~4.5 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 2.2 GB: LFM2.5 8B-A1B needs roughly 6.7 GB at Q4_K_M and iPhone 17 leaves only about 4.5 GB usable for a model. The lightest tracked hardware that runs LFM2.5 8B-A1B is the Nvidia GeForce RTX 3060 Ti (8GB) at 8 GB. See LFM2.5 8B-A1B on Nvidia GeForce RTX 3060 Ti (8GB).
- Q4_K_M needed
- ~6.7 GB
- Usable on device
- ~4.5 GB
- Device memory
- 8 GB
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
- 8 GB unified
- Usable for weights
- ~4.5 GB
- Best runtime
- llama.cpp + Metal (via PocketPal or Off Grid app)
What you can run instead
Run LFM2.5 8B-A1B on other hardware
FAQ
Can iPhone 17 run LFM2.5 8B-A1B?
No. LFM2.5 8B-A1B needs ~6.7 GB even at Q4_K_M, but iPhone 17 only has ~4.5 GB usable.
How much memory does LFM2.5 8B-A1B need?
iPhone 17 does not have enough memory. 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) Sources
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apple.com · 1 source
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developer.apple.com · 1 source
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en.wikipedia.org · 2 sources
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enclaveai.app · 1 source
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github.com · 3 sources
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huggingface.co · 2 sources
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layla-network.ai · 1 source
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macrumors.com · 1 source
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ollama.com · 1 source
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privatellm.app · 1 source
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.