text model · LFM · iOS

Can I run LFM2 24B-A2B on iPhone 16?

Compatibility verdict VRAM threshold engine

No, not enough memory would not load

No. LFM2 24B-A2B needs ~15.4 GB even at Q4_K_M, but iPhone 16 only has ~4.5 GB usable.

Needs ~15.4 GB Device usable ~4.5 GB

Needs ~15.4 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 10.9 GB: LFM2 24B-A2B needs roughly 15.4 GB at Q4_K_M and iPhone 16 leaves only about 4.5 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: ~4.5 GB
Device memory: 8 GB

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Which quant fits

Quant ladder vs ~4.5 GB usable

Q2_K

~12.1 GB

Q3_K_M

~13.7 GB

Q4_K_M

~15.4 GB

Q5_K_M

~19.1 GB

Q6_K

~21.7 GB

Q8_0

~25.6 GB

FP16

~46.4 GB

The line marks iPhone 16's ~4.5 GB budget; rungs past it are too large.

How to run it

On iOS use Apple Foundation Models (Built into iOS 26, ~3B on-device model, zero download, fully private.).

Model LFM

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

Full LFM2 24B-A2B requirements →

Device iOS

Memory: 8 GB unified
Usable for weights: ~4.5 GB
Power draw: ~11 W
Best runtime: llama.cpp + Metal (via PocketPal or Off Grid app)

Best models for iPhone 16 →

What you can run instead

Run LFM2 24B-A2B on other hardware

Apple M4 (24GB)Apple M4 Pro (24GB)Nvidia GeForce RTX 4090 (24GB)Nvidia GeForce RTX 3090 (24GB)AMD Radeon RX 7900 XTX (24GB)Nvidia GeForce RTX 5090 (32GB)

FAQ

Can iPhone 16 run LFM2 24B-A2B?

No. LFM2 24B-A2B needs ~15.4 GB even at Q4_K_M, but iPhone 16 only has ~4.5 GB usable.

How much memory does LFM2 24B-A2B need?

iPhone 16 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 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.

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Markdown

[![LFM2 24B-A2B on iPhone 16](https://localmodel.run/badge/lfm2-24b-a2b/iphone-16.svg)](https://localmodel.run/can-i-run/lfm2-24b-a2b/iphone-16)

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.