text model · Sarvam · iOS

Can I run Sarvam-M 24B on iPhone 17 Pro?

Compatibility verdict VRAM threshold engine

No, not enough memory would not load

No. Sarvam-M 24B needs ~16.3 GB even at Q4_K_M, but iPhone 17 Pro only has ~8 GB usable.

Needs ~16.3 GB Device usable ~8 GB

Needs ~16.3 GB even at Q4_K_M, but only ~8 GB is usable.

That figure is at a 4k context and moves about ±15% as context length changes.

The gap is about 8.3 GB: Sarvam-M 24B needs roughly 16.3 GB at Q4_K_M and iPhone 17 Pro leaves only about 8 GB usable for a model. The lightest tracked hardware that runs Sarvam-M 24B is the Nvidia GeForce RTX 4090 (24GB) at 24 GB. See Sarvam-M 24B on Nvidia GeForce RTX 4090 (24GB).

Q4_K_M needed: ~16.3 GB
Usable on device: ~8 GB
Device memory: 12 GB

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Which quant fits

Quant ladder vs ~8 GB usable

Q2_K

~12.1 GB

Q3_K_M

~13.7 GB

Q4_K_M

~16.3 GB

Q5_K_M

~19.1 GB

Q6_K

~21.7 GB

Q8_0

~27.1 GB

FP16

~49.2 GB

The line marks iPhone 17 Pro's ~8 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 Sarvam

Parameters: 24B
Q4_K_M size: 14.3 GB
Q8_0 size: 25.1 GB
Context: 32k

Full Sarvam-M 24B requirements →

Device iOS

Memory: 12 GB unified
Usable for weights: ~8 GB
Power draw: ~12 W
Best runtime: llama.cpp + Metal (via PocketPal or Off Grid app)

Best models for iPhone 17 Pro →

What you can run instead

Run Sarvam-M 24B on other hardware

Nvidia GeForce RTX 4090 (24GB)Nvidia GeForce RTX 3090 (24GB)AMD Radeon RX 7900 XTX (24GB)Nvidia GeForce RTX 5090 (32GB)32GB RAM Laptop (CPU/iGPU only)Apple M5 (32GB)

FAQ

Can iPhone 17 Pro run Sarvam-M 24B?

No. Sarvam-M 24B needs ~16.3 GB even at Q4_K_M, but iPhone 17 Pro only has ~8 GB usable.

How much memory does Sarvam-M 24B need?

iPhone 17 Pro does not have enough memory. At Q4_K_M the weights are ~14.3 GB; with KV cache and runtime overhead, budget ~16.3 GB at a 4k context.

What is the best tool to run Sarvam-M 24B 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

[![Sarvam-M 24B on iPhone 17 Pro](https://localmodel.run/badge/sarvam-m-24b/iphone-17-pro.svg)](https://localmodel.run/can-i-run/sarvam-m-24b/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.