Skip to content
localmodel.run

text model · Sarvam · iOS

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

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 only has ~4.5 GB usable.

Needs ~16.3 GB Device usable ~4.5 GB

Needs ~16.3 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 11.8 GB: Sarvam-M 24B needs roughly 16.3 GB at Q4_K_M and iPhone 17 leaves only about 4.5 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
~4.5 GB
Device memory
8 GB
Share on X Share on Reddit

Which quant fits

Quant ladder vs ~4.5 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'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 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
8 GB unified
Usable for weights
~4.5 GB
Best runtime
llama.cpp + Metal (via PocketPal or Off Grid app)
Best models for iPhone 17 →

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 run Sarvam-M 24B?

No. Sarvam-M 24B needs ~16.3 GB even at Q4_K_M, but iPhone 17 only has ~4.5 GB usable.

How much memory does Sarvam-M 24B need?

iPhone 17 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.

Embed this

Sarvam-M 24B on iPhone 17 compatibility badge A live badge for your model card or README, updated as the data is.
Markdown
$ [![Sarvam-M 24B on iPhone 17](https://localmodel.run/badge/sarvam-m-24b/iphone-17.svg)](https://localmodel.run/can-i-run/sarvam-m-24b/iphone-17)

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.