text model · Sarvam · Android

Can I run Sarvam-105B on Samsung Galaxy S25 Ultra (16GB, 1TB config only)?

Compatibility verdict VRAM threshold engine

No, not enough memory would not load

No. Sarvam-105B needs ~67.5 GB even at Q4_K_M, but Samsung Galaxy S25 Ultra (16GB, 1TB config only) only has ~12 GB usable.

Needs ~67.5 GB Device usable ~12 GB

Needs ~67.5 GB even at Q4_K_M, but only ~12 GB is usable.

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

The gap is about 55.5 GB: Sarvam-105B needs roughly 67.5 GB at Q4_K_M and Samsung Galaxy S25 Ultra (16GB, 1TB config only) leaves only about 12 GB usable for a model. The lightest tracked hardware that runs Sarvam-105B is the Apple M4 Max (128GB) at 128 GB. See Sarvam-105B on Apple M4 Max (128GB).

Q4_K_M needed: ~67.5 GB
Usable on device: ~12 GB
Device memory: 16 GB

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Which quant fits

Quant ladder vs ~12 GB usable

Q2_K

~47.3 GB

Q3_K_M

~54.6 GB

Q4_K_M

~67.5 GB

Q5_K_M

~78.1 GB

Q6_K

~89.4 GB

Q8_0

~114.9 GB

FP16

~213.3 GB

The line marks Samsung Galaxy S25 Ultra (16GB, 1TB config only)'s ~12 GB budget; rungs past it are too large.

How to run it

On Android use PocketPal AI (Polished app, download GGUF and run offline.).

Model Sarvam

Parameters: 105B (MoE, 10.3B active)
Q4_K_M size: 64.2 GB
Context: 128k

Full Sarvam-105B requirements →

Device Android

Memory: 16 GB ram
Usable for weights: ~12 GB
Power draw: ~8 W
Best runtime: llama.cpp (PocketPal) or MLC-LLM (Adreno GPU path)

Best models for Samsung Galaxy S25 Ultra (16GB, 1TB config only) →

What you can run instead

Run Sarvam-105B on other hardware

Apple M4 Max (128GB)AMD Ryzen AI Halo (128GB)Apple M5 Max (128GB)Apple M3 Ultra (256GB)

FAQ

Can Samsung Galaxy S25 Ultra (16GB, 1TB config only) run Sarvam-105B?

No. Sarvam-105B needs ~67.5 GB even at Q4_K_M, but Samsung Galaxy S25 Ultra (16GB, 1TB config only) only has ~12 GB usable.

How much memory does Sarvam-105B need?

Samsung Galaxy S25 Ultra (16GB, 1TB config only) does not have enough memory. At Q4_K_M the weights are ~64.2 GB; with KV cache and runtime overhead, budget ~67.5 GB at a 4k context. It is a Mixture-of-Experts model (105B total / 10.3B active), so all experts must stay in memory; memory tracks total params, not active params.

What is the best tool to run Sarvam-105B on Android?

On Android, PocketPal AI (Polished app, download GGUF and run offline.) is the go-to option. NPU acceleration is limited and chip-specific; most apps run on CPU. Expect 1B-4B class.

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[![Sarvam-105B on Samsung Galaxy S25 Ultra (16GB, 1TB config only)](https://localmodel.run/badge/sarvam-105b/samsung-s25-ultra-16gb.svg)](https://localmodel.run/can-i-run/sarvam-105b/samsung-s25-ultra-16gb)

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.