Skip to content

text model · Sarvam · Android

Can I run Sarvam-30B on Generic Android Phone (8GB RAM)?

Compatibility verdict VRAM threshold engine
No, not enough memory would not load

No. Sarvam-30B needs ~21.7 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) only has ~4.5 GB usable.

Needs ~21.7 GB Device usable ~4.5 GB

Needs ~21.7 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 17.2 GB: Sarvam-30B needs roughly 21.7 GB at Q4_K_M and Generic Android Phone (8GB RAM) leaves only about 4.5 GB usable for a model. The lightest tracked hardware that runs Sarvam-30B is the Nvidia GeForce RTX 4090 (24GB) at 24 GB. See Sarvam-30B on Nvidia GeForce RTX 4090 (24GB).

Q4_K_M needed
~21.7 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
~14.7 GB
Q3_K_M
~16.8 GB
Q4_K_M
~21.7 GB
Q5_K_M
~23.5 GB
Q6_K
~26.7 GB
Q8_0
~34 GB
FP16
~62.1 GB
The line marks Generic Android Phone (8GB RAM)'s ~4.5 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
30B (MoE, 2.4B active)
Q4_K_M size
19.6 GB
Context
64k
Full Sarvam-30B requirements →
Device Android
Memory
8 GB ram
Usable for weights
~4.5 GB
Best runtime
llama.cpp (PocketPal or SmolChat)
Best models for Generic Android Phone (8GB RAM) →

What you can run instead

Run Sarvam-30B on other hardware

FAQ

Can Generic Android Phone (8GB RAM) run Sarvam-30B?

No. Sarvam-30B needs ~21.7 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) only has ~4.5 GB usable.

How much memory does Sarvam-30B need?

Generic Android Phone (8GB RAM) does not have enough memory. At Q4_K_M the weights are ~19.6 GB; with KV cache and runtime overhead, budget ~21.7 GB at a 4k context. It is a Mixture-of-Experts model (30B total / 2.4B active), so all experts must stay in memory; memory tracks total params, not active params.

What is the best tool to run Sarvam-30B 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.

Embed this

Sarvam-30B on Generic Android Phone (8GB RAM) compatibility badge A live badge for your model card or README, updated as the data is.
Markdown
$ [![Sarvam-30B on Generic Android Phone (8GB RAM)](https://localmodel.run/badge/sarvam-30b/android-generic-8gb.svg)](https://localmodel.run/can-i-run/sarvam-30b/android-generic-8gb)

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.