text model · Sarvam · Android
S Can I run Sarvam-30B on Samsung Galaxy S25 Ultra (16GB, 1TB config only)?
No. Sarvam-30B needs ~21.7 GB even at Q4_K_M, but Samsung Galaxy S25 Ultra (16GB, 1TB config only) only has ~12 GB usable.
Needs ~21.7 GB even at Q4_K_M, but only ~12 GB is usable.
- Q4_K_M needed
- ~21.7 GB
- Usable on device
- ~12 GB
- Device memory
- 16 GB
How to run it
On Android use PocketPal AI (Polished app, download GGUF and run offline.). NPU acceleration is limited and chip-specific; most apps run on CPU. Expect 1B-4B class.
- Parameters
- 30B (MoE, 2.4B active)
- Q4_K_M size
- 19.6 GB
- Context
- 64k
- Memory
- 16 GB ram
- Usable for weights
- ~12 GB
- Best runtime
- llama.cpp (PocketPal) or MLC-LLM (Adreno GPU path)
What you can run instead
Run Sarvam-30B on other hardware
FAQ
Can Samsung Galaxy S25 Ultra (16GB, 1TB config only) run Sarvam-30B?
No. Sarvam-30B needs ~21.7 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-30B need?
Samsung Galaxy S25 Ultra (16GB, 1TB config only) 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.
Sources
Memory figures are estimates. See methodology.