Text model · Sarvam
SSarvam-30B requirements
Sarvam family · 30B params (Mixture-of-Experts, 2.4B active) · released Mar 2026. Minimum to run at Q4_K_M: Nvidia GeForce RTX 4090 (24GB).
Quantization sizes
| Quantization | Size on disk |
|---|---|
| Q2_K | 12.6 GB est |
| Q3_K_M | 14.7 GB est |
| Q4_K_M (default) | 19.6 GB |
| Q5_K_M | 21.4 GB est |
| Q6_K | 24.6 GB est |
| Q8_0 | 31.9 GB est |
| FP16 | 60 GB est |
Lower quant = smaller and faster, slightly lower quality. Q4_K_M is the common default.
Run it
llama-cli -hf sarvamai/sarvam-30b-gguf:Q4_K_M lms get sarvamai/sarvam-30b-gguf Which devices can run Sarvam-30B?
Apple Silicon Macs
RAM-only laptops
iPhone & iPad
Android
NVIDIA GPUs
FAQ
How much VRAM or RAM does Sarvam-30B need?
At Q4_K_M, Sarvam-30B needs about 21.7 GB (weights ~19.6 GB + KV cache + overhead) at a 4k context. At Q8_0 budget ~34 GB.
Can Sarvam-30B run on a laptop?
Sarvam-30B is large; you need a 24 GB+ GPU or a 32-48 GB Mac at Q4_K_M.
Is Sarvam-30B cheaper to run because it is a MoE model?
It is faster, not lighter. Sarvam-30B activates only 2.4B of 30B params per token (so it runs quickly), but all experts must stay in memory, so it still needs memory for the full 30B.
Can I use Sarvam-30B commercially?
Yes. Sarvam-30B is licensed Apache-2.0, which permits commercial use.
MoE with 128 sparse experts, top-6 routing, 2.4B active params. Released 2026-03 under Apache 2.0. Q4_K_M 19.6GB confirmed by summing the 6 shards in the official GGUF repo. No official Q8_0 GGUF released.
Sources
Memory figures are estimates. See methodology.