LocalClaw/LLM/Sarvam 30B
Open-weight MoE

Sarvam 30B

Sarvam AI open-weight MoE model trained for Indian languages, coding, reasoning, tool use and practical local deployment. Apache 2.0 with official GGUF availability.

32 GB power user 32 GB RAM Q4_K_M Indian-language local assistant
Run with LocalClaw Compare all models
Parameters
32B (2.4B active, MoE)
Minimum RAM
32 GB
Model size
18 GB
Quantization
Q4_K_M

Can Sarvam 30B run locally?

Sarvam 30B belongs on 32 GB machines when you want stronger quality without jumping to server hardware.

Search for sarvam-30b in LM Studio or another GGUF-compatible runtime.

Model sourcesarvamai/sarvam-30b-gguf
chatcodereasoningmultilingualpower

Install path

01
Check RAM fitMinimum 32 GB RAM. Start with the Q4_K_M quant.
02
Load the modelSearch sarvam-30b in LM Studio.
03
Control locallyUse LocalClaw to manage models, agents, chat, channels and scheduled OpenClaw work.

Strengths

  • Official Apache 2.0 open-weight release from Sarvam AI
  • Designed for Indian-language conversation and code-mixed local assistants
  • MoE shape keeps active compute much smaller than total parameter count
  • Strong public benchmark claims for math, coding and agentic tasks
  • Official GGUF repo is available for llama.cpp and LM Studio style workflows
  • Good fit when multilingual Indic support matters more than generic English-only ranking

Limitations

  • Custom Sarvam MoE architecture may need recent runtimes or patches
  • 32B total weights still require workstation-class memory when quantized
  • Independent local-runtime benchmarks are still limited compared with Qwen, Gemma or Llama
  • Best performance claims depend on official benchmark settings and should be validated locally

Best use cases

  • Indian-language local assistant
  • Code-mixed chat and support workflows
  • Local reasoning and coding on 32GB+ workstations
  • Tool-calling agents with Indic language users
  • Private multilingual document workflows
  • Evaluating sovereign open-weight models

Capability profile

speed
5
quality
8
coding
8
reasoning
8

Technical notes

Developer
Sarvam AI
License
Apache 2.0
Context window
65,536 tokens
Architecture
Mixture-of-Experts model with about 32B total parameters, 128 experts, top-6 routing and about 2.4B non-embedding active parameters.

This model fits these next steps

Hardware fit is based on LocalClaw's RAM tier, model size and quantization metadata. Always leave memory headroom for your OS and runtime.

Comfortable headroomMac mini M4 Pro 48GBMobile workstationMacBook Pro M4 Max 36GBPower-user picks32GB RAM guide

Similar models to compare

Qwen 3 MoE (30B/3B active) 30B (3B active)OLMo 3 32B Think 32BMistral Small 3.2 (24B) 24BGemma 4 26B A4B 26B (A4B active)

Where to go next