LocalClaw/LLM/Sarvam 30B
Open-weight MoE

Sarvam 30B

Catalogue summary: 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.

Repository editorial metadata; verify comparative claims in the linked upstream material.

32 GB catalogue minimum32 GB RAMQ4_K_MCoding assistant
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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 has a catalogue minimum of 32 GB RAM with Q4_K_M. Actual memory use and speed vary by context length, runtime, backend and system headroom.

Use sarvam-30b as the catalogue search term in a compatible runtime, and confirm the available format on the upstream repository before download.

Model sourcePublic GGUF repository (verified 2026-09-07): sarvamai/sarvam-30b-ggufUpstream releasePaper or model cardLicence text
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.

Catalogue record

  • Family: sarvam
  • Parameters: 32B (2.4B active, MoE)
  • Recommended quantization: Q4_K_M
  • Catalogue minimum RAM: 32 GB
  • Catalogue model size: 18 GB
  • Tags: chat, code, reasoning, multilingual, power

Practical limits

  • Catalogue RAM is a minimum estimate, not a guarantee for every context length or runtime.
  • Speed and memory use vary by quantization, backend, context length and system headroom.
  • Verify architecture, licence and usage restrictions in the linked upstream material before deployment.

Catalogue tags

  • chat
  • code
  • reasoning
  • multilingual
  • power

Capability profile

Repository catalogue ratings used by LocalClaw's editorial rubric. They are not a standardized third-party benchmark.

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

Related catalogue entries

Linked mechanically by family, shared tags and nearby RAM tier; this is not a quality ranking.

LLM-jp-4 33B Thinking 33BAgents-A1 35B (3B active, MoE)Granite 4.2 (30B) 29.3BOLMo 3 32B Think 32BPhi-3.5 MoE Instruct 42B (6.6B active, MoE)Qwen 3 (32B) 32BQwen 3.5 (27B) 27BQwen 3.6 (27B) 27B

Where to go next