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Sarvam 105B

Sarvam AIOpen WeightsPending Human Review

Sarvam 105B is a multilingual sparse language model trained from scratch in India. It emphasizes reasoning, coding, and agents across 22 Indian languages, using an MLA-style attention stack and context extension. Public access began through the limited Indus beta on February 20; Apache-licensed weight publication followed on March 6. This page represents the original open checkpoint, not later hosted conversational and work-agent specializations. The checkpoint supports up to 128K context, while reported evaluations use shorter task-specific limits. Model outputs require task-specific checks in languages and domains beyond the training focus.

2026-02-20
105B total, 10.3B active
Mixture-of-Experts Transformer
Apache-2.0

Specifications

Parameters
105B total, 10.3B active
Architecture
Mixture-of-Experts Transformer
License
Apache-2.0
Context Window
131,072 tokens
Type
text
Modalities
text

Benchmark Scores

Advanced Specifications

Model Family
Sarvam
API Access
Not Available
Chat Interface
Available
Multilingual Support
Yes

Capabilities & Limitations

Capabilities
multilingual generationreasoningcodingtool use
Known Limitations
Language performance varies by taskSparse activation does not remove total weight memory needsIndus beta access initially had capacity limits
Notable Use Cases
Indian-language assistantsprivate multilingual inferencecoding and agent research

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