Sarvam AI

Sarvam 105B Conversations

Sarvam AI

Sarvam released updated 105B conversational and work-agent variants at its July 2026 Epoch event. The conversational specialization targets low-latency agents, noisy speech transcripts, code-mixed language, policy following, and multi-turn tool calls. It generates text as part of a voice-agent stack rather than natively replacing speech recognition or synthesis. Both are documented as available through the Sarvam API. Public downloadable weights and full endpoint specifications for these updated checkpoints are not established. The release month is retained because the two-day event does not establish an exact first-availability day.

Sarvam 105B Work

Sarvam AI

Sarvam released updated 105B conversational and work-agent variants at its July 2026 Epoch event. The work-agent specialization is a reasoning variant tuned for sandboxed software engineering, data analysis, knowledge retrieval, office tasks, and long-running autonomy. It is distinct from the original open checkpoint and from Indus’s routing to third-party models. Both are documented as available through the Sarvam API. Public downloadable weights and full endpoint specifications for these updated checkpoints are not established. The release month is retained because the two-day event does not establish an exact first-availability day.

Sarvam 30B

Sarvam AI

Sarvam 30B is an Indian multilingual language model trained from scratch for practical local deployment. Its sparse experts support coding, reasoning, tool use, and conversational quality across 22 Indian languages. The 30B tier uses fewer layers and grouped key-value attention to reduce inference overhead. Public weights were released on March 6, following the family’s February announcement. 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.

Sarvam 105B

Sarvam AI

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.