From Scratch to SOTA: Training a 3B State-Space Vision Model for 1.4 Billion People

Official Schedule Context

India has 22 official languages. Across those languages live over a billion people whose knowledge

is locked inside scanned images in scripts that most frontier models perform poorly. The problem is

dire - until now, there wasn't even a comprehensive benchmark to measure Indic OCR performance, let

alone training data at scale. When Sarvam AI set out to solve this, we had to build the

infrastructure before the model, creating the first ground-truth benchmark for Indic document

intelligence. In this talk, Krishna Srinivasan, who led the Vision Models team to build India's

first sovereign VLM from scratch, will walk through the end-to-end engineering lifecycle. We will

cover: (a) Architecture: Why we chose a 3B-parameter state-space architecture over transformer

baselines to handle high-resolution visual inputs with minimal memory overhead and faster inference.

(b) Training Pipeline: The exact recipe we used: starting with text-only pre-training, moving to

continual pre-training with text and images, followed by SFT. Finally, we'll cover the advances we

made in implementing large-scale RL with Verifiable Rewards for visual tasks in just 3 days using

deterministic character-level reward signals. (c) Compute Efficiency: How we trained a frontier-

competitive multimodal model with extreme capital efficiency, optimizing distributed training and

GPU cluster management to punch far above our compute class. (d) Agentic Workflows: How this model

powers Sarvam Akshar, a first-of-its-kind agentic document intelligence workbench featuring visual

grounding and automated proofreading loops. The results speak for themselves: Sarvam Vision achieves

best-in-class global scores (84.3% on olmOCR-Bench, 93.28% on OmniDocBench) and dominates Indic OCR.

Attendees will learn the blueprint for compute-efficient multimodal training, and deploying state-

space VLMs for population-scale enterprise workloads.

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