• As global AI adoption accelerates, developers face a growing challenge: delivering large language model (LLM) performance that meets real-world latency and cost requirements. • Running models with tens of billions of parameters in production, especially for conversational or voice-based AI agents, demands high throughput, low latency, and predictable service-level performance. • For startups building sovereign AI models from scratch, these challenges are amplified by the need to balance model scale and accuracy with infrastructure efficiency-while also maintaining data sovereignty and cost control. • Sarvam AI, a generative AI startup based in Bengaluru, India, set out to build large, multilingual, multimodal foundation models that serve its country’s diverse population, support nearly two-dozen languages, and keep model development and data governance fully under India’s sovereign control. • To meet strict latency targets and improve inference efficiency for its flagship Sovereign 30B model, Sarvam AI collaborated with NVIDIA to co-design hardware and software optimizations. • This collaboration delivered a 4x speedup in inference performance on NVIDIA Blackwell over baseline NVIDIA H100 GPUs, and established a path for deployment on the next-generation NVIDIA Blackwell architecture.

Article Summaries:

  • As global AI adoption accelerates, developers face a growing challenge: delivering large language model (LLM) performance that meets real-world latency and cost requirements. Running models with tens of billions of parameters in production, especially for conversational or voice-based AI agents, demands high throughput, low latency, and predictable service-level performance. For startups building sovereign AI models from scratch, these challenges are amplified by the need to balance model scale and accuracy with infrastructure efficiency-while also maintaining data sovereignty and cost control

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