Decoding the Open-source Blueprint for India's Sovereign AI Future
summary
A production-ready sovereign stack called M4 coding runs on Indian infrastructure using OpenStack + OpenShift, Kubernetes, multi-GPU nodes (eight GPUs per node) and GPU choices H100 or MI400, with inferencing via vLLM / LLMD and OEM optimizations like TRT-LLM/NIMS. Benchmarks show 3–5× speed improvement and near double throughput at scale, an estimated $5.25M annual saving on a Sarvam 30B deployment, 100% domestic data residency (NITI-certified GCC) and pay-as-you-go pricing at ~9 rupees/hour.
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chapters (18)
- 0:00Introduction
- 1:03Why AI sovereignty matters
- 3:11Four layers of AI sovereignty
- 8:53Local AI talent and infrastructure
- 9:14NextGen coding agent use case
- 11:38Model quality and open-source alternatives
- 13:06Building AI infrastructure
- 16:39OpenShift AI deployment
- 18:02Choosing models for coding
- 19:04Mixture of Experts models
- 22:33Optimizing AI inferencing
- 26:45Tooling for coding agents
- 29:32Tokenomics and cost-effective AI
- 31:18Global AI infrastructure benchmarking
- 33:03AI for IT efficiency
- 37:37LLMD project gains
- 38:40Open source for global innovation
- 39:43Building India's AI skill set
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