Large Language Models (LLMs)
Technical Fundamentals
A Large Language Model (LLM) is an AI system based on the transformer architecture, trained on massive text corpora to predict and generate language. Training involves running billions of parameters through iterative gradient descent optimisation across trillions of tokens of text. This process is compute-intensive: frontier models like GPT-4 required thousands of NVIDIA A100 or H100 GPUs running for weeks.
- Transformer architecture (Vaswani et al., 2017 — "Attention Is All You Need") is the foundation of all modern LLMs including GPT, Llama, and Indian models
- Training parameters (size) indicate model complexity: GPT-3 had 175B parameters; Sarvam's model has 105B parameters; BharatGen Param 2 has 17B parameters
- India-specific LLM challenge: Most global training data is in English; Indian-language training data (22 scheduled languages) is sparse, requiring curated multilingual datasets
- Key GPU requirement for LLM training: NVIDIA H100 (80 GB HBM3 memory, 3.35 PFLOPS FP8 performance) is the current industry standard
● Tracked since February 18, 2026 · last seen February 25, 2026 · updates as the daily brief publishes
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