A story about two very different machines shaping the future of AI. In late 2024, the AI hardware world witnessed a quiet but significant shift: Meta began training large portions of its Llama 3 and 4 models using Google’s TPU (Tensor Processing Unit) pods. For a company that famously buys hundreds of thousands of NVIDIA GPUs, this move was surprising. Why would Meta—an empire built on GPUs—suddenly embrace Google’s custom silicon? To understand this, you have to look at the

CAG shifts the focus from dynamic retrieval to offline precomputation. It exploits the KV caching mechanism of transformer-based LLMs. Here, intermediate activations (keys and values) from the attention layers are stored for reuse, speeding up inference. Key Components and Flow Preprocessing Phase : The knowledge base, which may include documents, knowledge bases, or database extracts, is fed into the LLM. This allows the model to compute and store KV caches. The result is a

Techniques for enhancing LLM’s The provided diagram compares two primary techniques for enhancing Large Language Models (LLMs) with external or domain-specific knowledge: Retrieval-Augmented Generation (RAG) in the top section and Fine-Tuning in the bottom section. It uses icons and flow arrows to illustrate the processes, with labels like "Gemini" (likely referring to Google's LLM) and various data sources. This setup is common in AI systems to improve response accuracy, rel

The Transformative Power of AI Artificial intelligence is evolving rapidly. Three main concepts—Large Language Models (LLMs), Generative AI, and Agentic AI—are changing how businesses, developers, and users interact with intelligent systems. At the center of this AI landscape are LLMs like GPT-4. These models are trained on large datasets to recognize patterns, solve complex problems, and communicate in ways that resemble human speech. They serve as the cognitive foundation,