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1:53
🎮 GPU: The AI Math Engine! 🧠 Scenario: GPUs aren't just for gaming visuals. They are the primary engines for simulations, machine learning, and LLMs. Challenge: - Sequential vs. Parallel: CPUs handle tasks one-by-one; AI requires massive simultaneous math. - Math Load: Deep learning relies on trillions of matrix multiplications. Solution: Massive Parallelism 🎯 - Core Density: Thousands of small cores execute math in parallel. - Throughput: Optimized for high-speed data crunching, not complex
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Gemma 4 (specifically its edge-optimized E2B and E4B variants) running fully offline on an iPhone via apps like Locally AI or Google AI Edge Gallery. Download the ~1.5 GB quantized model, all inference happens on-device using Apple Neural Engine.
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CPU vs GPU vs TPU vs NPU vs LPU, explained visually:5 hardware architectures power AI today.Each one makes a fundamentally different tradeoff between flexibility, parallelism, and memory access.> CPUIt is built for general-purpose computing. A few powerful cores handle complex logic, branching, and system-level tasks.It has deep cache hierarchies and off-chip main memory (DRAM). It's great for operating systems, databases, and decision-heavy code, but not that great for repetitive math like matr
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