{"id":1267,"date":"2026-01-08T10:05:22","date_gmt":"2026-01-08T01:05:22","guid":{"rendered":"https:\/\/rtlearner.com\/?p=1267"},"modified":"2026-01-08T10:05:23","modified_gmt":"2026-01-08T01:05:23","slug":"ai-architecture-4-training-vs-inference","status":"publish","type":"post","link":"https:\/\/rtlearner.com\/en\/ai-architecture-4-training-vs-inference\/","title":{"rendered":"AI Architecture 4. Training vs. Inference"},"content":{"rendered":"

In previous posts, we learned that MAC operations, Memory Hierarchies, and Parallel Processing (SIMD) form the foundation of deep learning hardware. Now, we stand at the biggest crossroad in the AI semiconductor market: Training vs. Inference. Beast-like GPUs like NVIDIA's H100 handle both. However, recent NPUs in smartphones or autonomous driving chips focus solely on 'Inference.'<\/p>\n\n\n\n

To a software engineer, inference is just a single line of code switching to model.eval() and turning off gradients with torch.no_grad().<\/p>\n\n\n\n

But to us, Hardware Architects, \"removing the training function\" implies a massive 'diet' that fundamentally overturns the chip's design philosophy. In this post, we will analyze the dramatic changes and optimizations that occur inside the hardware when we put down the heavy burden of Backpropagation.<\/p>\n\n\n