The Age of AI Reasoning: How NVIDIA is Building the Crown of the Next Wave of Computing Power?

SnapshotLaborer

2026-03-17 02:00:01

Abstract generation in progress

In the GPT-3 era, a model with 175 billion parameters was already considered enormous; today, trillion-parameter hybrid expert models have become the norm. The biggest pain point in the AI industry—latency during inference—has become the next industry challenge for NVIDIA to overcome.

The GPU’s “throughput-first” design philosophy is facing serious challenges in real-time interactive inference scenarios. However, when handling individual user requests with “small batch, serial generation” tasks, its reliance on high-bandwidth memory (HBM) architecture leads to frequent data transfers, resulting in significant latency and power consumption waste.

The emergence of LPU (Light Processing Unit) is precisely aimed at solving this fundamental architectural mismatch.

Breaking through the noise of the complex industry chain, which core links should we pay attention to in the inference era?

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