NVIDIA

Let’s first establish the date. ChatGPT’s public research preview version was released on November 30, 2022, not 2023. [1]

After this point, NVIDIA’s data center GPU main roadmap is quite clear: The conclusion of Ampere, followed by Hopper taking over. Hopper focused on expanding VRAM capacity and refresh rate, while Blackwell will shift its focus from “single-card dense compute power” toward “inference throughput, power consumption, and system-level interconnection.” The China-specific versions represent a different story: A800, H800, and H20 are fundamentally compliance versions created under US export control constraints, and therefore cannot be viewed using the same metrics as the global flagship line.

While browsing the forum this time, what struck me most wasn’t which company released another leaderboard, but a very basic statement: “Not enough VRAM; no matter how large the parameters are, it’s useless.”

Previously, I always understood “slow model” as a computational power issue. However, the more I read, the clearer it became that often, the problem isn’t that the GPU can’t compute it, but rather that the data cannot reside in the right place. Just by changing the memory path, the token speed doesn’t just slow down; it drops drastically.

• Analysis of NVIDIA’s “Play” Worth a Billion Investment The global capital market is witnessing an unprecedented wave of centralization in 2025, centered around artificial intelligence (AI). This narrative not only reshapes the tech industry’s landscape but also exacerbates wealth inequality on Wall Street. The former “Magnificent Seven” no longer adequately describes today’s dynamics; the market is now dominated by a handful of super winners.

This article will delve into three key questions: