Nvidia Hopper, every mention
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tap a year for its mentions
every year anyone Justin Johnson 2Eiso Kant 2Thomas Sohmers 1Shawn Wang 1Philip Kiely 1Micah Hill-Smith 1
Verbatim, from the transcripts: the passages where Nvidia Hopper comes up
Next 100x in AI: Inference, Networking, & Self-Optimizing Models — Philip Kiely & Ali Taha, Baseten
- ▶ 37:12 Philip Kiely So like on GLM 5.2, um, if you want to get unquantized, uh, perhaps on hoppers even, um, and you're just using an off the shelf inference engine with no particular optimizations, no, no speculator, um, nothing, nothing extra around like KV…
The AI Frontier: from open weights to open research — Eiso Kant, Poolside AI
Artificial Analysis: The Independent LLM Analysis House — with George Cameron and Micah Hill-Smith
- ▶ 1:03:02 Micah Hill-Smith When you run all of that, for especially big sparse models, you can get a lot better than two or three eggs gain going from hopper to blackwell generation to video.
After LLMs: Spatial Intelligence and World Models — Fei-Fei Li & Justin Johnson, World Labs
- ▶ 13:01 Justin Johnson Like, if you look at, if you look at the numbers, like, even going from Hopper to Blackwell, like, the performance per watt is about the same. 2 times in the scene
⚡️Accelerators @ 3x NVIDIA H200 perf, Made in the USA - Thomas Sohmers + Mitesh Agrawal, Positron AI
- ▶ 14:53 Thomas Sohmers Percentage of theoretical memory bandwidth actually has gotten worse generation over generation, and we'll see exactly where Blackwell ends up, but all indications are, even though they, you know, more than doubled the theoretical memory…
2024 Year in Review: The Big Scaling Debate, the Four Wars of AI, Top Themes and the Rise of Agents
- ▶ 1:11:22 Shawn Wang For him to make that statement, I think it's a, it's another indication that the transition from the H to the B series is going to go very well.