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AUTONOMOUS VEHICLES | ARTIFICIAL INTELLIGENCE
Nvidia GTC 2024: Why Nvidia Dominates AI

By Egil Juliussen

vidia’s Developer Conference has been a major few years. Each GPU chip has functionality that improves performance
event in the GPU industry for more than a for specific application segments—initially for PC graphics and game
consoles and recently for autonomous-vehicle functions and AI-centric
decade. With the company’s GPUs having software and systems.
Nbecome the leading AI chips over the past two Thus far, the company has introduced a total of 16 GPU microar-
years, the 2024 Nvidia GPU Technology Conference (GTC) chitectures, all named after famous inventors and scientists across
multiple disciplines. The first was the Fahrenheit architecture,
attracted not just the traditional, engineering-centric released in 1995. Nvidia’s latest GPU microarchitecture, Blackwell,
attendees but many Wall Street analysts as well. debuted in March 2024.
Table 1 shows how Nvidia’s GPUs have improved since 1995. The
The AI GPU trend accelerated Nvidia’s market value and revenue table includes all 16 GPU microarchitectures, with a focus on the
tremendously in the past two years, and it is now one of the three most latest versions. The first GPU microarchitecture used in the automo-
valuable companies in the world based on market capitalization. tive industry, in 2015, was Maxwell, Nvidia’s ninth GPU architecture,
Nvidia’s revenue grew from US$10 billion in 2018 to US$61 billion in released in 2014.
2023, a spike that explains the influx of Wall Street analysts at The first Fahrenheit-based GPU had 1 million transistors and was
GTC 2024. based on a 500-nm manufacturing process technology. By 1999, the
The growth of generative AI since the launch of ChatGPT has technology enabled GPU chips with 15 million transistors.
accelerated the importance of GTC presentations and product Tesla, the sixth Nvidia GPU microarchitecture, was capable of
announcements. This article summarizes Nvidia’s key announcements 210 million transistors in 2006 and 1.4 billion transistors in 2010.
at GTC 2024, offering a perspective that considers Nvidia’s history, During its four-year manufacturing run, the Tesla GPU chip shrank
notably the evolution of its GPU chip architecture and technology; the from 90 nm to 40 nm, even as the transistor count grew 6.7×.
importance of Nvidia’s CUDA software technology; and why and how The first Nvidia GPU microarchitecture to offer more than 1 billion
Nvidia’s GPUs became a key technology for the AI industry. transistors at introduction was Maxwell, which provided 2.9 billion
transistors in 2014. Maxwell was also the first Nvidia GPU to be used
HISTORY OF NVIDIA’S GPU ARCHITECTURE in automotive applications. Each new generation added more com-
The microarchitectures that Nvidia designs for each GPU generation puting power through computing accelerators for specific calculation
offer significant improvements with each new version. Multiple GPU in parallel or simultaneously, which increased the performance of
chips are designed based on each microarchitecture over a period of a GPU-based chips.

Table 1: Evolution of Nvidia’s GPU Microarchitecture
Microarchitecture Introduction Microarchitecture Transistor Manufacturing Fab Partner
Name Year Generation Range Technology
Fahrenheit 1995 1st 1M to 15M 250 nm to 500 nm TSMC
Celsius 1999 2nd 17M to 25M 150 nm to 220 nm TSMC
Kelvin 2001 3rd 29M to 63M 150 nm TSMC
Rankine 2003 4th 45M to 135M 130 nm to 150 nm TSMC, IBM
Curie 2004 5th 75M to 302M 130 nm TSMC
Tesla 2006 6th 219M to 1,400M 40 nm to 90 nm TSMC
Fermi 2010 7th 260M to 3,000M 40 nm TSMC
Kepler 2013 8th 292M to 7,080M 28 nm TSMC
Maxwell 2014 9th 2.94B to 8B 28 nm TSMC
Pascal 2016 10th 1.8B to 12B 16 nm TSMC
Volta 2017 11th 21.1B 12 nm TSMC
Turing 2018 12th 4.7B to 18.6B 12 nm TSMC
Ampere 2020 13th 8.7B to 28.3B 7 nm TSMC
Hopper 2022 14th 80B 4 nm TSMC
Ada Lovelace 2022 15th 18.9B to 76.5B 4 nm TSMC
Blackwell 2024 16th 208B 4 nm TSMC

(Source: VSI Labs, April 2024)

JUNE 2024 | www.eetimes.eu