게임에서 인공지능 거인으로 성장한 엔비디아, 이제 ChatGPT를 지원하는 방법 :: ChatGPT 정리

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How Nvidia Grew From Gaming To A.I. Giant, Now Powering ChatGPT
https://www.youtube.com/watch?v=d3L2uPuxOxU 

 

 

[ 요약 ]

세계 수백만 게이머들이 사용하는 NVIDIA의 G-Force 칩은 거의 30년간 게이머들에게 사랑받았다. 이제는 AI 기술 분야에서도 성과를 거두고 있으며, AI 스타트업에 대한 벤처 자본 투자는 급증하고 있다. AI 기술의 중요성을 이해하고 NVIDIA의 칩 기술에 대한 이해를 높이며, AI 기술 관련 투자에 대해 고민해보는 것이 좋겠다. 다만, 미국-중국 관계와 TSMC에 대한 잠재적인 영향이 있으므로 주의가 필요하다.

NVIDIA는 불확실한 새로운 시장에서 선두에 있을 때마다 파산 직전에 놓인 기업이다. 그러나 그들은 항상 이러한 위기를 극복해 왔다. NVIDIA는 1993년에 콘도에서 시작해 지금은 세계에서 가장 가치 있는 기업 중 하나이다. 그들은 GPU 칩을 만들어 CPU에 계산 능력을 더하는 일을 하고 있으며, 이는 대부분 게임 산업에서 사용된다. 그러나 그들의 창업자이자 CEO인 Jensen Huang은 대규모 기업들과 경쟁하는 작은 기업이었을 때 새로운 기술을 발명해야 했으며, 그리고 그것을 적용하는 방법을 찾아야 했다.

NVIDIA는 깊은 학습에 이상적인 병렬 처리를 사용해 컴퓨터가 스스로 학습하는 인공지능에 집중하게 되었다. 일부 산업에서 NVIDIA의 AI 기술은 이미 대단한 성과를 보이고 있으며, 이를 통해 더욱 효율적인 의약품 개발 및 유전자 연구, 그리고 미술 등 다양한 분야에서 유용하게 사용될 수 있다. 최근에는 대화형 AI 모델을 훈련하는 데 사용되는 NVIDIA A100 등의 새로운 제품이 출시되었으며, 이에 따라 대규모 언어 모델 및 실시간 번역, 텍스트-이미지 렌더링 등의 영역에서 더욱 많은 발전이 예상된다. NVIDIA의 발전에 대한 관심을 높이고, 이에 따른 투자 가능성을 고려해보는 것도 좋은 선택일 것이다.

인공지능이 어떻게 사용될 것인지에 대한 산업 내부의 보호 장치는 매우 중요하다. NVIDIA는 콘텐츠를 인증하는 방법을 찾고 있으며, 이를 통해 비디오, 텍스트 및 오디오가 실제로 만들어진 것인지 가상으로 만들어진 것인지 알 수 있다. 그러나 AI 시장의 더 넓은 걱정에도 불구하고, NVIDIA는 인공지능 붐의 중심에 있다. 미국이 지난 10월 중국으로의 최첨단 AI 칩 수출을 금지하는 규제를 도입하면서, NVIDIA의 A100을 포함하여 광범위한 새로운 규제가 소개되었다. NVIDIA는 중국 내 수익의 1/4을 차지하고 있다. 이로 인해 투자자들의 우려가 커지고 있다. NVIDIA는 규제를 준수하도록 노력하고 있으며, TSMC에 대한 의존도가 높아져 위험이 커졌다. 이러한 위험은 AMD, Qualcomm, Intel 등 모든 칩 제조 업체에 영향을 미치고 있다. 이에 대한 대응책으로 미국은 CHIPS 법안을 통해 520억 달러를 투자하여 미국 내에서 칩 제조를 유치하고 있다. TSMC는 400억 달러를 투자하여 미국 내에서 2개의 칩 제작 공장을 건설하고 있다. NVIDIA는 미국 내 칩 제조를 확대할 예정이다. AI 칩에 대한 수요는 계속해서 증가할 것이며, 경쟁 업체가 출현할 가능성도 있다. NVIDIA는 자율 주행 자동차 및 로봇 컴퓨터에도 집중하고 있다.

 

 

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[ 한글 전체 ]

전 세계 수억 명의 게이머가 플레이하는 게임입니다.
G-Force입니다.
이것이 바로 그 안에 들어 있는 칩입니다.
거의 30년 동안 NVIDIA의 칩은 게이머들의 선망의 대상이었습니다,
그래픽의 가능성을 개척하고 전체 시장을 장악하며
그래픽 처리 장치라는 용어를 처음 대중화한 이래로 30년 동안 게이머들의 사랑을 받아왔습니다.
이제 이 칩은 완전히 다른 것을 구동하고 있습니다.
ChatTVT는 매우 격렬한 대화를 시작했습니다.
아이폰 이후 가장 혁신적인 제품이라고 생각합니다.
AI 스타트업에 대한 벤처 캐피탈의 관심이 급증했습니다.
이 분야에서 일하는 우리 모두는 낙관적이었습니다.
언젠가는 더 많은 사람들이 이 기술의 중요성을 이해할 것이라고 낙관했습니다.
그리고 실제로 그런 일이 일어나기 시작했다는 것은 정말 흥분되는 일입니다.
ChatGPT와 같은 대규모 언어 모델의 기반이 되는 엔진으로서,
NVIDIA는 마침내 AI에 대한 투자에 대한 보상을 받고 있습니다,
다른 거대 칩 업체들이 미중 무역 긴장의 그늘에서 어려움을 겪고 있는 와중에도
칩 공급 부족으로 인한 수요 약화로 어려움을 겪고 있습니다.
하지만 캘리포니아에 본사를 둔 이 칩 설계업체는 거의 모든 칩을 대만 반도체 제조 회사
거의 모든 칩을 대만 반도체 제조사에 의존하고 있어 취약한 상태입니다.
가장 큰 위험은 미중 관계의 악화이며
그리고 TSMC에 대한 잠재적 영향입니다.
그게 바로 그것입니다.
제가 NVIDIA의 주주라면 이 문제 때문에 밤잠을 설치게 됩니다.
NVIDIA가 불확실한 상황의 최전선에서
불확실한 신흥 시장의 최전선에서 흔들리는 것은 이번이 처음이 아닙니다.
창립자이자 CEO인 젠슨 황이 취임한 이후에도 몇 차례 파산 직전까지 갔었습니다.
설립자 겸 CEO인 젠슨 황이 불가능해 보이는 벤처에 회사를 걸었을 때였습니다.
모든 회사는 실수를 저지르고 저도 실수를 많이 합니다.
특히 초창기에는 그런 실수 때문에 회사가 위험에 처하기도 합니다.
우리는 작고 아주 큰 회사들과 경쟁하고 있고
새로운 기술을 발명하려고 노력하고 있으니까요.
저희는 NVIDIA 실리콘밸리 본사에서 황과 만나
어떻게 이 새로운 기술을 개발했는지 알아보고
그리고 게임 그 이상을 지원하는 모든 방법에 대한 비하인드 스토리를 들어보았습니다.
이제 세계에서 가장 가치 있는 10대 기업 중 하나입니다,
NVIDIA는 실리콘 밸리에서 30년이 지난 지금까지도
30년이 지난 지금도 창업자가 여전히 회사를 이끌고 있습니다.
저는 AI 슈퍼컴퓨터가 처음 만들어졌을 때
AI 슈퍼컴퓨터를 처음 만들었습니다.
60세의 젠슨 황, 올해의 재테크 비즈니스 인물이자
그리고 2021년 타임이 선정한 가장 영향력 있는 인물 중 한 명입니다,
어렸을 때 대만에서 미국으로 이민을 와서
오리건 주립대와 스탠포드에서 공학을 공부했습니다.
90년대 초, 황은 데니즈에서 동료 엔지니어 크리스 말리코스키와 커티스 프리암을 만났습니다,
그들은 3D 그래픽이 탑재된 PC를 만들겠다는 꿈에 대해 이야기했습니다,
당시 쥬라기 공원과 같은 영화로 인기를 끌었던 3D 그래픽을 갖춘 PC를 구현하는 꿈에 대해 이야기했습니다.
30년 전으로 거슬러 올라가면 당시에는 PC 혁명이 막 시작되던 시기였죠,
컴퓨팅의 미래는 무엇이고 소프트웨어는 어떻게 실행되어야 하는지에 대해
그리고 소프트웨어는 어떻게 실행되어야 하는지에 대해 많은 논쟁이 있었습니다.
그리고 당연히 큰 진영이 있었습니다,
CPU나 범용 소프트웨어가 최선의 방법이라고 믿었습니다,
그리고 오랫동안 그렇게 믿었습니다.
하지만 저희는 가속화가 없으면 불가능할
가속화가 없이는 불가능하다고 생각했습니다.
친구들은 1993년 캘리포니아 프리몬트의 한 콘도에서 NVIDIA를 시작했습니다.
회사 이름은 다음 버전을 뜻하는 N.V.와 라틴어로 N.V.를 뜻하는 엔비디아에서 영감을 얻었습니다.
그들은 컴퓨팅 속도를 획기적으로 높여 모든 사람이 N.V.를 사용하게 되기를 바랐습니다.
매출의 80% 이상을 차지하는 주요 사업은 여전히 GPU입니다.
일반적으로 PC 마더보드에 꽂는 카드로 판매됩니다,
중앙 처리 장치인 CPU를 가속화하고 컴퓨팅 성능을 추가합니다,
중앙 처리 장치인 CPU를 가속하고 컴퓨팅 성능을 추가합니다.
당시에는 수십 개의 GPU 제조업체 중 하나였습니다.
실제로 살아남은 것은 그들과 AMD뿐이었습니다,
엔비디아는 소프트웨어 커뮤니티와 매우 잘 협력했기 때문입니다.
이것은 칩 사업이 아닙니다.
이것은 엔드 투 엔드로 사물을 파악하는 사업입니다.
하지만 처음에는 미래가 보장되지 않았습니다.
처음에는 솔직히 애플리케이션이 그리 많지 않았습니다.
하지만 우리는 현명하게도 홈런을 칠 수 있는 한 가지 조합을 선택했습니다.
바로 컴퓨터 그래픽이었고, 이를 비디오 게임에 적용했습니다.
이제 NVIDIA는 게임과 할리우드에 혁신을 가져온 것으로 유명합니다,
시각 효과를 빠르게 렌더링하는 것으로 유명합니다.
NVIDIA는 1997년에 최초의 고성능 그래픽 칩을 설계했습니다,
제조가 아닌 설계를 담당했는데, 이는 왕이 엔비디아를 멋진 칩 회사로 만들기 위해 노력했기 때문입니다,
칩을 만드는 데 드는 막대한 비용을 TSMC에 아웃소싱하여 자본 지출을 크게 줄였습니다.
우리 모두를 대신하여, 당신은 저의 영웅입니다.
감사합니다. 
TSMC의 선구적인 업적이 없었다면 오늘날의 엔비디아는 물론 다른 1,000여 개의 훌륭한 반도체 회사도 존재하지 않았을 것입니다,
TSMC의 선구적인 노력이 없었다면 말이죠.
1999년, 대부분의 직원을 해고하고 파산 직전까지 갔던 TSMC는
NVIDIA는 세계 최초의 공식 GPU라고 주장하는 GeForce 256을 출시했습니다.
이 그래픽 카드는 사용자 지정 음영 및 조명 효과를 구현할 수 있는 최초의 프로그래밍 가능 그래픽 카드였습니다.
2000년까지 NVIDIA는 Microsoft의 첫 번째 Xbox의 독점 그래픽 공급업체였습니다.
Microsoft의 Xbox는 바로 이 시기에 프로그래머블 셰이더라는 것을 발명했습니다,
오늘날 컴퓨터 그래픽이 어떻게 구현되는지를 정의했습니다.
NVIDIA는 1999년에 상장되었고, 팬데믹 기간 동안 수요가 급증할 때까지 주가는 거의 보합세를 유지했습니다.
2006년에는 CUDA라는 소프트웨어 툴킷을 출시하여 결국 AI 붐의 중심에 서게 됩니다.
이는 본질적으로 직렬 컴퓨팅에서 병렬 컴퓨팅으로 NVIDIA GPU의 작동 방식을 변경하는 컴퓨팅 플랫폼이자 프로그래밍 모델입니다.
병렬 컴퓨팅은 한 가지 작업을 훨씬 더 작은 컴퓨터로 동시에 처리하는 것입니다.
그렇죠? 이는 마치 군대에서 한 명의 거대한 병사가 일을 아주 잘하는 것과
한 번에 한 명씩 일을 처리하는 군대와 수천 명의 병사로 구성된 군대의 차이는
수천 명의 군인이 동시에 문제를 해결할 수 있는 군대를 갖는 것과는 다릅니다.
따라서 매우 다른 컴퓨팅 접근 방식입니다.
엔비디아의 큰 발걸음이 항상 올바른 방향이었던 것은 아닙니다.
2010년대 초에 테그라 프로세서 라인으로 스마트폰에 진출했다가 실패한 적이 있습니다.
스마트폰 시장이 자신들에게 적합하지 않다는 것을 금방 깨닫고 바로 스마트폰 시장에서 철수했습니다.
2020년에 NVIDIA는 오랫동안 기다려온 70억 달러 규모의 데이터센터 칩 회사인 멜라녹스 인수를 마무리했습니다.
하지만 작년에 NVIDIA는 심각한 규제 문제를 이유로 400억 달러 규모의 ARM 인수 입찰을 포기해야 했습니다.
ARM은 Apple에 아이폰과 아이패드용 ARM 아키텍처 라이선스를 제공하는 것으로 잘 알려진 주요 CPU 회사입니다,
Amazon, 그리고 여러 주요 자동차 제조업체에 라이선스를 제공하는 것으로 유명합니다.
몇 가지 어려움에도 불구하고 오늘날 NVIDIA는 26,000명의 직원과 캘리포니아 산타클라라에 새로 지어진 다각형 테마의 본사를 보유하고 있습니다,
그래픽 그 이상을 위한 수십억 개의 칩을 생산하고 있습니다.
데이터센터, 클라우드 컴퓨팅, 그리고 가장 눈에 띄는 AI를 생각해보세요.
우리는 모든 컴퓨터 회사에서 만든 모든 클라우드에 들어가 있으며, 어느 날 갑자기,
이전에는 불가능했던 새로운 애플리케이션이 사용자를 발견할 수 있습니다.
10여 년 전, 많은 사람들이 AI의 빅뱅 순간으로 꼽는 AlexNet의 엔진은 NVIDIA의 CUDA와 GPU였습니다.
놀랍도록 정확한 새로운 신경망으로, 2012년 저명한 이미지 인식 경연 대회에서 경쟁자들을 압도했습니다. 
실제와 같은 그래픽을 만드는 데 필요한 동일한 병렬 처리가 딥 러닝에도 이상적이라는 것이 밝혀졌습니다,
컴퓨터가 프로그래머의 코드에 의존하지 않고 스스로 학습하는 딥 러닝에도 이상적입니다.
유니티는 회사 전체가 딥 러닝에 투자하는 현명한 결정을 내렸습니다.
10여 년 전, 우리는 소프트웨어의 이러한 방식이 모든 것을 바꿀 수 있다는 것을 일찍이 알았습니다.
우리는 회사를 밑바닥부터 위쪽과 옆쪽까지 모두 바꿨습니다.
우리가 만든 모든 칩은 인공 지능에 초점을 맞췄습니다.
브라이언 카탄자라는 6년 전 NVIDIA의 딥 러닝 팀에 처음이자 유일한 직원이었습니다.
지금은 50명의 직원이 근무하고 있으며 계속 성장하고 있습니다.
10년 동안 월스트리트에서는 왜 이런 투자를 하는지에 대해 질문했습니다. 아무도 사용하지 않았기 때문입니다.
그리고 그들은 우리의 시가총액을 0달러로 평가했습니다.
그리고 2016년경이 되어서야 CUDA가 나온 지 10년이 지나서야
갑자기 사람들이 이 기술이 컴퓨터 프로그램을 작성하는 획기적인 방법이며
획기적인 속도 향상으로 인공 지능 분야에서 획기적인 결과를 가져올 수 있다는 것을 알게 되었습니다.
그렇다면 엔비디아의 AI가 실제로 적용되는 분야에는 어떤 것이 있을까요?
의료 분야가 가장 큰 분야입니다. 몇 주가 걸리던 신약 개발과 DNA 시퀀싱이 몇 시간 만에 훨씬 빨라진다고 생각해보세요.
저희는 게놈 시퀀싱 기술에서 기네스 세계 기록을 달성하여 실제로 환자를 진단하고
그리고 임상시험에 참여한 환자 중 한 명인 13세 소년에게 심장 이식을 시행했고, 그 결과 현재 건강하게 잘 지내고 있습니다,
그리고 간질 발작을 일으켰던 3개월 된 아기에게 항발작제를 처방할 수 있게 되었습니다.
그리고 건물 전체를 뒤덮은 라피크와 아돌즈의 작품과 같이 엔비디아 AI로 구동되는 예술 작품도 있습니다.
그리고 암호화폐가 호황을 누리기 시작했을 때, NVIDIA의 GPU는 디지털 화폐 채굴을 위한 탐나는 도구가 되었습니다.
권장되는 사용법은 아니지만, 이로 인해 문제가 생겼습니다,
암호화폐 채굴은 호황과 불황을 반복해왔기 때문입니다.
게임 카드가 품절되고 가격이 올라갔다가 암호화폐 채굴 붐이 붕괴하면
바로 게임 쪽에서 큰 폭락이 일어납니다.
엔비디아가 채굴 전용으로 만들어진 간소화된 GPU를 만들긴 했지만,
암호화폐 채굴자들이 게임용 GPU를 사들이면서 가격이 천정부지로 치솟는 것을 막지는 못했습니다.
그리고 이러한 공급 부족은 끝났지만, 엔비디아는 작년에 일부 게이머들에게 큰 충격을 주었습니다.
새로운 40 시리즈 GPU의 가격을 이전 세대보다 훨씬 높게 책정하여 일부 게이머들에게 큰 충격을 주었습니다.
지금은 공급이 너무 많아서 가장 최근에 보고된 분기별 게임 수익은 전년 대비 46% 감소했습니다.
하지만 NVIDIA는 AI 붐 덕분에 최근 실적 보고서에서 여전히 기대치를 상회하는 실적을 달성했습니다.
Microsoft와 Google과 같은 거대 기술 기업들이 데이터센터를 수천 대의 NVIDIA A100으로 채우면서,
채팅 GPT와 같은 대규모 언어 모델을 훈련하는 데 사용되는 엔진입니다.
배송 시에는 한 개씩 묶어서 배송하는 것이 아니라 8개씩 묶어서 배송합니다.
권장 가격이 거의 20만 달러에 달하는 NVIDIA의 DGX A100 서버 보드에는 8암페어 GPU가 탑재되어 있습니다.
8암페어 GPU가 함께 작동하여 채팅 GPT의 놀라울 정도로 빠르고 인간과 같은 응답을 가능하게 합니다.
저는 방대한 텍스트 데이터 세트를 학습하여 다양한 주제에 대한 텍스트를 이해하고 생성할 수 있습니다. 
제너레이티브 AI 경쟁에 뛰어든 기업들은 공개적으로 NVIDIA A100을 얼마나 많이 보유하고 있는지 자랑하고 있습니다.
예를 들어 Microsoft는 10,000개로 채팅 GPT를 훈련시켰습니다.
이 제품을 사용하고 컴퓨팅 용량을 추가하는 것은 매우 쉽습니다.
그리고 일단 컴퓨팅 용량을 추가하면 컴퓨팅 용량은 기본적으로 현재 밸리의 통화입니다.
암페어에서 다음 세대인 호퍼는 이미 출하를 시작했습니다.
제너레이티브 AI의 일부 용도는 실시간 번역과 즉각적인 텍스트-이미지 렌더링입니다.
하지만 이 기술은 놀랍도록 설득력 있고 일부에서는 위험하다고 말하는 딥 페이크 비디오, 텍스트 및 오디오의 배후에 있는 기술이기도 합니다.
사람들이 이러한 큰 두려움에 대비하거나 안전장치를 마련하기 위해 NVIDIA가 어떤 방식으로 보호하고 있나요?
네, AI가 어떻게 사용될 것인지에 대해 업계에서 구축하고 있는 안전장치가 매우 중요하다고 생각합니다.
저희는 동영상이 실제 세계에서 실제로 제작되었는지 또는 가상 세계에서 유사하게 제작되었는지 알 수 있도록 콘텐츠를 인증할 수 있는 방법을 찾고 있습니다.
또는 텍스트와 오디오의 경우 가상 세계에서 제작되었는지 확인할 수 있는 방법을 찾고 있습니다.
하지만 생성형 AI 붐의 중심에 있다고 해서 NVIDIA가 시장의 우려로부터 자유로울 수는 없습니다.
지난 10월, 미국은 중국에 대한 첨단 AI 칩 수출을 금지하는 새로운 규정을 전면적으로 도입했습니다,
중국으로의 수출을 금지하는 새로운 규정을 도입했습니다.
매출의 약 4분의 1이 중국 본토에서 발생합니다.
새로운 수출 규제에 대한 투자자들의 우려를 어떻게 진정시킬 수 있을까요?
NVIDIA의 기술은 수출 통제 대상입니다.
이는 우리가 만드는 기술의 중요성을 반영하는 것입니다.
우리가 가장 먼저 해야 할 일은 규정을 준수하는 것입니다.
모든 제품이 규정을 준수할 수 있도록 모든 제품을 재설계하기 위해
규정을 준수하면서도 중국 내 상업용 고객에게 서비스를 제공할 수 있도록 모든 제품을 재설계하느라 한 달 정도 정신이 없었습니다.
규제를 준수하는 부품으로 중국 고객에게 서비스를 제공할 수 있게 되어 기쁘게 고객을 지원하고 있습니다.
하지만 NVIDIA의 더 큰 지정학적 리스크는 대만의 TSMC에 대한 의존도입니다.
두 가지 문제가 있습니다.
첫째, 중국이 언젠가 대만 섬을 점령할 것인가?
둘째, TSMC를 대체할 수 있는 경쟁자가 있는가?
현재로서는 인텔이 공격적으로 노력하고 있습니다.
인텔의 목표는 2025년까지이며 두고 볼 일입니다.
그리고 이것은 비단 엔비디아만의 리스크가 아닙니다.
이는 AMD, 퀄컴, 심지어 인텔에게도 위험 요소입니다.
이것이 바로 지난 여름 미국이 칩스 법안을 통과시킨 큰 이유입니다,
칩 회사가 미국 내에서 생산하도록 장려하기 위해 520억 달러를 책정했습니다.
현재 TSMC는 애리조나에 400억 달러를 들여 두 개의 칩 제조 공장인 FABS를 건설하고 있습니다.
사실 TSMC는 정말 중요한 회사입니다.
그리고 전 세계에 이런 회사가 하나만 있는 것은 아닙니다.
다양성과 이중화에도 투자하는 것은 우리 자신과 그들에게 필수적입니다.
애리조나로 제조 시설을 이전할 예정인가요?
네, 물론입니다. 애리조나를 사용할 것입니다. 그래요
그리고 칩 부족 문제도 있습니다.
공급이 수요를 따라잡는 상황이 거의 끝나가면서
일부 유형의 칩은 가격 하락을 경험하고 있습니다.
하지만 챗봇 붐은 적어도 현재로서는 AI 칩에 대한 수요가 계속 증가하고 있음을 의미합니다.
이들에게 가장 큰 문제는 어떻게 하면 앞서 나갈 수 있을까요?
고객이 경쟁자가 될 수도 있기 때문입니다.
Microsoft는 내부적으로 이러한 것들을 설계할 수 있습니다.
아마존과 구글은 이미 내부적으로 이런 것들을 설계하고 있습니다.
Tesla와 Apple도 자체 맞춤형 칩을 설계하고 있습니다.
하지만 젠슨은 경쟁은 순선이라고 말합니다. 
전 세계가 데이터 센터에 필요로 하는 전력의 양은 점점 더 늘어날 것입니다.
최근 추세를 보면 알 수 있듯이 매우 빠르게 증가하고 있습니다.
그리고 이는 전 세계의 진정한 문제입니다.
AI와 채팅 GPT가 엔비디아에 많은 화제를 불러일으키고 있지만,
황의 유일한 관심사는 아닙니다.
이 모델을 컴퓨터에 넣으면 자율 주행 자동차가 됩니다.
그리고 그 컴퓨터를 여기에 넣으면 작은 로봇 컴퓨터가 됩니다.
아마존에서 사용하는 것과 같은 종류죠.
맞습니다.
아마존과 다른 기업들은 창고에서 로봇을 구동하고
거대한 공간의 디지털 트윈을 생성하고 시뮬레이션을 실행하여
시뮬레이션을 실행하여 매일 수백만 개의 패키지의 흐름을 최적화합니다.
엔비디아의 로봇 연구소에서 이와 같은 구동 장치는 한때 모바일 분야에서 실패작으로 평가받았던
테그라 칩으로 구동됩니다.
이제 이 칩은 세계 최대 규모의 전자상거래를 구동하는 데 사용됩니다.
2016년부터 2019년까지 Tesla Model 3에도 NVIDIA의 Tegra 칩이 사용되었습니다.
이제 Tesla는 자체 칩을 사용합니다.
하지만 NVIDIA는 메르세데스 벤츠와 같은 다른 자동차 제조업체를 위한 자율 주행 기술을 개발하고 있습니다.
그래서 저희는 이를 NVIDIA 드라이브라고 부릅니다.
그리고 기본적으로 NVIDIA는 확장 가능한 플랫폼을 구동합니다.
간단한 ADAS 보조 주행에 사용하든
긴급 제동 경고, 충돌 전 경고,
또는 크루즈 컨트롤을 위한 차선 유지부터 고무 택시까지
고무 택시까지, 모든 조건과 날씨에 상관없이 어디에서든 주행할 수 있습니다.
엔비디아는 또한 완전히 다른 분야에서 경쟁을 시도하고 있습니다,
자체 데이터 센터의 CPU인 Grace를 출시했습니다.
게임이라는 핵심 비즈니스에만 집중했으면 좋겠다는 게이머들에게 한 말씀 부탁드립니다.
물리 시뮬레이션에 대한 우리의 모든 연구가 아니었다면,
인공 지능에 대한 우리의 모든 연구가 아니었다면,
최근 지포스 RTX로 이룬 성과는 불가능했을 것입니다.
2018년에 출시된 RTX는 레이 트레이싱이라는 새로운 기술을 통해
레이 트레이싱이라는 새로운 기술이 탑재되었습니다.
컴퓨터 그래픽과 비디오 게임을 다음 단계로 끌어올리기 위해,
우리는 스스로를 재창조하고 혁신해야 했습니다,
기본적으로 빛의 경로를 시뮬레이션하고 모든 것을 제너레이티브 AI로 시뮬레이션해야 했습니다.
그래서 하나의 픽셀을 계산하고 나머지 7개의 픽셀을 AI로 상상합니다.
정말 놀랍습니다.
직소 퍼즐을 예로 들어 8개의 조각 중 하나를 주면
AI가 나머지 조각을 채웠다고 상상해 보세요.
레이 트레이싱은 현재 사이버펑크 2077, 포트나이트, 마인크래프트 등 300개에 가까운 게임에서 사용되고 있습니다.
또한 클라우드의 NVIDIA GeForce GPU를 사용하면 거의 모든 PC에서 1,500개 이상의 게임을 고퀄리티로 스트리밍할 수 있습니다.
이는 또한 시뮬레이션을 가능하게 하는 요소 중 하나입니다,
실제 상황에서 사물이 어떻게 작동할지 모델링하는 시뮬레이션을 가능하게 하는 요소이기도 합니다,
기후 예측이나 자율 주행 기술을 생각해보세요.
수백만 마일의 가상 도로를 기반으로 하는 자율 주행 기술을 예로 들 수 있습니다.
이 모든 것이 NVIDIA가 옴니버스라고 부르는 것의 일부입니다,
황은 이 모든 것이 엔비디아의 다음 큰 베팅이라고 말합니다.
현재 700개 이상의 고객이 이 기술을 사용하고 있습니다,
자동차 산업부터 물류 창고, 풍력 터빈 공장에 이르기까지 700개 이상의 고객이 있습니다.
그래서 저는 그 발전이 정말 기대됩니다.
이 기술은 아마도 엔비디아의 모든 기술이 집약된 가장 큰 컨테이너일 것입니다,
컴퓨터 그래픽, 인공 지능, 로봇 공학, 물리 시뮬레이션이
이 모든 것이 하나로 합쳐져 큰 기대를 걸고 있습니다. 

 

 

SMALL

[ English Summary ]

Used by millions of gamers around the world, NVIDIA's G-Force chips have been a favorite of gamers for nearly 30 years. Now they're making waves in AI technology, and venture capital investment in AI startups is skyrocketing. If you understand the importance of AI technology and have a good understanding of NVIDIA's chip technology, you may want to consider investing in AI technology. However, caution is warranted due to US-China relations and the potential impact on TSMC.

NVIDIA is a company that has been on the brink of bankruptcy every time it has been at the forefront of an uncertain new market. But they've always come through. NVIDIA started in a condo in 1993 and is now one of the most valuable companies in the world. They make GPU chips to add computing power to CPUs, which are mostly used in the gaming industry. But when their founder and CEO, Jensen Huang, was a small company competing with larger ones, he had to invent new technologies and find ways to apply them.

NVIDIA came to focus on artificial intelligence, where computers teach themselves using parallel processing, which is ideal for deep learning. In some industries, NVIDIA's AI technology is already showing great promise, enabling more efficient drug development, genetic research, and art. New products such as the NVIDIA A100, which is used to train conversational AI models, have recently been released, and more advances are expected in areas such as large-scale language models, real-time translation, and text-to-image rendering. It's a good idea to keep an eye on NVIDIA's progress and consider investing in it.

Safeguards within the industry about how AI is used are critical. NVIDIA is looking at ways to authenticate content, so we know whether video, text, and audio are real or virtual. But despite the wider worries in the AI market, NVIDIA is at the center of the AI boom. When the U.S. introduced regulations in October banning the export of cutting-edge AI chips to China, it introduced a wide range of new regulations, including NVIDIA's A100. NVIDIA makes a quarter of its revenue in China. This has raised investor concerns. NVIDIA is working to comply with the regulations, and its increased reliance on TSMC increases the risk. These risks affect all chipmakers, including AMD, Qualcomm, and Intel. In response, the U.S. is investing $52 billion to bring chip manufacturing back to the U.S. through the CHIPS Act. TSMC is investing $40 billion to build two chip fabs in the US. NVIDIA plans to expand chip manufacturing in the U.S. The demand for AI chips will continue to grow, and competitors are likely to emerge. NVIDIA is also focusing on self-driving cars and robotic computers.

[ English Full Text ]

This is what hundreds of millions of gamers in the world plays on.
It's a G-Force.
This is the chip that's inside.
For nearly 30 years, NVIDIA's chips have been coveted by gamers,
shaping what's possible in graphics and dominating the entire market
since it first popularized the term graphics processing unit with the G-Force 256.
Now, its chips are powering something entirely different.
ChatTVT has started a very intense conversation.
Thinks it's the most revolutionary thing since the iPhone.
Venture capital interest in AI startups has skyrocketed.
All of us working in this field have been optimistic
that at some point the broader world would understand the importance of this technology.
And it's actually really exciting that that's starting to happen.
As the engine behind large language models like ChatGPT,
NVIDIA is finally reaping rewards for its investment in AI,
even as other chip giants suffer in the shadow of U.S.-China trade tensions
and an ease in the chip shortage that's weakened demand.
But the California-based chip designer relies on Taiwan semiconductor manufacturing company
to make nearly all its chips, leaving it vulnerable.
The biggest risk is really kind of U.S.-China relations
and the potential impact to TSMC.
That's that.
If I'm a shareholder in NVIDIA, that's really the only thing that keeps me up at night.
This isn't the first time NVIDIA has found itself teetering
on the leading edge of an uncertain emerging market.
It's near bankruptcy a handful of times in its history
when founder and CEO Jensen Huang bet the company on impossible-seeming ventures.
Every company makes mistakes, and I make a lot of them.
And, you know, some of them puts the company in peril, especially in the beginning.
Because we're small and we're up against very, very large companies
and we're trying to invent this brand-new technology.
We sat down with Huang at NVIDIA's Silicon Valley headquarters
to find out how he pulled off this latest reinvention
and got a behind-the-scenes look at all the ways it powers far more than just gaming.
Now one of the world's top 10 most valuable companies,
NVIDIA is one of the rare Silicon Valley giants
that 30 years in still has its founder at the helm.
I delivered the first one of these inside an AI supercomputer
to open AI when it was first created.
60-year-old Jensen Huang, a fortune-business person of the year
and one of Time's most influential people in 2021,
immigrated to the U.S. from Taiwan as a kid
and studied engineering at Oregon State and Stanford.
In the early 90s, Huang met fellow engineers Chris Malikowski and Curtis Priam at Denny's,
where they talked about dreams of enabling PCs with 3D graphics,
the kind made popular by movies like Jurassic Park at the time.
If you go back 30 years, at the time the PC revolution was just starting,
and there was quite a bit of debate about what is the future of computing
and how should software be run.
And there was a large camp, and rightfully so,
that believed that CPU or general-purpose software was the best way to go,
and it was the best way to go for a long time.
We felt, however, that there was a class of applications
that wouldn't be possible without acceleration.
The friends launched NVIDIA out of a condo in Fremont, California in 1993.
The name was inspired by N.V. for next version and NVIDIA, the Latin word for N.V.
They hoped to speed up computing so much, everyone would be green with N.V.
At more than 80% of revenue, its primary business remains GPUs.
Typically sold as cards that plug into a PC's motherboard,
they accelerate, add computing power, to central processing units, CPUs,
from companies like AMD and Intel.
You know, they were one among tens of GPU makers at that time.
They are the only ones, and them and AMD actually, who really survived,
because NVIDIA worked very well with the software community.
This is not a chip business.
This is a business of figuring out things end-to-end.
But at the start, its future was far from guaranteed.
In the beginning, there weren't that many applications for it, frankly.
And we smartly chose one particular combination that was a home run.
It was computer graphics, and we applied it to video games.
Now, NVIDIA is known for revolutionizing gaming and Hollywood,
with rapid rendering of visual effects.
NVIDIA designed its first high-performance graphics chip in 1997,
designed, not manufactured, because Wang was committed to making NVIDIA a fabulous chip company,
keeping capital expenditure way down by outsourcing the extraordinary expense of making the chips to TSMC.
On behalf of all of us, you're my hero.
Thank you.
NVIDIA today wouldn't be here, and nor the other 1,000 fabulous semiconductor companies wouldn't be here,
if not for the pioneering work that TSMC did.
In 1999, after laying off the majority of workers and nearly going bankrupt to do it,
NVIDIA released what it claims was the world's first official GPU, the GeForce 256.
It was the first programmable graphics card that allowed custom shading and lighting effects.
By 2000, NVIDIA was the exclusive graphics provider for Microsoft's first Xbox.
Microsoft in the Xbox happened at exactly the time that we invented this thing called a programmable shader,
and it defines how computer graphics is done today.
NVIDIA went public in 1999, and its stock stayed largely flat until demand went through the roof during the pandemic.
In 2006, it released a software toolkit called CUDA that would eventually propel it to the center of the AI boom.
It's essentially a computing platform and programming model that changes how NVIDIA GPUs work, from serial to parallel compute.
Parallel computing is, let me take a task, and attack it all at the same time using much smaller machines.
Right? So it's the difference between having an army where you have one giant soldier
who is able to do things very well, but one at a time, versus an army of thousands of soldiers
who are able to take that problem and do it in parallel.
So it's a very different computing approach.
NVIDIA's big steps haven't always been in the right direction.
In the early 2010s, it made unsuccessful moves into smartphones with its Tegra line of processors.
You know, they quickly realized that the smartphone market wasn't for them, so they exited right from that.
In 2020, NVIDIA closed a long-awaited $7 billion deal to acquire data center chip company Melanox.
But just last year, NVIDIA had to abandon a $40 billion bid to acquire ARM, citing significant regulatory challenges.
ARM is a major CPU company known for licensing its signature ARM architecture to Apple for iPhones and iPads,
Amazon for Kindles, and many major car makers.
Despite some setbacks, today NVIDIA has 26,000 employees, a newly built polygon-themed headquarters in Santa Clara, California,
and billions of chips used for far more than just graphics.
Think data centers, cloud computing, and most prominently, AI.
We're in every cloud made by every computer company, and then all of a sudden, one day,
a new application that wasn't possible before it discovers you.
More than a decade ago, NVIDIA's CUDA and GPUs were the engine behind AlexNet, what many consider AI's big bang moment.
It was a new, incredibly accurate neural network that obliterated the competition during a prominent image recognition contest in 2012.
Turns out, the same parallel processing needed to create lifelike graphics is also ideal for deep learning,
where a computer learns by itself rather than relying on a programmer's code.
We had the good wisdom to go put the whole company behind it.
We saw early on, about a decade or so ago, that this way of doing software could change everything.
We changed the company from the bottom all the way to the top and sideways.
Every chip that we made was focused on artificial intelligence.
Brian Katanzara was the first and only employee on NVIDIA's deep learning team six years ago.
Now it's 50 people and growing.
For 10 years, Wall Street asked NVIDIA, why are you making this investment? No one's using it.
And they valued it at $0 in our market cap.
And it wasn't until around 2016 that, you know, 10 years after CUDA came out,
that all of a sudden people understood this is a dramatically different way of writing computer programs
and it has transformational speed-ups that then yield breakthrough results in artificial intelligence.
So what are some real-world applications for NVIDIA's AI?
Healthcare is one big area. Think far faster drug discovery and DNA sequencing that takes hours instead of weeks.
We were able to achieve the Guinness World Record in a genomic sequencing technique to actually diagnose these patients
and administer one of the patients in the trial to have a heart transplant, a 13-year-old boy who's thriving today as a result,
and then also a three-month-old baby that was having epileptic seizures and to be able to prescribe an anti-seizure medication.
And then there's art powered by NVIDIA AI, like Rafique and Adoles creations that cover entire buildings.
And when crypto started to boom, NVIDIA's GPUs became the coveted tool for mining the digital currency.
Which is not really a recommended usage, but that has created, you know, problems because, you know,
crypto mining has been a boom or bust cycle.
So gaming cards, right, go out of stock, prices get paid up, and then when the crypto mining boom collapses,
then there's a big crash right on the gaming side.
Although NVIDIA did create a simplified GPU made just for mining,
it didn't stop crypto miners from buying up gaming GPUs, sending prices through the roof.
And although that shortage is over, NVIDIA caused major sticker shock among some gamers last year
by pricing its new 40-series GPUs far higher than the previous generation.
Now there's too much supply, and the most recently reported quarterly gaming revenue was down 46% from the year before.
But NVIDIA still beat expectations in its most recent earnings report thanks to the AI boom.
As tech giants like Microsoft and Google fill their data centers with thousands of NVIDIA A100s,
the engines used to train large language models like chat GPT.
When we ship them, we don't ship them in packs of one, we ship them in packs of eight.
With a suggested price of nearly $200,000, NVIDIA's DGX A100 server board has eight ampere GPUs
that work together to enable things like the insanely fast and uncannily human-like responses of chat GPT.
I have been trained on a massive data set of text, which allows me to understand and generate text on a wide range of topics.
Companies scrambling to compete in generative AI are publicly boasting about how many NVIDIA A100s they have.
Microsoft, for example, trained chat GPT with 10,000.
It's very easy to use their products and add more computing capacity.
And once you add that computing capacity, computing capacity is basically the currency of the valley right now.
In the next generation up from ampere, Hopper has already started to ship.
Some uses for generative AI are real-time translation and instant text-to-image renderings.
But this is also the tech behind eerily convincing and some say dangerous deep fake videos, text and audio.
Are there any ways that NVIDIA is sort of protecting against some of these bigger fears that people have or building in safeguards?
Yes, I think the safeguards that we're building as an industry about how AI is going to be used are extraordinarily important.
We're trying to find ways of authenticating content so that we can know if a video was actually created in the real world
or virtually, similarly, for text and audio.
But being at the center of the generative AI boom doesn't make NVIDIA immune to wider market concerns.
In October, the US introduced sweeping new rules that banned exports of leading-edge AI chips to China,
including NVIDIA's A100.
About a quarter of your revenue comes from mainland China.
How do you calm investor fears over the new export controls?
Well, NVIDIA's technology is export control.
It's a reflection of the importance of the technology that we make.
The first thing that we have to do is comply with the regulations.
And it was a turbulent month or so as the company went upside down to re-engineer all of our products
so that it's compliant with the regulation and yet still be able to serve the commercial customers that we have in China.
We're able to serve our customers in China with the regulated parts and delightfully support them.
But perhaps an even bigger geopolitical risk for NVIDIA is its dependence on TSMC in Taiwan.
There's two issues.
One, will China take over the island of Taiwan at some point?
And two, is there a viable competitor to TSMC?
And as of right now, you know, Intel is trying aggressively to get there.
And you know, their goal is by 2025 and we will see.
And this is not just an NVIDIA risk.
This is a risk for AMD, for Qualcomm, even for Intel.
This is a big reason why the U.S. passed the CHIPS Act last summer,
which sets aside $52 billion to incentivize chip companies to manufacture on U.S. soil.
Now, TSMC is spending $40 billion to build two chip fabrication plants, FABS, in Arizona.
The fact of the matter is TSMC is a really important company.
And the world doesn't have more than one of them.
It is imperative upon ourselves and them for them to also invest in diversity and redundancy.
Will you be moving any of your manufacturing to Arizona?
Oh, absolutely. We'll use Arizona. Yeah.
And then there's the CHIPS shortage.
As it largely comes to a close and supply catches up with demand,
some types of chips are experiencing a price slump.
But for NVIDIA, the chatbot boom means demand for its AI chips continues to grow, at least for now.
See, the biggest question for them is how do they stay ahead?
Because their customers can be their competitors also.
You know, Microsoft can try and design these things internally.
Amazon and Google are already designing these things internally.
Tesla and Apple are designing their own custom chips, too.
But Jensen says competition is a net good.
The amount of power that the world needs in the data center will grow.
And you can see in the recent trends, it's growing very quickly.
And that's a real issue for the world.
While AI and chat GPT have been generating lots of buzz for NVIDIA,
it's far from Huang's only focus.
And we take the model and we put it into this computer and that's a self-driving car.
And we take that computer and we put it into here and that's a little robot computer.
Like the kind that's used with Amazon.
That's right.
Amazon and others use NVIDIA to power robots in their warehouses
and to create digital twins of the massive spaces and run simulation
to optimize the flow of millions of packages each day.
Driving units like these in NVIDIA's robotics lab are powered by the Tegra chips
that were once a flop in mobile phones.
Now they're used to power the world's biggest e-commerce operations.
NVIDIA's Tegra chips were also used in Tesla Model 3's from 2016 to 2019.
Now Tesla uses its own chips.
But NVIDIA is making autonomous driving tech for other car makers like Mercedes-Benz.
So we call it NVIDIA Drive.
And basically NVIDIA drives a scalable platform.
Whether you want to use it for simple ADAS assisted driving
for your emergency braking warning, pre-collision warning,
or just holding the lane for cruise control all the way up to a rubber taxi
where it is doing everything, driving anywhere in any condition, any type of weather.
NVIDIA is also trying to compete in a totally different arena,
releasing its own data center's CPU, Grace.
What do you say to gamers who wish you had kept focus entirely on the core business of gaming?
Well, if not for all of our work in physics simulation,
if not for all of our research in artificial intelligence,
what we did recently with GeForce RTX would not have been possible.
Released in 2018, RTX is NVIDIA's next big move in graphics
with a new technology called ray tracing.
For us to take computer graphics and video games to the next level,
we had to reinvent and disrupt ourselves,
basically simulating the pathways of light and simulate everything with generative AI.
And so we compute one pixel and we imagine with AI the other seven.
It's really quite amazing.
Imagine a jigsaw puzzle and we gave you one out of eight pieces
and somehow the AI filled in the rest.
Ray tracing is used in nearly 300 games now, like Cyberpunk 2077, Fortnite, and Minecraft.
And NVIDIA GeForce GPUs in the cloud allow full-quality streaming of 1,500-plus games to nearly any PC.
It's also part of what enables simulations,
modeling of how objects would behave in real-world situations,
think climate forecasting, or autonomous drive tech
that's informed by millions of miles of virtual roads.
It's all part of what NVIDIA calls the omniverse,
what Huang points to as the company's next big bet.
We have 700-plus customers who are trying it now,
from car industry to logistics warehouse to wind turbine plants.
And so I'm really excited about the progress there.
It represents probably the single greatest container of all of NVIDIA's technology,
computer graphics, artificial intelligence, robotics, and physics simulation,
all into one I have great hopes for.