#黃仁勳 驚喜為 #美超微 #梁見後 #COMPUTEX 主題演講站台|完整精華
Summary
TLDR在这段视频中,Nvidia的CEO Jensen Huang分享了他对人工智能和加速计算的远见。他强调了数据量指数级增长与CPU扩展速度放缓之间的矛盾,以及加速计算如何帮助释放数据中心的浪费能量,并加速每个应用。Jensen还介绍了生成式AI(generative AI)的概念,它将影响全球所有数据中心,推动其现代化。此外,他还提到了液冷技术在降低功耗方面的作用,以及如何通过提高吞吐量、利用率和启动速度来增加收入。最后,他强调了安全性、技术进步和良好政策的重要性,以确保AI的健康发展。
Takeaways
- 🧠 AI正在改变最小化计算,因为数据量呈指数级增长,而CPU的扩展速度已经放缓多年。
- 🌿 绿色计算(Green Computing)即能效计算,是Nvidia所关注的,旨在减少数据中心的能源浪费。
- 🚀 加速计算(Accelerated Computing)时代已经到来,它将释放数据中心的潜能,提高应用和数据中心的效率。
- 🤖 生成式AI(Generative AI)是一种新兴的计算方式,它涉及文本、图像、视频的生成,将影响全球所有的数据中心。
- 💰 数据中心需要现代化改造以适应生成式AI,这将带来巨大的需求和市场潜力。
- 🔧 Nvidia和Supermicro合作,提供多种新产品和解决方案,以支持加速计算和数据中心的现代化。
- 💡 液冷技术(DLC)正在被用于降低数据中心的功耗,提高AI芯片的制造能力。
- 🔗 高速互连技术(如NVLink)使得GPU和CPU之间能够高效地传输数据,这对于AI训练和推理至关重要。
- 🛠️ Nvidia的软件堆栈,包括CUDA和网络计算,为高性能计算提供了强大的支持。
- 🔐 安全技术的发展同样重要,需要为AI制定安全政策和实践,以确保其可靠性和安全性。
Q & A
Nvidia的CEO Jensen在演讲中提到了哪些关于AI和计算的变革?
-Jensen在演讲中提到了加速计算(accelerated computing)和绿色计算(Green Computing)的到来,强调了由于数据量指数级增长和CPU扩展放缓,加速计算能够释放数据中心中浪费的能源和成本。他还提到了生成式AI(generative AI)的崛起,这将影响全球所有的数据中心。
Jensen提到了加速计算能带来哪些好处?
-加速计算能够节省数据中心中浪费的能源和成本,提高应用和数据中心的效率。通过加速计算,可以释放之前浪费的能源,用于新的目的,比如加速每个应用和数据中心。
什么是生成式AI,它将如何影响数据中心?
-生成式AI是一种能够生成文本、图像、视频等内容的人工智能技术。Jensen认为,生成式AI的转变将影响世界上每一个数据中心,因为到2030年,全球数据的价值可能达到3万亿美元,这要求我们现代化所有的数据中心,以适应这些先进的系统。
Jensen如何描述Super Micro和Nvidia的合作关系?
-Jensen将Super Micro团队描述为准备好接受订单的合作伙伴,他们准备好提供所需的产品和服务,以帮助现代化数据中心。他还开玩笑说自己是Super Micro的最佳销售人员,并且他们之间有良好的合作关系。
在演讲中提到的数据中心现代化涉及到哪些方面?
-数据中心现代化涉及到采用加速计算和生成式AI等先进技术,以及采用液冷系统(DLC)来降低功耗,提高性能和成本效率。此外,还需要软件的兼容性和整个系统的整合,以确保快速启动、高利用率和高吞吐量。
为什么Jensen强调了数据中心的能源效率?
-Jensen强调能源效率是因为在加速计算中,能源效率直接转化为更高的性能。通过节省能源,可以将这些能量用于更多的计算任务,从而提高数据中心的整体性能和产出。
Nvidia在数据中心现代化中扮演了什么角色?
-Nvidia在数据中心现代化中扮演了关键角色,提供了加速计算平台和生成式AI技术。Nvidia的GPU和其他技术是构建现代AI工厂的基础,这些AI工厂能够直接产生收入。
Jensen提到了哪些Nvidia的新产品或技术?
-Jensen提到了Nvidia的多个新产品和技术,包括新的GPU服务器、液冷系统(DLC)以及与Super Micro合作的各种配置,如Blackwell GPU和Grace CPU的结合使用。
在演讲中,Jensen如何描述AI的未来和其在不同行业中的应用?
-Jensen认为AI的未来非常重要,他提到生成式AI能够嵌入智能,并且可以在许多不同的行业中使用。他强调了AI的实用性,以及如何通过不同的技术来提高AI的安全性和功能性。
Jensen在演讲中提到了哪些关于AI安全性的观点?
-Jensen提到为了确保AI的安全性,我们需要开发出多种技术,比如AI监控AI、人员监控AI以及建立各种安全护栏。他强调了良好的政策、实践和工业标准的重要性,以确保AI的安全和可靠。
Jensen如何描述Super Micro在数据中心现代化中的角色?
-Jensen将Super Micro描述为一个准备好立即服务的合作伙伴,他们能够提供所需的硬件和工程支持,以帮助客户快速启动和运行现代化的数据中心。
在演讲中,Jensen提到了哪些关于数据中心的经济效益?
-Jensen提到了数据中心的经济效益与吞吐量、利用率和启动时间直接相关。他强调了通过提高这些指标,可以增加数据中心的收入,这也是为什么他们称其为AI工厂的原因。
Jensen在演讲中提到了哪些关于软件在高性能计算中的作用?
-Jensen强调了软件在高性能计算中的重要性,他提到了Cuda、网络计算以及分布式计算的软件堆栈,这些都是构建在Nvidia硬件之上的。他还提到了软件的兼容性,这是高性能计算中非常关键的部分。
Outlines
🤖 AI与加速计算的新时代
本段落介绍了Nvidia CEO Jensen Huang的演讲,他强调了AI如何改变最小化计算,并提出了加速计算和绿色计算的概念。加速计算由于数据量指数级增长和CPU扩展放缓,变得尤为重要。Jensen提到了数据中心的能源和成本浪费问题,并强调通过加速计算可以释放这些资源,用于新的目的。此外,他还提出了生成性AI的概念,这是一种能够生成文本、图像、视频等的AI技术,它将影响全球所有的数据中心,并需要在未来几年内进行现代化改造。
🚀 数据中心的液体冷却技术革新
在第二段中,讨论了数据中心的液体冷却技术(DLC),这是一种降低功耗并提高AI芯片制造能力的技术。Jensen Huang提到了Supermicro公司每月能够生产1000个液体冷却系统,这将是一个巨大的公司规模。他还提到了GPU服务器的复杂性,强调了除了GPU之外,还有许多其他组件和技术。此外,Jensen还提到了通过液体冷却系统节省下来的能源可以用于未来的计算,这将有助于创建新的商品——计算吞吐量,这将直接影响数据中心的收入。
🏭 AI工厂的兴起与经济效益
第三段讲述了AI工厂的概念,这些工厂不仅仅是数据存储或文件检索的地方,而是直接产生收入的设施。Jensen Huang强调了AI工厂的启动时间、吞吐量和利用率对收入的影响,以及如何通过整合系统到机架规模来提高这些指标。他还提到了Supermicro公司如何准备好为这些AI工厂提供支持,并且所有这些系统都经过了Nvidia软件的认证。此外,Jensen还讨论了不同类型的配置,以适应不同的使用案例和数据中心需求。
🌐 网络作为计算结构的重要性
在第四段中,Jensen Huang讨论了网络作为计算结构的重要性,特别是对于分布式计算。他提到了Cuda和DOA这两个软件堆栈,它们使得工作负载能够高效地在网络中分布。此外,他还提到了400和800吉比特每秒的高速互连,以及即将到来的1600吉比特每秒的技术。Jensen强调了在训练阶段和推理阶段,不同类型的CPU如何被用于AI,以及如何通过节能的互连提高性能。
🛡️ AI的安全性与未来发展
最后一段中,Jensen Huang讨论了AI的安全性和未来发展。他比喻了飞机的自动驾驶技术,强调了为了确保AI的安全性,需要发明许多技术、实践和政策。Jensen提到了AI监控、人为监督以及安全防护措施的重要性。他强调了为了使AI变得“难以置信地好”,需要在技术、安全性、政策和实践方面不断进步。最后,他以幽默的方式结束了演讲,强调了购买更多产品可以提高安全性的观点。
Mindmap
Keywords
💡人工智能
💡加速计算
💡绿色计算
💡生成式AI
💡数据中心现代化
💡液冷系统
💡GPU
💡软件栈
💡Cuda
💡以太网
💡AI安全
Highlights
英伟达CEO Jensen Huang分享了他对AI改变世界的伟大愿景。
加速计算和绿色计算同时到来,标志着数据中心的能源效率和成本效率的显著提升。
数据量呈指数级增长,而CPU扩展速度放缓,导致数据中心存在大量能源和成本浪费。
加速计算可以释放数据中心的潜能,为新目的使用之前浪费的能源。
英伟达宣布了220种新产品,展示了其在加速计算领域的创新能力。
生成性AI(Generative AI)的出现,预示着数据中心的转型。
到2030年,全球数据中心的价值可能达到3万亿美元,需要现代化改造以适应生成性AI。
超微公司(Super Micro)准备接受订单,提供现代化的数据中心解决方案。
英伟达正在出货数据中心液冷技术,以降低功耗并提高AI芯片的制造效率。
超微公司每月出货1000个液冷系统,展现了其在数据中心冷却技术方面的领先地位。
数据中心的现代化改造对于提高能源效率和降低成本至关重要。
英伟达的GPU不仅是芯片,整个系统比单纯的GPU要复杂得多。
超微公司的工程设计使得安装和启动时间极快,提高了数据中心的利用率和吞吐量。
英伟达的软件许可确保了所有产品在数据中心的兼容性和即插即用。
数据中心被视为AI工厂,直接产生收入,强调了可靠性、吞吐量和启动时间的重要性。
英伟达的CUDA和其他软件堆栈为高性能计算提供了基础。
网络现在是计算的织物,而不仅仅是发送电子邮件的工具。
英伟达的Blackwell GPU和Grace CPU通过高速互连连接,优化了训练和推理的性能。
AI技术的发展需要同时关注安全性和效率,确保AI的可靠性和实用性。
英伟达致力于推进AI技术的发展,同时注重安全技术和政策的制定。
Transcripts
I know only
some fortunately we are very lucky again
to invite the AI
genius our common
friend our common friend is very busy
huh Invidia found CEO Jensen to share
his great vision with us
[Applause]
[Music]
[Applause]
[Music]
thank
you hi
everybody now
what that AI is changing minium because
of you what's new
today I have to admit just now when I
was coming to your keynote in the car I
fell asleep
and so right now right now I'm a little
bit groggy so if I say nonsense things
please I let me apologize first no well
let's see um uh Charles we've gone back
a very long ways yeah and and um uh what
are we doing oh I needed some water I
need to spe up okay right my energy
yeah they said I was on this side and
you keep going on my
side this is what happens when we don't
practice you don't need to and you are
no time you you don't need and so so um
I uh what what were we saying um this is
a very important time because we have a
new agent Computing coming there are two
things that are happening at the same
time the first is accelerated Computing
accelerated Computing has arrived at a
time
oh Green Computing
yeah Green computer yeah okay
Computing I think I think when you say
Green Computing you mean energy
efficient Computing right yes Nvidia is
energy efficient Computing yes we have S
we follow you all
right look Green Computing and Green
Computing all right so so um uh
accelerated computing's time has come
because for a very long time the amount
of data processing has been increasing
exponentially yeah and yet CPU scaling
has slowed for many many years so we've
been we have now an enormous amount of
waste wasted energy and wasted cost
trapped inside the data centers so when
we accelerate the data centers the
savings
incredible because it has been sold long
of waste
trapped and so now we can release the
waste and use that energy for a new
purpose number one accelerate every
application accelerate every data center
these
amazing servers here right so many new
products so many new products you have
220 new products unbelievable did he
tell you that already no I W very high
I came to announce super micros products
and so that's the first thing the second
thing is because the Energy Efficiency
and the performance efficiency and the
cost efficiency is so incredibly great
with accelerated Computing a new way of
doing Computing has emerged and it's
called generative AI generative AI is an
incredible thing people say generative
AI inference it's related not the same
inference
recognizing C dog speech inference
generation text Generation image
Generation video generation that's what
we call a generative AI the pressure of
generative AI to not the pressure but
the the transition to generative AI will
affect every single data center in the
world we have a trillion dat a trillion
dollars worth of data centers in the
world that's established $3 trillion
probably by 2030 in another 6 years we
have to to modernize all of them with
these amazing systems yeah that's the
reason why the demand is so great
because all of these data centers has to
be modernized and Charles and the super
micro team is ready to take your
order Json I'm your I'm your best sales
guy thank you I work on commission no
commission we buy more cheaper from you
don't buy more
[Laughter]
chips so
that's Jon sh Michael is now shipping
data center uh liqu cooling DLC R inum
production now to lower the power
consumption so you can manufacture more
AI chip yeah yeah thousand of how here
you see
[Applause]
[Laughter]
I have many American colleagues they
don't understand my Chinese I have many
Chinese colleagues they don't understand
my
Chinese hi
y we are shipping up to 1,000 R per
month now 1,000 R like it is multiply by
ASP yeah you're going to be a gigantic
company yeah thank
you that's why I need a more
CH did you guys all do the
math Millions
times thousands time 52 no no no you
charging me $2 million more than $2
million for d
[Laughter]
are we allowed to do this on TV are we
on
TV I I guess the well is
this so we are shipping about 1,000
that's incredible now this this uh
600,000 Parts this is probably more than
600,000 parts how many pounds oh I don't
know can I move three I think it's 3,000
lb more than 3,000 lb
yeah it's incredible so yeah our goal
this year is to ship more than 10,000
record you know the Charles this is the
thing that's really amazing uh people
think that we're building
gpus you know GP is a
chip there are 72 chips in here and then
there are six
600,000 other
parts
it's 72 chips probably weighs one
pound this is 3, 2,999 other
pounds so the amount of Technology
that's inside one of these RS is really
quite extraordinary this is a technology
Marvel the most most most complex most
advanced computer the world's ever made
yeah exactly the p in the world now yeah
absolutely incredible and the software
that it takes to run this is
unbelievable yeah unbelievable isn't
that right and so I think that that
people now are starting to realize that
when we say GPU server of course the
brain is the GPU yeah but the system is
much much more complex than that and
super micro does amazing engineering
thank
[Laughter]
you huh what I
okay then we there's some Americans this
year we are going to ship hopefully make
when we're together sometimes we speak
Taiwanese sometimes we speak Mandarin
and then when we disagree we speak
[Laughter]
English we try to make a thlc mar share
from 1% to 15%
this year wow Save lot of power for your
TB yeah yeah the Energy Efficiency is so
much better the cost to the data center
is cheaper cheaper that's right people
don't realize this liquid cooled systems
eliminates an enormous amount of cost in
the data center yeah so that you can use
that waste capture that waste and put it
into Computing in the future in the
future Computing throughput is
revenues because it's token generation
and token generation is dollars per
million tokens just like
energy dollars per kilowatt hour we have
now invented a new commodity this is a
very important idea for all of you this
is a new commodity it has value and the
faster you can generate it the higher
throughput the greater utilization the
higher your revenues it is absolutely
true and it's directly measurable that's
why this is a factory not a data center
that's why this is a factory not a file
server it's not a retrieval of files
it's not used for exchanging emails this
is directly generating revenues for
factories that's why we call it AI
factories and so
powerful and only s million
[Laughter]
dollars a
such okay so $3 million and you can
generate who knows how much revenue per
year right uh 3 million 1,000 and every
year have how many
months
12 the the return on the return on large
language model generation token
generation is going to be very very good
yeah be huge and the reason for that is
because the token embeds intelligence
yeah and the int could be used in so
many different Industries and so the
future is very important it's time to
Startup yeah time to
Startup throughput yeah
utilization all matter so
reliability has Revenue implication
throughput has Revenue implication
startup has Revenue implication yeah
that's why it's so important that we
integrate the whole s whole system into
a rack scale get all the software
working connected to all the all the
networking so that and we build all of
our own data centers we build our own
supercomputers so that we know when you
install this when you install super
micro in your factories the startup time
will be extremely fast your utilization
will be extremely high and your
throughput will be extremely high
because your revenues depends on it
Factory output is measured by all of
those factors very complicated yeah and
all of those R are Invidia sofware
license all certified so the sound of
that parking the cable and they can run
and it runs that's right and all of the
Nvidia Nims all of the large language
models it just runs on all these systems
yeah
[Laughter]
[Applause]
we are shipping thousand R
very
yes very
beautiful Charles Charles said that this
is
everything everything in here is NVIDIA
for all the American citizens
there
from to H AI everything all Nvidia sare
all all Green Computing all Green
Computing all green computer all all
support that's
fantastic
good let go through something detail
okay okay
okay H1 H2 B1 for you cooling wow
shipping in B wow and this one your p200
uhhuh fully ready beautiful for your
chip beautiful beautiful this will be
how many time faster than this so we
have we have we have uh uh for Blackwell
Blackwell has air cool
liquor
cooled
x86
Grace MV link 8 MV link 2 MV link 36 MV
link 72 yeah so many different
configurations yeah so that depending on
the type of type of utilization type of
use case you have the type of data
center that you have uh Charles is ready
to serve you immediately right
immediately doesn't need to acheve yeah
one hand we got to acheve second hand we
Shi to C W thank goodness we only need
two hands in two weeks in two
weeks that's incredible and all of it
software compatible this is really this
is really the amazing thing certifi
literally everything here is software
compatible one% yeah and software as we
know is the most complex part of high
performance Computing yeah thank you for
those great offering they are all ready
to service our customer there are three
very important software Stacks that we
have in our company that everything is
built on top of the first of course is
Cuda very famous the second for all of
the networking because networking is
just not networking networking today
networking today is a Computing
fabric networking today is a Computing
fabric not just for sending email to
each
other
4 Mez a gigahertz megahertz this is not
1980s
be
Mez
kilohertz gahz gigahertz yes 400
gigabits per second 800 gigabits per
second and and then of course Next
Generation coming 1600 but the important
thing is all of the software that we
have that runs on the networking for
distributed computing is on top of two
software Stacks one is called DOA for
the nick nickel for the fabric yeah and
it enables us to distribute the workload
across the network very very efficiently
because ethernet was was not designed
for hyperform computing you make our job
easier but still very py because you
have so many
great my job is to help give you
[Laughter]
job we
and because because you do such a good
job it becomes gives me job oh don't
forget that your another
baby yeah yeah yeah
yeah inside
here this this is an incredible
incredible system in fact in
fact in fact um these chips are all
connected together using high-speed
interconnect the world's fastest CIS the
CIS is incredibly fast and very energy
efficient and so we can connect this
great CPU to dual Blackwell gpus and
that's very important because in the
training stage the memory system of
Grace could be used for checkpoint
restart checkpoint and restarting is
very important for high utilization and
high uptime and so checkpoint restart uh
could be stored in the system memory
that system memory is very low energy
very low power and the link between
Blackwell and Grace is very very high
second during inference time as you know
there's a concept called
prompts context in context training
prompting that prompt memory that
context memory is right here this is the
memory memory the thinking memory the
working memory of AI and so this memory
needs to be very high performance very
low energy and so during training we
have good use for gray gray CPU during
inference we have excellent use for gray
CPU and the interconnect is very very
high speed very low power F optimiz and
so the re the benefit is because we
compress so many in one system yeah if
we
save 20 watts 50 Watts on the
interconnect you multiply by the whole
rack then we can take the energy and use
it for computing y so Energy Efficiency
translates to higher performance to
that's right Green
[Applause]
Computing
huh I am a super micro employee
super micro
employee where AI Control
us of course not um we we have to we
have to uh the most important thing of
course at the moment is we have to make
AI work
well right now ai is of course uh
working extremely well and in many
applications AI has become good enough
to good enough to become useful it has
achieved the plateau of good enough very
useful however we want it to be
incredibly good we want it to be very
functional everything from Guard railing
for uh fine-tuning skill learning there
are many different things that we still
have to improve okay so we know that AI
is AI still has long ways to go that's
job number one is Advance the technology
at the same time we have to advanced
Safety technology as you know uh our the
planes that we all flew on to come here
has autopilot and autopilot is automatic
technology in order for planes to be
safe a great deal of Technology had to
be invented to keep the plane safe yeah
also practices to monitor the planes air
traffic control other planes monitor the
planes Pilots monitoring each other many
different ways to keep uh AI uh keep
autopilot safe in the future we'll do
the same thing with AI there will be AIS
that watch AIS there are people that
watch AIS there's gu right guard rails
that keep AI guard rail and so there's
going to be a whole lot of different
Technologies we need to create for
safety technology for safety and then
third of course we need to have good
policies for safety good practices and
good policies for safety talking about
it is very important so that we can all
remind each other that we have to do
good science good engineering good
business practice good policy practice
good industrial practice all of those
things has to advance so perfect
strategy so the conclusion is one the
more you buy the more
safe the more you buy the more you safe
the more you buy the more you safe
yeah thank you Jas thank you so much
good job thank you
everybody thank you okay thank you thank
you thank you thank you all right have a
great
thank you
[Music]
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