Amazon’s 144-Chip Trainium 3 Monster Server Just Destroyed Google & Nvidia’s Future!

HELLSHADOWRISE

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Transcript

00:00Last week, Amazon didn't just announce a new chip.

00:03They dropped a bomb on the entire AI industry.

00:06On stage in Las Vegas, AWS introduced its new Tranium 3 chip

00:11and claimed it could deliver more than four times the performance of its predecessor,

00:16use around 40% less energy,

00:18and cut the cost of training and running AI models by up to 50%

00:21compared to equivalent NVIDIA GPU systems.

00:24It was the boldest claim Amazon had made in years.

00:27And for a moment, the entire AI world paused to process it.

00:32And what's so shocking is that AWS didn't just talk about improvements.

00:36They positioned Tranium 3 as a direct challenge

00:39to NVIDIA's decade-long control of the AI compute market.

00:43The message couldn't have been clearer.

00:46The era of one company supplying nearly 90% of global AI training hardware

00:51was beginning to fracture.

00:53And Amazon, the world's largest cloud provider,

00:56was the one lighting the fuse.

00:59The timing of Amazon's announcement could not have been more strategic.

01:04NVIDIA had only recently begun shipping its Blackwell B200 GPUs

01:08in volume throughout mid-2025,

01:12following their original unveiling at GTC 2024

01:15as the successors to the massively popular H100 and H200 lineup.

01:21Blackwell was already being hailed as the next era of AI compute,

01:25a technological showcase with higher memory,

01:28faster bandwidth, tighter interconnects,

01:31and an NVLink system that lets 72 GPUs behave like a single supercomputer.

01:37Everyone expected Amazon to stay in NVIDIA's shadow.

01:41Instead, they walked out with a chip designed specifically to attack NVIDIA's weak spot,

01:47the economics of AI.

01:49Amazon didn't challenge NVIDIA head-on.

01:52Instead, it went after the only battleground NVIDIA cannot afford to lose,

01:57the economics of AI.

01:59When AWS said Tranium 3 could deliver the same class of AI training at up to half the cost,

02:06engineers, investors, and researchers immediately understood the implications.

02:10AI companies today spend not just millions,

02:13but hundreds of millions of dollars in compute.

02:16The largest model training runs now consume so much electricity

02:20that entire data centers are built around them.

02:24A chip that significantly lowers cost per token is not just another component,

02:28it is a structural change in how AI is built and who can afford to build it.

02:34But to see why Tranium 3 matters now,

02:37you have to rewind to the moment Amazon introduced the first-generation Tranium chip.

02:42At the time, the entire AI world revolved around NVIDIA.

02:46The V100 and A100 were the default engines for every model,

02:50every experiment, every startup.

02:54AWS customers were begging for more GPUs.

02:57Amazon was paying billions to NVIDIA,

03:00and everyone could see where this was heading.

03:01More demand, more shortages, and more rising prices.

03:07So, Amazon built something small but strategic.

03:12Tranium 1.

03:13It wasn't designed to defeat NVIDIA.

03:16It was designed to give AWS a foothold.

03:19Tranium 1 provided solid BF16 and FP32 performance,

03:24integrated tightly into the EC2 ecosystem,

03:28and introduced the Neuron software stack,

03:31the compiler and runtime layer that slowly taught developers how to train models

03:35without relying entirely on CUDA.

03:39It was Amazon's way of saying,

03:40we need our own path.

03:42It worked.

03:43Not because Tranium 1 changed the industry,

03:46but because it gave AWS the one thing every AI chip project needs.

03:51Real-world data.

03:53That allowed them to build Tranium 2.

03:57Tranium 2 was the moment Amazon went from experimenting to competing.

04:02Featuring 96 gigabytes of HBM3E memory

04:05and redesigned Neuron Core V3 engines built specifically for transformer architectures,

04:11Tranium 2 delivered a major leap in capability.

04:16AWS introduced the Trane 2 Ultra Server,

04:20a system that connected 64 Tranium 2 chips in a single box,

04:25producing more than 80 FP8 petaflops of compute.

04:28Suddenly, AWS had something that could train truly massive language models,

04:35and real companies used it.

04:38Anthropic revealed that more than 1 million Tranium 2 chips

04:41were involved in the training and deployment of clawed models.

04:46Amazon's own AI teams relied on it internally,

04:49and countless start-ups adopted it

04:53because it was simply cheaper to run large models on Tranium 2

04:57than on comparable GPU clusters inside AWS.

05:01That success created the pressure behind Tranium 3,

05:06a chip that didn't need to match NVIDIA on raw performance.

05:09It only needed to make AI affordable again.

05:12Which brings us to why last week's announcement

05:15hit the AI world so hard.

05:17Tranium 3, built on TSMC's advanced 3nm process,

05:24packs high-density compute units

05:26optimised specifically for training transformer models,

05:29the AI architecture behind generative systems

05:32like Claude, Gemini and ChatGPT.

05:35The chip holds 144GB of HBM3E memory directly on the package,

05:42a major increase from the 96GB on Tranium 2.

05:46More importantly, it delivers nearly 5TB per second of memory bandwidth.

05:52This is the kind of bandwidth required for trillion parameter models,

05:56which are increasingly memory-bound rather than compute-bound.

06:00When a model is too large to fit in cache,

06:03and repeatedly has to fetch data from slower memory,

06:06performance collapses.

06:07Tranium 3 was engineered to keep data constantly flowing.

06:12The compute capability is equally significant.

06:16At 2.52 petaflops of FP8 performance per chip,

06:20Tranium 3 enters the same computational class

06:23as Google's Trillium TPU V6,

06:26and the lower-end configurations of NVIDIA's Blackwell GPUs.

06:30But the real breakthrough is how AWS scales this compute.

06:36Unlike traditional servers that house 8 GPUs,

06:40AWS designed a new ultra-server architecture

06:42that can hold up to 144 Tranium 3 chips.

06:47This gives a single machine

06:49more than 360 petaflops of FP8 compute,

06:53a number that would have required multiple GPU racks just a few years ago.

06:59These ultra-servers can then be woven into massive ultra-clusters,

07:04enabling companies to run enormous distributed training jobs

07:08entirely inside AWS's cloud,

07:11without competing with the global demand for GPUs.

07:14But the comparison becomes sharper when Google enters the frame.

07:18For years, TPUs were Google's secret weapon.

07:21They powered internal workloads like search ranking,

07:25YouTube recommendations,

07:27Maps routing,

07:29and Gemini model training.

07:31TPU V5P pods deliver more than 2.5 exaflops of FP8 compute.

07:37TPU V6 Trillium introduced higher energy efficiency,

07:42nearly 200 gigabytes of HBM memory,

07:45and bandwidth levels, comparable to Tranium 3.

07:49And then came Ironwood.

07:50Google's most ambitious TPU yet,

07:53a chip designed not just for performance,

07:56but for planetary-scale clustering.

07:59Ironwood delivers around 4.6 petaflops of FP8 compute per chip,

08:05with 192 gigabytes of HBM 3e,

08:09placing it directly in the same performance class

08:11as NVIDIA's Blackwell,

08:12and well above Tranium in raw throughput.

08:15But Ironwood's real strength isn't inside the chip.

08:18It's in the environment Google built around it.

08:22Ironwood sits inside pods of up to 9,216 TPUs,

08:27all connected through Google's three-dimensional torus mesh

08:31and an optical switching layer

08:33that can literally rewire the cluster in real time.

08:36It's the closest thing the industry has to a living supercomputer.

08:39And that's where the philosophical difference becomes obvious.

08:45Google builds TPUs, largely for Google.

08:48The chips are deeply integrated into Google Cloud's internal infrastructure

08:53and tuned for Google's software stack,

08:56JAX, XLA, and Pathways.

08:59The ecosystem is powerful, but it is built inward,

09:03optimised for Google-scale engineering rather than broad accessibility.

09:06Even though Google now sells TPUs to external customers like META,

09:12the TPU environment remains far more specialised

09:16and far less open than NVIDIA's or Amazon's.

09:19Amazon, by contrast, builds Tranium for customers.

09:23The Neuron SDK is designed to be accessible,

09:27modular, and compatible with the major AI frameworks.

09:30Where TPUs prioritise Google-scale optimisation,

09:35Tranium prioritises market-scale adoption.

09:39This contrast becomes even sharper

09:41when NVIDIA re-enters the picture.

09:44NVIDIA's current line-up is still unmatched in raw performance.

09:48The H100 and H200 dominate today's large model training runs.

09:54Blackwell B200 pushes that dominance further,

09:57with nearly 200 gigabytes of HBM3e memory

10:01and compute throughput, unlike anything seen before.

10:06And at the system level,

10:07the GB200 NVL72 rack

10:10essentially merges 72 GPUs

10:13into a single oversized accelerator,

10:16a supercomputer inside a cabinet.

10:19Most importantly,

10:20NVIDIA continues to hold the most powerful advantage

10:23in the entire AI hardware world,

10:26CUDA.

10:27Combined with KUDNN, NCCL,

10:31TensorT,

10:32and a decade of ecosystem maturity,

10:35CUDA is still the backbone

10:36of almost every major AI framework on the planet.

10:40But NVIDIA faces a problem

10:42that Amazon and Google do not demand.

10:45The world wants more GPUs

10:48than NVIDIA can produce.

10:50Even with massive expansion at TSMC Samsung

10:53and new U.S. fabs,

10:55the global supply remains tight.

10:58GPUs disappear from cloud inventories instantly.

11:02Companies wait months to secure capacity.

11:05Prices remain high

11:06because demand is relentless.

11:08And this is where Tranium 3 poses a real threat.

11:13It doesn't have to beat NVIDIA on pure performance.

11:16It only has to be good enough,

11:17cheap enough,

11:18and available enough

11:19to divert a meaningful percentage of demand

11:22away from GPUs.

11:24Amazon used the launch event

11:26to emphasise its full-stack AI ambitions.

11:30Alongside Tranium 3,

11:32AWS showcased the Nova 2 model series,

11:35which includes text, image, video, and voice models

11:39designed to operate across Bedrock,

11:41Amazon's unified AI platform.

11:44And woven subtly into the keynote

11:46was Amazon's quiet confirmation

11:49that Tranium 4 is already in development.

11:52While details were limited,

11:55AWS hinted that Tranium 4

11:58will be the company's first chip

12:00built specifically for frontier-scale multimodal training,

12:04the kind required for trillion-parameter successors

12:07to Claude, Gemini, and Amazon's own Nova models.

12:12Bedrock now hosts models

12:13from Mistral, Google, Anthropic, NVIDIA,

12:18Minimax, and others,

12:19allowing companies to train, test,

12:21and deploy with a single interface.

12:25Amazon is no longer positioning itself

12:27as merely a cloud provider,

12:28but as an AI ecosystem

12:31with vertical integration across hardware,

12:34models, infrastructure, and developer tools.

12:37Tranium 3 is the present.

12:40Tranium 4 is the signal

12:42that Amazon intends to compete

12:44at the very top of the model training hierarchy.

12:47Meanwhile, the broader industry shift

12:50makes Amazon's strategy even clearer.

12:53OpenAI is no longer relying solely on NVIDIA.

12:57It's building custom accelerators

12:59with AMD and Broadcom.

13:01Meta has its MTIA chips

13:03powering large parts of its inference stack.

13:07Microsoft is developing the Maya architecture

13:09to reduce Azure's dependence on GPUs.

13:12Tesla keeps expanding Dojo

13:15to serve both autonomy and AI workloads.

13:19And Apple has quietly placed neural engines

13:21inside every device in its ecosystem.

13:25The Monopoly era,

13:26the period when NVIDIA supplied

13:27almost the entire world's AI compute,

13:30is fading.

13:31What comes next is a multi-chip,

13:33multi-cloud,

13:35multi-vendor landscape

13:36where no single company

13:38controls the future of AI.

13:40So, do you think Amazon

13:41has finally built enough momentum

13:43to challenge NVIDIA's dominance?

13:45Or is Tranium still underrated by the industry?

13:48Comment your thought below.

13:51And if you want the real story

13:52behind the world's fastest-moving tech

13:54and AI breakthroughs,

13:56make sure to like and subscribe

13:58to Evolving AI for daily coverage.

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