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Building AI infrastructure at yotta-scale requires more than raw performance; it demands an open, modular rack design that can evolve across product generations, combining leadership compute engines with high-speed networking to connect thousands of accelerators into a single, unified system. Take a look at the Helios. Our rack-scale AI... show more

AMD

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$AMD is becoming a two-engine AI story with EPYC CPUs for the growing compute needed around agentic workloads and Instinct GPUs for hyperscalers that need a credible alternative to $NVDA. The CPU side still looks underappreciated as AI agents require more high-core-count server compute and 2nm Venice should help AMD keep taking share as $INTC works through roadmap and manufacturing challenges. The bridge is Helios where AMD can package EPYC CPUs, Instinct GPUs and Pensando networking into a full rack-scale AI platform backed by real demand from $META, $ORCL and OpenAI.

$AMD is becoming a two-engine AI story with EPYC CPUs for the growing compute needed around agentic workloads and Instinct GPUs for hyperscalers that need a credible alternative to $NVDA. The CPU side still looks underappreciated as AI agents require more high-core-count server compute and 2nm Venice should help AMD keep taking share as $INTC works through roadmap and manufacturing challenges. The bridge is Helios where AMD can package EPYC CPUs, Instinct GPUs and Pensando networking into a full rack-scale AI platform backed by real demand from $META, $ORCL and OpenAI.

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🎤 Overheard at #CES2026: AI compute is entering the yottascale era. William Myrhang shows how the AMD Helios AI Platform enables AI to scale by the rack and by the data center, not one server at a time. Designed for agentic and large-scale AI workloads, Helios makes yottascale compute practical, deployable, and sustainable. Zettaflops defined the early AI boom. Yottaflops will define what comes next. AMD is helping build the open foundation needed to support this unprecedented scale.

🎤 Overheard at #CES2026: AI compute is entering the yottascale era. William Myrhang shows how the AMD Helios AI Platform enables AI to scale by the rack and by the data center, not one server at a time. Designed for agentic and large-scale AI workloads, Helios makes yottascale compute practical, deployable, and sustainable. Zettaflops defined the early AI boom. Yottaflops will define what comes next. AMD is helping build the open foundation needed to support this unprecedented scale.

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“Helios is truly a gamechanger.” Well said, Dr. Lisa Su. This unified rack system, integrating AMD CPUs, GPUs, Pensando NICs, and ROCm software, is purpose-built for the most demanding AI workloads. Helios delivers leadership memory, bandwidth, and interconnect speed. 2026 is not that far away. Get ready! #AdvancingAI

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$MU $SKHY $DRAM Holy Shit $AMD's Lisa Su just spent two full minutes at the 2026 AI keynote raving about HBM4 capacity as the thing that separates Helios from the competition. Not compute. Memory. "When you compare Helios to the competition, we're delivering 15% more compute, 50% more HBM4 memory capacity and memory bandwidth, and 50% more scale-out bandwidth." Notice which number is bigger. "What that means is that every Helios can deliver more performance for the largest models, more capacity for longer context, and the bandwidth to scale across thousands of racks.... More than 18,000 CDNA five GPU compute units, over 4,600 Zen 6 CPU cores, and 31 terabytes of HBM4 memory, all in a single rack." "That's what it takes to run agentic AI at scale. Today, I'm excited to announce that Helios is in full production. We have shipments on track to start at the end of the third quarter and ramping into the fourth quarter in the second half of the year. I can tell you, customer demand for Helios is extremely strong, we're extremely proud of the work that we have done across the leading AI labs to adopt Helios."

$MU $SKHY $DRAM Holy Shit $AMD's Lisa Su just spent two full minutes at the 2026 AI keynote raving about HBM4 capacity as the thing that separates Helios from the competition. Not compute. Memory. "When you compare Helios to the competition, we're delivering 15% more compute, 50% more HBM4 memory capacity and memory bandwidth, and 50% more scale-out bandwidth." Notice which number is bigger. "What that means is that every Helios can deliver more performance for the largest models, more capacity for longer context, and the bandwidth to scale across thousands of racks.... More than 18,000 CDNA five GPU compute units, over 4,600 Zen 6 CPU cores, and 31 terabytes of HBM4 memory, all in a single rack." "That's what it takes to run agentic AI at scale. Today, I'm excited to announce that Helios is in full production. We have shipments on track to start at the end of the third quarter and ramping into the fourth quarter in the second half of the year. I can tell you, customer demand for Helios is extremely strong, we're extremely proud of the work that we have done across the leading AI labs to adopt Helios."

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Today, AMD and Microsoft are announcing an expanded strategic partnership to deliver a full stack of AMD AI solutions for Microsoft Azure, spanning GPUs, CPUs, networking and software. At the center of this expansion, Microsoft will deploy AMD Helios to power frontier model AI inference for Microsoft, its AI customers and support Azure AI services. Read more on the news ahead of #AdvancingAI:

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$QCOM buying Modular was about more than adding another AI asset. I sat down with Modular CEO Chris Lattner and Qualcomm CEO Cristiano R. Amon on why the industry needs a better software model for heterogeneous compute and how Qualcomm + Modular intend to attack it. If they can make it dramatically easier for developers to target CPUs, GPUs and accelerators without being locked into a single architecture, this could matter a lot.

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“This is the most advanced accelerator we've ever built!” - Dr. Lisa Su. Helios' "heart": The MI400 Series. It runs trillion-plus parameter models, delivers up to 40 PFLOPS of FP4 performance, features 432 GB of HBM4, and supports 300 GB/s of scale-out bandwidth to connect across racks and clusters. Now, this is how we power the future of AI. #AdvancingAI

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Moonshot AI limiting Kimi K3 subscriptions because of compute constraints is a massive signal because the 2.8 trillion parameter model is already trained and the bottleneck is now emerging as usage scales. Serving that demand requires a ton more accelerators, enormous memory capacity and faster networking to keep data moving efficiently across the cluster. So as AI spending shifts toward serving millions or billions of daily requests then memory and networking become critical bottlenecks giving $MU, $SKHY, $AVGO, $CRDO, $ALAB and $MRVL direct exposure to inference at scale.

Moonshot AI limiting Kimi K3 subscriptions because of compute constraints is a massive signal because the 2.8 trillion parameter model is already trained and the bottleneck is now emerging as usage scales. Serving that demand requires a ton more accelerators, enormous memory capacity and faster networking to keep data moving efficiently across the cluster. So as AI spending shifts toward serving millions or billions of daily requests then memory and networking become critical bottlenecks giving $MU, $SKHY, $AVGO, $CRDO, $ALAB and $MRVL direct exposure to inference at scale.

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🎤 Overheard at #CES2026: AMD EVP Phil Guido shares how Helios addresses the performance, cost and compute demands for customers in a single solution.

🎤 Overheard at #CES2026: AMD EVP Phil Guido shares how Helios addresses the performance, cost and compute demands for customers in a single solution.

AMD

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