Today at Microsoft Ignite, AMD and Microsoft featured how AMD products, including the upcoming AMD Instinct MI300X accelerator, AMD EPYC CPUs and AMD Ryzen CPUs with AI engines, are enabling new services and compute capabilities across cloud and generative AI, Confidential Computing, Cloud Computing and smarter, more intelligent PCs.

"AMD is fostering AI everywhere - from the cloud, to the enterprise and end point devices - all powered by our CPUs, GPUs, accelerators and AI engines," said Vamsi Boppana, Senior Vice President, AI, AMD. "Together with Microsoft and a rapidly growing ecosystem of software and hardware partners, AMD is accelerating innovation to bring the benefits of AI to a broad portfolio of compute engines, with expanding software capabilities."

During its Ignite conference, Microsoft introduced a duo of custom-designed silicon made to accelerate AI and excel in cloud workloads. First of the two is Microsoft's Azure Cobalt 100 CPU, a 128-core design that features a 64-bit Armv9 instruction set, implemented in a cloud-native design that is set to become a part of Microsoft's offerings. While there aren't many details regarding the configuration, the company claims that the performance target is up to 40% when compared to the current generation of Arm servers running on Azure cloud. The SoC has used Arm's Neoverse CSS platform customized for Microsoft, with presumably Arm Neoverse N2 cores.

The next and hottest topic in the server space is AI acceleration, which is needed for running today's large language models. Microsoft hosts OpenAI's ChatGPT, Microsoft's Copilot, and many other AI services. To help make them run as fast as possible, Microsoft's project Athena now has the name of Maia 100 AI accelerator, which is manufactured on TSMC's 5 nm process. It features 105 billion transistors and supports various MX data formats, even those smaller than 8-bit bit, for maximum performance. Currently tested on GPT 3.5 Turbo, we have yet to see performance figures and comparisons with competing hardware from NVIDIA, like H100/H200 and AMD, with MI300X. The Maia 100 has an aggregate bandwidth of 4.8 Terabits per accelerator, which uses a custom Ethernet-based networking protocol for scaling. These chips are expected to appear in Microsoft data centers early next year, and we hope to get some performance numbers soon.

AMD in its post Q3-2023 financial results call stated that it expects the Instinct MI300X accelerator to be the fastest product in AMD history to rake in $1 billion in sales. This would be the time it took for a product in its lifecycle to register $1 billion in sales. With the MI300 series, the company hopes to finally break into the AI-driven HPC accelerator market that's dominated by NVIDIA, and at scale. This growth is attributable to two distinct factors. The first of which is that NVIDIA is supply bottlenecked, and customers and looking for alternatives, and finally found a suitable one with the MI300 series; and the second is that with the MI300 series, AMD has finally ironed out the software ecosystem backing the hardware that looks incredible on paper.

It's also worth noting here, that AMD is rumored to be sacrificing its market presence in the enthusiast-class gaming GPU segment with its next-generation, with the goal of maximizing its foundry allocation for HPC accelerators such as the MI300X. HPC accelerators are a significantly higher margin class of products than gaming GPUs such as the Radeon RX 7900 XTX. The RX 7900 XTX and its refresh under the RX 7950 series, are not expected to have a successor in the RDNA4 generation. "We now expect datacenter GPU revenue to be approximately $400 million in the fourth quarter and exceed $2 billion in 2024 as revenue ramps throughout the year," said Dr. Lisa Su, CEO AMD, at the company's earnings call with analysts and investors. "This growth would make MI300 the fastest product to ramp to $1 billion in sales in AMD history."

Today, AMD released the findings from a new survey of global IT leaders which found that 3 in 4 IT leaders are optimistic about the potential benefits of AI—from increased employee efficiency to automated cybersecurity solutions—and more than 2 in 3 are increasing investments in AI technologies. However, while AI presents clear opportunities for organizations to become more productive, efficient, and secure, IT leaders expressed uncertainty on their AI adoption timeliness due to their lack of implementation roadmaps and the overall readiness of their existing hardware and technology stack.

AMD commissioned the survey of 2,500 IT leaders across the United States, United Kingdom, Germany, France, and Japan to understand how AI technologies are re-shaping the workplace, how IT leaders are planning their AI technology and related Client hardware roadmaps, and what their biggest challenges are for adoption. Despite some hesitations around security and a perception that training the workforce would be burdensome, it became clear that organizations that have already implemented AI solutions are seeing a positive impact and organizations that delay risk being left behind. Of the organizations prioritizing AI deployments, 90% report already seeing increased workplace efficiency.

AMD today announced revenue for the second quarter of 2023 of $5.4 billion, gross margin of 46%, operating loss of $20 million, net income of $27 million and diluted earnings per share of $0.02. On a non-GAAP basis, gross margin was 50%, operating income was $1.1 billion, net income was $948 million and diluted earnings per share was $0.58.

"We delivered strong results in the second quarter as 4th Gen EPYC and Ryzen 7000 processors ramped significantly," said AMD Chair and CEO Dr. Lisa Su. "Our AI engagements increased by more than seven times in the quarter as multiple customers initiated or expanded programs supporting future deployments of Instinct accelerators at scale. We made strong progress meeting key hardware and software milestones to address the growing customer pull for our data center AI solutions and are on-track to launch and ramp production of MI300 accelerators in the fourth quarter."

TrendForce reports that explosive growth in generative AI applications like chatbots has spurred significant expansion in AI server development in 2023. Major CSPs including Microsoft, Google, AWS, as well as Chinese enterprises like Baidu and ByteDance, have invested heavily in high-end AI servers to continuously train and optimize their AI models. This reliance on high-end AI servers necessitates the use of high-end AI chips, which in turn will not only drive up demand for HBM during 2023~2024, but is also expected to boost growth in advanced packaging capacity by 30~40% in 2024.

TrendForce highlights that to augment the computational efficiency of AI servers and enhance memory transmission bandwidth, leading AI chip makers such as Nvidia, AMD, and Intel have opted to incorporate HBM. Presently, Nvidia's A100 and H100 chips each boast up to 80 GB of HBM2e and HBM3. In its latest integrated CPU and GPU, the Grace Hopper Superchip, Nvidia expanded a single chip's HBM capacity by 20%, hitting a mark of 96 GB. AMD's MI300 also uses HBM3, with the MI300A capacity remaining at 128 GB like its predecessor, while the more advanced MI300X has ramped up to 192 GB, marking a 50% increase. Google is expected to broaden its partnership with Broadcom in late 2023 to produce the AISC AI accelerator chip TPU, which will also incorporate HBM memory, in order to extend AI infrastructure.

AMD recently revealed its Instinct MI300X GPU at their Data Center and AI Technology Premiere event on Tuesday (June 15). The keynote presentation did not provide any details about the new accelerator model's power consumption, but that did not stop one tipster - Hoang Anh Phu - from obtaining this information from Team Red's post-event footnotes. A comparative observation was made: "MI300X (192 GB HBM3, OAM Module) TBP is 750 W, compared to last gen, MI250X TBP is only 500-560 W." A leaked Giga Computing roadmap from last month anticipated server-grade GPUs hitting the 700 W mark.

NVIDIA's Hopper H100 took the crown - with its demand for a maximum of 700 W - as the most power-hungry data center enterprise GPU until now. The MI300X's OCP Accelerator Module-based design now surpasses Team Green's flagship with a slightly greater rating. AMD's new "leadership generative AI accelerator" sports 304 CDNA 3 compute units, which is a clear upgrade over the MI250X's 220 (CDNA 2) CUs. Engineers have also introduced new 24G B HBM3 stacks, so the MI300X can be specced with 192 GB of memory (as a maximum), the MI250X is limited to a 128 GB memory capacity with its slower HBM2E stacks. We hope to see sample units producing benchmark results very soon, with the MI300X pitted against H100.