ASUS today announced that its flagship high-density AI server, ESC A8A-E12U, now supports the latest AMD Instinct MI350 series GPUs. This enhancement empowers enterprises, research institutions, and cloud providers to accelerate their AI and HPC workloads with next-generation performance and efficiency—while preserving compatibility with existing infrastructure.

Built on the 4th Gen AMD CDNA architecture, AMD Instinct MI350 series GPUs deliver powerful new capabilities, including 288 GB of HBM3E memory and up to 8 TB/s of bandwidth—enabling faster, more energy-efficient execution of large AI models and complex simulations. With expanded support for low-precision compute formats such as FP4 and FP6, the Instinct MI350 series significantly accelerates generative AI, inference, and machine-learning workloads. Importantly, Instinct MI350 series GPUs maintain drop-in compatibility with existing AMD Instinct MI300 series-based systems, such as those running Instinct MI325X—offering customers a cost-effective and seamless upgrade path. These innovations reduce server resource requirements and simplify scaling and workload management, making Instinct MI350 series GPUs an ideal choice for efficient, large-scale AI deployments.

AMD revealed today that it anticipates charges of up to $800 million following the Trump administration's latest crackdown on exports of advanced processors to China. The company confirmed that these new rules affect its MI308 product line. The U.S. Commerce Department declared on Tuesday that it was putting in place new licensing requirements impacting several high-performance chips, including NVIDIA's H20, AMD's MI308, and similar products. This regulatory move comes at a high price for AMD, as China stands as its second-biggest market in 2024 with about $6.23 billion in revenue, over 24% of the company's total sales.

AMD's regulatory filing reveals that the company faces $800 million in charges due to inventory issues, purchase commitments, and needed reserves. AMD "expects to apply for licenses but there is no assurance that licenses will be granted,". This uncertainty grows when we consider what Jefferies analysts pointed out on Tuesday: the U.S. has never agreed on licenses for graphics processor unit shipments to China. This news comes right after NVIDIA's announcement that it would take $5.5 billion in charges because of the same export rules. As a result, AMD and NVIDIA stocks were dropping by more than 5%.

Customers evaluating AI infrastructure today rely on a combination of industry-standard benchmarks and real-world model performance metrics—such as those from Llama 3.1 405B, DeepSeek-R1, and other leading open-source models—to guide their GPU purchase decisions. At AMD, we believe that delivering value across both dimensions is essential to driving broader AI adoption and real-world deployment at scale. That's why we take a holistic approach—optimizing performance for rigorous industry benchmarks like MLperf while also enabling Day 0 support and rapid tuning for the models most widely used in production by our customers.

This strategy helps ensure AMD Instinct GPUs deliver not only strong, standardized performance, but also high-throughput, scalable AI inferencing across the latest generative and language models used by customers. We will explore how AMD's continued investment in benchmarking, open model enablement, software and ecosystem tools helps unlock greater value for customers—from MLPerf Inference 5.0 results to Llama 3.1 405B and DeepSeek-R1 performance, ROCm software advances, and beyond.

GIGABYTE Technology, internationally renowned for its R&D capabilities and a leading innovator in server and data center solutions, continues to lead technological innovation during this critical period of AI and computing advancement. With its comprehensive AI product portfolio, GIGABYTE will showcase its complete range of AI computing solutions at CES 2025, from data center infrastructure to IoT applications and personal computing, demonstrating how its extensive product line enables digital transformation across all sectors in this AI-driven era.

Powering AI from the Cloud
With AI Large Language Models (LLMs) now routinely featuring parameters in the hundreds of billions to trillions, robust training environments (data centers) have become a critical requirement in the AI race. GIGABYTE offers three distinctive solutions for AI infrastructure.
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The battle of AI acceleration in the data center is, as most readers are aware, insanely competitive, with NVIDIA offering a top-tier software stack. However, AMD has tried in recent years to capture a part of the revenue that hyperscalers and OEMs are willing to spend with its Instinct MI300X accelerator lineup for AI and HPC. Despite having decent hardware, the company is not close to bridging the gap software-wise with its competitor, NVIDIA. According to the latest report from SemiAnalysis, a research and consultancy firm, they have run a five-month experiment using Instinct MI300X for training and benchmark runs. And the findings were surprising: even with better hardware, AMD's software stack, including ROCm, has massively degraded AMD's performance.

"When comparing NVIDIA's GPUs to AMD's MI300X, we found that the potential on paper advantage of the MI300X was not realized due to a lack within AMD public release software stack and the lack of testing from AMD," noted SemiAnalysis, breaking down arguments in the report further, adding that "AMD's software experience is riddled with bugs rendering out of the box training with AMD is impossible. We were hopeful that AMD could emerge as a strong competitor to NVIDIA in training workloads, but, as of today, this is unfortunately not the case. The CUDA moat has yet to be crossed by AMD due to AMD's weaker-than-expected software Quality Assurance (QA) culture and its challenging out-of-the-box experience."

The 64th edition of the TOP500 reveals that El Capitan has achieved the top spot and is officially the third system to reach exascale computing after Frontier and Aurora. Both systems have since moved down to No. 2 and No. 3 spots, respectively. Additionally, new systems have found their way onto the Top 10.

The new El Capitan system at the Lawrence Livermore National Laboratory in California, U.S.A., has debuted as the most powerful system on the list with an HPL score of 1.742 EFlop/s. It has 11,039,616 combined CPU and GPU cores and is based on AMD 4th generation EPYC processors with 24 cores at 1.8 GHz and AMD Instinct MI300A accelerators. El Capitan relies on a Cray Slingshot 11 network for data transfer and achieves an energy efficiency of 58.89 GigaFLOPS/watt. This power efficiency rating helped El Capitan achieve No. 18 on the GREEN500 list as well.

IBM and AMD have announced a collaboration to deploy AMD Instinct MI300X accelerators as a service on IBM Cloud. This offering, which is expected to be available in the first half of 2025, aims to enhance performance and power efficiency for Gen AI models such as and high-performance computing (HPC) applications for enterprise clients. This collaboration will also enable support for AMD Instinct MI300X accelerators within IBM's watsonx AI and data platform, as well as Red Hat Enterprise Linux AI inferencing support.

"As enterprises continue adopting larger AI models and datasets, it is critical that the accelerators within the system can process compute-intensive workloads with high performance and flexibility to scale," said Philip Guido, executive vice president and chief commercial officer, AMD. "AMD Instinct accelerators combined with AMD ROCm software offer wide support including IBM watsonx AI, Red Hat Enterprise Linux AI and Red Hat OpenShift AI platforms to build leading frameworks using these powerful open ecosystem tools. Our collaboration with IBM Cloud will aim to allow customers to execute and scale Gen AI inferencing without hindering cost, performance or efficiency."

NEC Corporation (NEC; TSE: 6701) has received an order for a next-generation supercomputer system from Japan's National Institutes for Quantum Science and Technology (QST), under the National Research and Development Agency, and the National Institute for Fusion Science (NIFS), part of the National Institutes of Natural Sciences under the Inter-University Research Institute Corporation. The new supercomputer system is scheduled to be operational from July 2025. The next-generation supercomputer system will feature multi-architecture with the latest CPUs and GPUs and will consist of large storage capacity and a high-speed network. This system is expected to be used for various research and development in the field of fusion science research.

Specifically, the system will be used for precise prediction of experiments and creation of operation scenarios in the ITER project, which is being promoted as an international project, and the Satellite Tokamak (JT-60SA) project, which is being promoted as a Broader Approach activity, and for design of DEMO reactors. The DEMO project promotes large-scale numerical calculations for DEMO design and R&D to accelerate the realization of a DEMO reactor that contributes to carbon neutrality. In addition, NIFS will conduct numerical simulation research using the supercomputer for multi-scale and multi-physics systems, including fusion plasmas, to broadly accelerate research on the science and applications of fusion plasmas, and as an Inter-University Research Institute, will provide universities and research institutes nationwide with opportunities for collaborative research using the state-of-the-art supercomputer.

According to the market research firm Mercury Research, the desktop CPU market has witnessed a remarkable transformation, with AMD seizing a substantial 28.7% market share in Q3 of 2024—a giant leap since the launch of the original Zen architecture in 2017. This 5.7 percentage point surge from the previous quarter is a testament to the company's continuous innovation against the long-standing industry leader, Intel. Their year-over-year growth of nearly ten percentage points, fueled by the success of their Ryzen 7000 and 9000 series processors, starkly contrasts Intel's Raptor Lake processors, which encountered technical hurdles like stability issues. AMD's revenue share soared by 8.5 percentage points, indicating robust performance in premium processor segments. Intel, witnessing a decline in its desktop market share to 71.3%, attributes this shift to inventory adjustments rather than competitive pressure and still holds the majority.

AMD's success story extends beyond desktops, with the company claiming 22.3% of the laptop processor market and 24.2% of the server segment. A significant milestone was reached as AMD's data center division generated $3.549 billion in quarterly revenue, a new record for a company not even present in the data center in any considerable quantity just a decade ago. Stemming from strong EPYC processor sales to hyperscalers and cloud providers, along with Instinct MI300X for AI applications, AMD's acceleration of data center deployments is massive. Despite these shifts, Intel continues to hold its dominant position in client computing, with 76.1% of the overall PC market, held by its strong corporate relationships and extensive manufacturing infrastructure. OEM partners like Dell, HP, Lenovo, and others rely heavily on Intel for their CPU choice, equipping institutions like schools, universities, and government agencies.

A Chinese tech forum ChipHell user who goes by zcjzcj11111 sprung up a fascinating take on what the next-generation AMD "Navi 48" GPU could be, and put their imagination on a render. Apparently, the "Navi 48," which powers AMD's series-topping performance-segment graphics card, is a dual chiplet-based design, similar to the company's latest Instinct MI300 series AI GPUs. This won't be a disaggregated GPU such as the "Navi 31" and "Navi 32," but rather a scale-out multi-chip module of two GPU dies that can otherwise run on their own in single-die packages. You want to call this a multi-GPU-on-a-stick? Go ahead, but there are a couple of changes.

On AMD's Instinct AI GPUs, the chiplets have full cache coherence with each other, and can address memory controlled by each other. This cache coherence makes the chiplets work like one giant chip. In a multi-GPU-on-a-stick, there would be no cache coherence, the two dies would be mapped by the host machine as two separate devices, and then you'd be at the mercy of implicit or explicit multi-GPU technologies for performance to scale. This isn't what's happening on AI GPUs—despite multiple chiplets, the GPU is seen by the host as a single PCI device with all its cache and memory visible to software as a contiguously addressable block.

During the Open Compute Project (OCP) Summit 2024, Meta, one of the prime members of the OCP project, showed its NVIDIA "Blackwell" GB200 systems for its massive data centers. We previously covered Microsoft's Azure server rack with GB200 GPUs featuring one-third of the rack space for computing and two-thirds for cooling. A few days later, Google showed off its smaller GB200 system, and today, Meta is showing off its GB200 system—the smallest of the bunch. To train a dense transformer large language model with 405B parameters and a context window of up to 128k tokens, like the Llama 3.1 405B, Meta must redesign its data center infrastructure to run a distributed training job on two 24,000 GPU clusters. That is 48,000 GPUs used for training a single AI model.

Called "Catalina," it is built on the NVIDIA Blackwell platform, emphasizing modularity and adaptability while incorporating the latest NVIDIA GB200 Grace Blackwell Superchip. To address the escalating power requirements of GPUs, Catalina introduces the Orv3, a high-power rack capable of delivering up to 140kW. The comprehensive liquid-cooled setup encompasses a power shelf supporting various components, including a compute tray, switch tray, the Orv3 HPR, Wedge 400 fabric switch with 12.8 Tbps switching capacity, management switch, battery backup, and a rack management controller. Interestingly, Meta also upgraded its "Grand Teton" system for internal usage, such as deep learning recommendation models (DLRMs) and content understanding with AMD Instinct MI300X. Those are used to inference internal models, and MI300X appears to provide the best performance per Dollar for inference. According to Meta, the computational demand stemming from AI will continue to increase exponentially, so more NVIDIA and AMD GPUs is needed, and we can't wait to see what the company builds.

Dell Technologies (NYSE: DELL) expands the world's broadest generative AI (GenAI) solutions portfolio with Dell AI Factory additions tailored for AMD environments. These solutions offer enterprises enhanced AI capabilities, including greater scalability and flexibility, to stay competitive in the evolving technology landscape.

"By integrating AMD technology into the latest Dell servers, AI solutions and services through the Dell AI Factory, we're providing the performance and efficiencies enterprises need today and in the future," said Arthur Lewis, president, Infrastructure Solutions Group, Dell Technologies. "Together with AMD, we are setting new standards in AI performance, giving enterprises powerful and cost-effective solutions essential for modern data-driven environments."

During its "Advancing AI" conference today, AMD has updated its AI accelerator portfolio with the Instinct MI325X accelerator, designed to succeed its MI300X predecessor. Built on the CDNA 3 architecture, Instinct MI325X brings a suite of improvements over the old SKU. Now, the MI325X features 256 GB of HBM3E memory running at 6 TB/s bandwidth. The capacity memory alone is a 1.8x improvement over the old MI300 SKU, which features 192 GB of regular HBM3 memory. Providing more memory capacity is crucial as upcoming AI workloads are training models with parameter counts measured in trillions, as opposed to billions with current models we have today. When it comes to compute resources, the Instinct MI325X provides 1.3 PetaFLOPS at FP16 and 2.6 PetaFLOPS at FP8 training and inference. This represents a 1.3x improvement over the Instinct MI300.

A chip alone is worthless without a good platform, and AMD decided to make the Instinct MI325X OAM modules a drop-in replacement for the current platform designed for MI300X, as they are both pin-compatible. In systems packing eight MI325X accelerators, there are 2 TB of HBM3E memory running at 48 TB/s memory bandwidth. Such a system achieves 10.4 PetaFLOPS of FP16 and 20.8 PetaFLOPS of FP8 compute performance. The company uses NVIDIA's H200 HGX as reference claims for its performance competitiveness, where the company claims that the Instinct MI325X outperforms NVIDIA H200 HGX system by 1.3x across the board in memory bandwidth, FP16 / FP8 compute performance and 1.8x in memory capacity.

The MLCommons consortium on Wednesday posted MLPerf Inference v4.1 benchmark results for popular AI inferencing accelerators available in the market, across brands that include NVIDIA, AMD, and Intel. AMD's Instinct MI300X accelerators emerged competitive to NVIDIA's "Hopper" H100 series AI GPUs. AMD also used the opportunity to showcase the kind of AI inferencing performance uplifts customers can expect from its next-generation EPYC "Turin" server processors powering these MI300X machines. "Turin" features "Zen 5" CPU cores, sporting a 512-bit FPU datapath, and improved performance in AI-relevant 512-bit SIMD instruction-sets, such as AVX-512, and VNNI. The MI300X, on the other hand, banks on the strengths of its memory sub-system, FP8 data format support, and efficient KV cache management.

The MLPerf Inference v4.1 benchmark focused on the 70 billion-parameter LLaMA2-70B model. AMD's submissions included machines featuring the Instinct MI300X, powered by the current EPYC "Genoa" (Zen 4), and next-gen EPYC "Turin" (Zen 5). The GPUs are backed by AMD's ROCm open-source software stack. The benchmark evaluated inference performance using 24,576 Q&A samples from the OpenORCA dataset, with each sample containing up to 1024 input and output tokens. Two scenarios were assessed: the offline scenario, focusing on batch processing to maximize throughput in tokens per second, and the server scenario, which simulates real-time queries with strict latency limits (TTFT ≤ 2 seconds, TPOT ≤ 200 ms). This lets you see the chip's mettle in both high-throughput and low-latency queries.

ASRock Rack sells the entire stack of servers a data-center could possibly want, and at Computex 2024, the company showed us their servers meant for AI GPUs. The 6U8M-GENOA2, as its name suggests, is a 6U server based on 2P AMD EPYC 9004 series "Genoa" processors in the SP5 package. You can configure it with even the variants of "Genoa" that come with 3D V-cache, for superior compute performance from the large cache. Each of the two SP5 sockets is wired to 12 DDR5 RDIMM slots, for a total of 24 memory channels. The server supports eight AMD Instinct MI300X or MI325X AI GPUs, which it wires out using Infinity Fabric links and PCIe Gen 5 x16 individually. A 3 kW 80 Plus Titanium PSU keeps the server fed. There are vacant Gen 5 x16 slots left even after connecting the GPUs, so you could give it a DPU-based 40 GbE NIC.

The 6U8X-EGS2 B100 is a 6U AI GPU server modeled along the 6U8M-GENOA2, with a couple of big changes. To begin with, the EPYC "Genoa" chips make way for a 2P Intel Xeon Socket E (LGA4677) CPU setup, for 2P Xeon 5 "Emerald Rapids" processors. Each socket is wired to 16 DDR5 DIMM slots (the processor itself has 8-channel DDR5, but this is a 2 DIMM-per-channel setup). The server integrates an NVIDIA NVSwitch that wires out NVLinks to eight NVIDIA B100 "Blackwell" AI GPUs. The server features eight HHHL PCIe Gen 5 x16, and five FHHL PCIe Gen 5 x16 connectors. There are vacant x16 slots for your DPU/NIC, you can even use an AIC NVIDIA BlueField card. The same 3 kW PSU as the "Genoa" system is also featured here.

Today at Microsoft Build, AMD (NASDAQ: AMD) showcased its latest end-to-end compute and software capabilities for Microsoft customers and developers. By using AMD solutions such as AMD Instinct MI300X accelerators, ROCm open software, Ryzen AI processors and software, and Alveo MA35D media accelerators, Microsoft is able to provide a powerful suite of tools for AI-based deployments across numerous markets. The new Microsoft Azure ND MI300X virtual machines (VMs) are now generally available, giving customers like Hugging Face, access to impressive performance and efficiency for their most demanding AI workloads.

"The AMD Instinct MI300X and ROCm software stack is powering the Azure OpenAI Chat GPT 3.5 and 4 services, which are some of the world's most demanding AI workloads," said Victor Peng, president, AMD. "With the general availability of the new VMs from Azure, AI customers have broader access to MI300X to deliver high-performance and efficient solutions for AI applications."

According to the latest report from Bloomberg, AMD has hit a roadblock in offering its top-of-the-line AI accelerator in the Chinese market. The newest AI chip is called Instinct MI309, a lower-performance Instinct MI300 variant tailored to meet the latest US export rules for selling advanced chips to China-based entities. However, the Instinct MI309 still appears too powerful to gain unconditional approval from the US Department of Commerce, leaving AMD in need of an export license. Originally, the US Department of Commerce made a rule: Total Processing Performance (TPP) score should not exceed 4800, effectively capping AI performance at 600 FP8 TFLOPS. This rule ensures that processors with slightly lower performance may still be sold to Chinese customers, provided their performance density (PD) is sufficiently low.

However, AMD's latest creation, Instinct MI309, is everything but slow. Based on the powerful Instinct MI300, AMD has not managed to bring it down to acceptable levels to acquire a US export license from the Department of Commerce. It is still unknown which Chinese customer was trying to acquire AMD's Instinct MI309; however, it could be one of the Chinese AI labs trying to get ahold of more training hardware for their domestic models. NVIDIA has employed a similar tactic, selling A800 and H800 chips to China, until the US also ended the export of these chips to China. AI labs located in China can only use domestic hardware, including accelerators from Alibaba, Huawei, and Baidu. Cloud services hosting GPUs in US can still be accessed by Chinese companies, but that is currently under US regulators watchlist.

Brett Simpson, Partner and Co-Founder of Arete Research, sat down with AMD CTO Mark Papermaster during the former's "Investor Webinar Conference." A transcript of the Arete + AMD question and answer session appeared online last week—the documented fireside chat concentrated mostly on "AI compute market" topics. Papermaster was asked about his company's competitive approach when taking on NVIDIA's very popular range of A100 and H100 AI GPUs, as well as the recently launched GH200 chip. The CTO did not reveal any specific pricing strategies—a "big picture" was painted instead: "I think what's important when you just step back is to look at total cost of ownership, not just one GPU, one accelerator, but total cost of ownership. But now when you also look at the macro, if there's not competition in the market, you're going to see not only a growth of the price of these devices due to the added content that they have, but you're -- without a check and balance, you're going to see very, very high margins, more than that could be sustained without a competitive environment."

Papermaster continued: "And what I think is very key with -- as AMD has brought competition market for these most powerful AI training and inference devices is you will see that check and balance. And we have a very innovative approach. We've been a leader in chiplet design. And so we have the right technology for the right purpose of the AI build-out that we do. We have, of course, a GPU accelerator. But there's many other circuitry associated with being able to scale and build out these large clusters, and we're very, very efficient in our design." Team Red started to ship its flagship accelerator, Instinct MI300X, to important customers at the start of 2024—Arete Research's Simpson asked about the possibility of follow-up models. In response, AMD's CTO referenced some recent history: "Well, I think the first thing that I'll highlight is what we did to arrive at this point, where we are a competitive force. We've been investing for years in building up our GPU road map to compete in both HPC and AI. We had a very, very strong harbor train that we've been on, but we had to build our muscle in the software enablement."

GIGABYTE Technology, an IT pioneer advancing global industries through cloud and AI computing systems, is presenting innovative enterprise computing solutions at MWC 2024, featuring trailblazing servers, green computing solutions, and edge AI technologies, under the theme "Future of COMPUTING." These advancements usher in new possibilities for agile and sustainable IT strategies, enabling industries to harness real-time intelligence across hyperconnected data centers, cloud, edge, and devices, resulting in enhanced efficiency, cost-effectiveness, and competitive advantages, all propelled by the synergies of 5G and AI technologies.

GIGABYTE presents G593-ZX1/ZX2, the AI server featuring AMD Instinct MI300X 8-GPU, which is a new addition to GIGABYTE's flagship AI/HPC server series. Other highlighted exhibits include the high-density H223-V10 supporting the NVIDIA Grace Hopper Superchip, the G383-R80 server supporting four AMD Instinct MI300A APUs, and a G593 series AI server equipped with the powerful NVIDIA HGX H100 8-GPU.

NVIDIA is anticipating supply issues for its upcoming Blackwell GPUs, which are expected to significantly improve artificial intelligence compute performance. "We expect our next-generation products to be supply constrained as demand far exceeds supply," said Colette Kress, NVIDIA's chief financial officer, during a recent earnings call. This prediction of scarcity comes just days after an analyst noted much shorter lead times for NVIDIA's current flagship Hopper-based H100 GPUs tailored to AI and high-performance computing. The eagerly anticipated Blackwell architecture and B100 GPUs built on it promise major leaps in capability—likely spurring NVIDIA's existing customers to place pre-orders already. With skyrocketing demand in the red-hot AI compute market, NVIDIA appears poised to capitalize on the insatiable appetite for ever-greater processing power.

However, the scarcity of NVIDIA's products may present an excellent opportunity for significant rivals like AMD and Intel. If both companies can offer a product that could beat NVIDIA's current H100 and provide a suitable software stack, customers would be willing to jump to their offerings and not wait many months for the anticipated high lead times. Intel is preparing the next-generation Gaudi 3 and working on the Falcon Shores accelerator for AI and HPC. AMD is shipping its Instinct MI300 accelerator, a highly competitive product, while already working on the MI400 generation. It remains to be seen if AI companies will begin the adoption of non-NVIDIA hardware or if they will remain a loyal customer and agree to the higher lead times of the new Blackwell generation. However, capacity constrain should only be a problem at launch, where the availability should improve from quarter to quarter. As TSMC improves CoWoS packaging capacity and 3 nm production, NVIDIA's allocation of the 3 nm wafers will likely improve over time as the company moves its priority from H100 to B100.

Samsung, SK hynix, and Micron are considered to be the top manufacturing sources of High Bandwidth Memory (HBM)—the HBM3 and HBM3E standards are becoming increasingly in demand, due to a widespread deployment of GPUs and accelerators by generative AI companies. Taiwan's Commercial Times proposes that there is an ongoing shortage of HBM components—but this presents a growth opportunity for smaller manufacturers in the region. Naturally, the big name producers are expected to dive in head first with the development of next generation models. The aforementioned financial news article cites research conducted by the Gartner group—they predict that the HBM market will hit an all-time high of $4.976 billion (USD) by 2025.

This estimate is almost double that of projected revenues (just over $2 billion) generated by the HBM market in 2023—the explosive growth of generative AI applications has "boosted" demand for the most performant memory standards. The Commercial Times report states that SK Hynix is the current HBM3E leader, with Micron and Samsung trailing behind—industry experts believe that stragglers will need to "expand HBM production capacity" in order to stay competitive. SK Hynix has shacked up with NVIDIA—the GH200 Grace Hopper platform was unveiled last summer; outfitted with the South Korean firm's HBM3e parts. In a similar timeframe, Samsung was named as AMD's preferred supplier of HBM3 packages—as featured within the recently launched Instinct MI300X accelerator. NVIDIA's HBM3E deal with SK Hynix is believed to extend to the internal makeup of Blackwell GB100 data-center GPUs. The HBM4 memory standard is expected to be the next major battleground for the industry's hardest hitters.

Supermicro, Inc., a Total IT Solution Manufacturer for AI, Cloud, Storage, and 5G/Edge, is announcing three new additions to its AMD-based H13 generation of GPU Servers, optimized to deliver leading-edge performance and efficiency, powered by the new AMD Instinct MI300 Series accelerators. Supermicro's powerful rack scale solutions with 8-GPU servers with the AMD Instinct MI300X OAM configuration are ideal for large model training.

The new 2U liquid-cooled and 4U air-cooled servers with the AMD Instinct MI300A Accelerated Processing Units (APUs) accelerators are available and improve data center efficiencies and power the fast-growing complex demands in AI, LLM, and HPC. The new systems contain quad APUs for scalable applications. Supermicro can deliver complete liquid-cooled racks for large-scale environments with up to 1,728 TFlops of FP64 performance per rack. Supermicro worldwide manufacturing facilities streamline the delivery of these new servers for AI and HPC convergence.

Generative AI (GenAI) is the decade's most promising accelerator for innovation with 78% of IT decision makers reporting they're largely excited for the potential GenAI can have on their organizations.¹ Most see GenAI as a means to provide productivity gains, streamline processes and achieve cost savings. Harnessing this technology is critical to ensure organizations can compete in this new digital era.

Dell Technologies and AMD are coming together to unveil an expansion to the Dell Generative AI Solutions portfolio, continuing the work of accelerating advanced workloads and offering businesses more choice to continue their unique GenAI journeys. This new technology highlights a pivotal role played by open ecosystems and silicon diversity in empowering customers with simple, trusted and tailored solutions to bring AI to their data.

Today at the "Advancing AI" event, AMD was joined by industry leaders including Microsoft, Meta, Oracle, Dell Technologies, HPE, Lenovo, Supermicro, Arista, Broadcom and Cisco to showcase how these companies are working with AMD to deliver advanced AI solutions spanning from cloud to enterprise and PCs. AMD launched multiple new products at the event, including the AMD Instinct MI300 Series data center AI accelerators, ROCm 6 open software stack with significant optimizations and new features supporting Large Language Models (LLMs) and Ryzen 8040 Series processors with Ryzen AI.

"AI is the future of computing and AMD is uniquely positioned to power the end-to-end infrastructure that will define this AI era, from massive cloud installations to enterprise clusters and AI-enabled intelligent embedded devices and PCs," said AMD Chair and CEO Dr. Lisa Su. "We are seeing very strong demand for our new Instinct MI300 GPUs, which are the highest-performance accelerators in the world for generative AI. We are also building significant momentum for our data center AI solutions with the largest cloud companies, the industry's top server providers, and the most innovative AI startups ꟷ who we are working closely with to rapidly bring Instinct MI300 solutions to market that will dramatically accelerate the pace of innovation across the entire AI ecosystem."

Today, AMD announced the availability of the AMD Instinct MI300X accelerators - with industry leading memory bandwidth for generative AI and leadership performance for large language model (LLM) training and inferencing - as well as the AMD Instinct MI300A accelerated processing unit (APU) - combining the latest AMD CDNA 3 architecture and "Zen 4" CPUs to deliver breakthrough performance for HPC and AI workloads.

"AMD Instinct MI300 Series accelerators are designed with our most advanced technologies, delivering leadership performance, and will be in large scale cloud and enterprise deployments," said Victor Peng, president, AMD. "By leveraging our leadership hardware, software and open ecosystem approach, cloud providers, OEMs and ODMs are bringing to market technologies that empower enterprises to adopt and deploy AI-powered solutions."