NVIDIA's next-gen Blackwell platform, which includes B-series GPUs and integrates NVIDIA's own Grace Arm CPU in models such as the GB200, represents a significant development. TrendForce points out that the GB200 and its predecessor, the GH200, both feature a combined CPU+GPU solution, primarily equipped with the NVIDIA Grace CPU and H200 GPU. However, the GH200 accounted for only approximately 5% of NVIDIA's high-end GPU shipments. The supply chain has high expectations for the GB200, with projections suggesting that its shipments could exceed millions of units by 2025, potentially making up nearly 40 to 50% of NVIDIA's high-end GPU market.

Although NVIDIA plans to launch products such as the GB200 and B100 in the second half of this year, upstream wafer packaging will need to adopt more complex and high-precision CoWoS-L technology, making the validation and testing process time-consuming. Additionally, more time will be required to optimize the B-series for AI server systems in aspects such as network communication and cooling performance. It is anticipated that the GB200 and B100 products will not see significant production volumes until 4Q24 or 1Q25.

On March 29th, the United States announced another round of updates to its export controls, targeting advanced computing, supercomputers, semiconductor end-uses, and semiconductor manufacturing products. These new regulations, which took effect on April 4th, are designed to prevent certain countries and businesses from circumventing U.S. restrictions to access sensitive chip technologies and equipment. Despite these tighter controls, TrendForce believes the practical impact on the industry will be minimal.

The latest updates aim to refine the language and parameters of previous regulations, tightening the criteria for exports to Macau and D:5 countries (China, North Korea, Russia, Iran, etc.). They require a detailed examination of all technology products' Total Processing Performance (TPP) and Performance Density (PD). If a product exceeds certain computing power thresholds, it must undergo a case-by-case review. Nevertheless, a new provision, Advanced Computing Authorized (ACA), allows for specific exports and re-exports among selected countries, including the transshipment of particular products between Macau and D:5 countries.

The expansion of AI technology affects not only the production and demand for graphics cards but also the electricity grid that powers them. Data centers hosting thousands of GPUs are becoming more common, and the industry has been building new facilities for GPU-enhanced servers to serve the need for more AI. However, these powerful GPUs often consume over 500 Watts per single card, and NVIDIA's latest Blackwell B200 GPU has a TGP of 1000 Watts or a single kilowatt. These kilowatt GPUs will be present in data centers with 10s of thousands of cards, resulting in multi-megawatt facilities. To combat the load on the national electricity grid, US President Joe Biden's administration has been discussing with big tech to re-evaluate their power sources, possibly using smaller nuclear plants. According to an Axios interview with Energy Secretary Jennifer Granholm, she has noted that "AI itself isn't a problem because AI could help to solve the problem." However, the problem is the load-bearing of the national electricity grid, which can't sustain the rapid expansion of the AI data centers.

The Department of Energy (DOE) has been reportedly talking with firms, most notably hyperscalers like Microsoft, Google, and Amazon, to start considering nuclear fusion and fission power plants to satisfy the need for AI expansion. We have already discussed the plan by Microsoft to embed a nuclear reactor near its data center facility and help manage the load of thousands of GPUs running AI training/inference. However, this time, it is not just Microsoft. Other tech giants are reportedly thinking about nuclear as well. They all need to offload their AI expansion from the US national power grid and develop a nuclear solution. Nuclear power is a mere 20% of the US power sourcing, and DOE is currently financing a Holtec Palisades 800-MW electric nuclear generating station with $1.52 billion in funds for restoration and resumption of service. Microsoft is investing in a Small Modular Reactors (SMRs) microreactor energy strategy, which could be an example for other big tech companies to follow.

One key takeaway from the ongoing GTC is that Nvidia's AI empire has taken shape with strong partnerships from TSMC and other Taiwanese makers, such as those major server ODMs.

According to the news report from the technology-focused media DIGITIMES Asia, during his keynote at GTC on March 18, Huang underscored his company's partnerships with TSMC, as well as the supply chain in Taiwan. Speaking to the press later, Huang said Nvidia will have a very strong demand for CoWoS, the advanced packaging services TSMC offers.

During its 55th annual shareholders' meeting, Samsung Electronics announced its entry into the AI processor market with the upcoming launch of its Mach-1 AI accelerator chips in early 2025. The South Korean tech giant revealed its plans to compete with established players like NVIDIA in the rapidly growing AI hardware sector. The Mach-1 generation of chips is an application-specific integrated circuit (ASIC) design equipped with LPDDR memory that is envisioned to excel in edge computing applications. While Samsung does not aim to directly rival NVIDIA's ultra-high-end AI solutions like the H100, B100, or B200, the company's strategy focuses on carving out a niche in the market by offering unique features and performance enhancements at the edge, where low power and efficient computing is what matters the most.

According to SeDaily, the Mach-1 chips boast a groundbreaking feature that significantly reduces memory bandwidth requirements for inference to approximately 0.125x compared to existing designs, which is an 87.5% reduction. This innovation could give Samsung a competitive edge in terms of efficiency and cost-effectiveness. As the demand for AI-powered devices and services continues to soar, Samsung's foray into the AI chip market is expected to intensify competition and drive innovation in the industry. While NVIDIA currently holds a dominant position, Samsung's cutting-edge technology and access to advanced semiconductor manufacturing nodes could make it a formidable contender. The Mach-1 has been field-verified on an FPGA, while the final design is currently going through a physical design for SoC, which includes placement, routing, and other layout optimizations.

Dell Technologies is strengthening its collaboration with NVIDIA to help enterprises adopt AI technologies. By expanding the Dell Generative AI Solutions portfolio, including with the new Dell AI Factory with NVIDIA, organizations can accelerate integration of their data, AI tools and on-premises infrastructure to maximize their generative AI (GenAI) investments. "Our enterprise customers are looking for an easy way to implement AI solutions—that is exactly what Dell Technologies and NVIDIA are delivering," said Michael Dell, founder and CEO, Dell Technologies. "Through our combined efforts, organizations can seamlessly integrate data with their own use cases and streamline the development of customized GenAI models."

"AI factories are central to creating intelligence on an industrial scale," said Jensen Huang, founder and CEO, NVIDIA. "Together, NVIDIA and Dell are helping enterprises create AI factories to turn their proprietary data into powerful insights."

NVIDIA on Monday, at the 2024 GTC conference, unveiled the "Blackwell" B200 and GB200 AI GPUs. These are designed to offer an incredible 5X the AI inferencing performance gain over the current-gen "Hopper" H100, and come with four times the on-package memory. The B200 "Blackwell" is the largest chip physically possible using existing foundry tech, according to its makers. The chip is an astonishing 208 billion transistors, and is made up of two chiplets, which by themselves are the largest possible chips.

Each chiplet is built on the TSMC N4P foundry node, which is the most advanced 4 nm-class node by the Taiwanese foundry. Each chiplet has 104 billion transistors. The two chiplets have a high degree of connectivity with each other, thanks to a 10 TB/s custom interconnect. This is enough bandwidth and latency for the two to maintain cache coherency (i.e. address each other's memory as if they're their own). Each of the two "Blackwell" chiplets has a 4096-bit memory bus, and is wired to 96 GB of HBM3E spread across four 24 GB stacks; which totals to 192 GB for the B200 package. The GPU has a staggering 8 TB/s of memory bandwidth on tap. The B200 package features a 1.8 TB/s NVLink interface for host connectivity, and connectivity to another B200 chip.

ASUS today announced its participation at the NVIDIA GTC global AI conference, where it will showcase its solutions at booth #730. On show will be the apex of ASUS GPU server innovation, ESC NM1-E1 and ESC NM2-E1, powered by the NVIDIA MGX modular reference architecture, accelerating AI supercomputing to new heights. To help meet the increasing demands for generative AI, ASUS uses the latest technologies from NVIDIA, including the B200 Tensor Core GPU, the GB200 Grace Blackwell Superchip, and H200 NVL, to help deliver optimized AI server solutions to boost AI adoption across a wide range of industries.

To better support enterprises in establishing their own generative AI environments, ASUS offers an extensive lineup of servers, from entry-level to high-end GPU server solutions, plus a comprehensive range of liquid-cooled rack solutions, to meet diverse workloads. Additionally, by leveraging its MLPerf expertise, the ASUS team is pursuing excellence by optimizing hardware and software for large-language-model (LLM) training and inferencing and seamlessly integrating total AI solutions to meet the demanding landscape of AI supercomputing.

Powering a new era of computing, NVIDIA today announced that the NVIDIA Blackwell platform has arrived—enabling organizations everywhere to build and run real-time generative AI on trillion-parameter large language models at up to 25x less cost and energy consumption than its predecessor.

The Blackwell GPU architecture features six transformative technologies for accelerated computing, which will help unlock breakthroughs in data processing, engineering simulation, electronic design automation, computer-aided drug design, quantum computing and generative AI—all emerging industry opportunities for NVIDIA.

Jensen Huang's opening GTC 2024 keynote is scheduled to happen tomorrow afternoon (13:00 Pacific time)—many industry experts believe that the NVIDIA boss will take the stage and formally introduce his company's B100 "Blackwell" GPU architecture. An enlightened few have been treated to preview (AI and HPC) units—including Dell's CEO, Jeff Clarke—but pre-introduction leaks have not flowed out. Team Green is likely enforcing strict conditions upon a fortunate selection of trusted evaluators, within a pool of ecosystem partners and customers.

Today, a brave soul has broken that silence—tech tipster, AGF/XpeaGPU, fears repercussions from the leather-jacketed one. They revealed a handful of technical details, a day prior to Team Green's highly anticipated unveiling: "I don't want to spoil NVIDIA B100 launch tomorrow, but this thing is a monster. 2 dies on (TSMC) CoWoS-L, 8x8-Hi HBM3E stacks for 192 GB of memory." They also crystal balled an inevitable follow-up card: "one year later, B200 goes with 12-Hi stacks and will offer a beefy 288 GB. And the performance! It's... oh no Jensen is there... me run away!" Reuters has also joined in on the fun, with some predictions and insider information: "NVIDIA is unlikely to give specific pricing, but the B100 is likely to cost more than its predecessor, which sells for upwards of $20,000." Enterprise products are expected to arrive first—possibly later this year—followed by gaming variants, maybe months later.

NVIDIA's next-generation AI GPU, the B100 "Blackwell," is now in the hands of the company's biggest ecosystem partners and customers for evaluation, and one of them is Dell. Jeff Clarke, the OEM giant's chief operating officer, speaking to industry analysts in an investor teleconference, said that he is excited about the upcoming B100 and B200 chips from NVIDIA. B100 is codename for the AI GPU NVIDIA designs for PCIe add-on card and the SXM socket, meant for systems powered by x86 CPUs such as the AMD EPYC or Intel Xeon Scalable. The B200 is its variant meant for machines powered by NVIDIA's in-house Arm-based processors, such as the successor to its Grace CPU, and its combination with an AI GPU, called Grace Hopper (GH200).

Perhaps the most interesting remark by Clarke about the B100 is that he doesn't think it needs liquid cooling, and can make do with high-airflow cooling like the H100. "We're excited about what happens at the B100 and the B200, and we think that's where there's actually another opportunity to distinguish engineering confidence. Our characterization in the thermal side, you really don't need to direct-liquid cooling to get to the energy density of 1000 W per GPU. That happens next year with the B200," he said. NVIDIA is planning a 2024 debut for "Blackwell" in the AI GPU space with the B100, with B200 slated for 2025, possibly alongside a new CPU.