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.

It's official: NVIDIA delivered the world's fastest platform in industry-standard tests for inference on generative AI. In the latest MLPerf benchmarks, NVIDIA TensorRT-LLM—software that speeds and simplifies the complex job of inference on large language models—boosted the performance of NVIDIA Hopper architecture GPUs on the GPT-J LLM nearly 3x over their results just six months ago. The dramatic speedup demonstrates the power of NVIDIA's full-stack platform of chips, systems and software to handle the demanding requirements of running generative AI. Leading companies are using TensorRT-LLM to optimize their models. And NVIDIA NIM—a set of inference microservices that includes inferencing engines like TensorRT-LLM—makes it easier than ever for businesses to deploy NVIDIA's inference platform.

Raising the Bar in Generative AI
TensorRT-LLM running on NVIDIA H200 Tensor Core GPUs—the latest, memory-enhanced Hopper GPUs—delivered the fastest performance running inference in MLPerf's biggest test of generative AI to date. The new benchmark uses the largest version of Llama 2, a state-of-the-art large language model packing 70 billion parameters. The model is more than 10x larger than the GPT-J LLM first used in the September benchmarks. The memory-enhanced H200 GPUs, in their MLPerf debut, used TensorRT-LLM to produce up to 31,000 tokens/second, a record on MLPerf's Llama 2 benchmark. The H200 GPU results include up to 14% gains from a custom thermal solution. It's one example of innovations beyond standard air cooling that systems builders are applying to their NVIDIA MGX designs to take the performance of Hopper GPUs to new heights.

Today, MLCommons published results of the industry-standard MLPerf v4.0 benchmark for inference. Intel's results for Intel Gaudi 2 accelerators and 5th Gen Intel Xeon Scalable processors with Intel Advanced Matrix Extensions (Intel AMX) reinforce the company's commitment to bring "AI Everywhere" with a broad portfolio of competitive solutions. The Intel Gaudi 2 AI accelerator remains the only benchmarked alternative to Nvidia H100 for generative AI (GenAI) performance and provides strong performance-per-dollar. Further, Intel remains the only server CPU vendor to submit MLPerf results. Intel's 5th Gen Xeon results improved by an average of 1.42x compared with 4th Gen Intel Xeon processors' results in MLPerf Inference v3.1.

"We continue to improve AI performance on industry-standard benchmarks across our portfolio of accelerators and CPUs. Today's results demonstrate that we are delivering AI solutions that deliver to our customers' dynamic and wide-ranging AI requirements. Both Intel Gaudi and Xeon products provide our customers with options that are ready to deploy and offer strong price-to-performance advantages," said Zane Ball, Intel corporate vice president and general manager, DCAI Product Management.

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.

NVIDIA today announced its next-generation AI supercomputer—the NVIDIA DGX SuperPOD powered by NVIDIA GB200 Grace Blackwell Superchips—for processing trillion-parameter models with constant uptime for superscale generative AI training and inference workloads.

Featuring a new, highly efficient, liquid-cooled rack-scale architecture, the new DGX SuperPOD is built with NVIDIA DGX GB200 systems and provides 11.5 exaflops of AI supercomputing at FP4 precision and 240 terabytes of fast memory—scaling to more with additional racks.

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.

GIGABYTE Technology and Giga Computing, a subsidiary of GIGABYTE and an industry leader in enterprise solutions, will showcase their solutions at the GIGABYTE booth #1224 at NVIDIA GTC, a global AI developer conference running through March 21. This event will offer GIGABYTE the chance to connect with its valued partners and customers, and together explore what the future in computing holds.

The GIGABYTE booth will focus on GIGABYTE's enterprise products that demonstrate AI training and inference delivered by versatile computing platforms based on NVIDIA solutions, as well as direct liquid cooling (DLC) for improved compute density and energy efficiency. Also not to be missed at the NVIDIA booth is the MGX Pavilion, which features a rack of GIGABYTE servers for the NVIDIA GH200 Grace Hopper Superchip architecture.

TSMC is experiencing unprecedented demand from AI chip customers—unnamed parties have (fancifully) requested the construction of entirely new fabrication facilities. Taiwan's leading semiconductor contract manufacturer seems to concentrating on "sensible" expansions, mainly in the area of CoWoS packaging output—according to an Economic Daily report, company leadership and local government were negotiating over the construction of four new advanced packaging plants. Insiders propose that plans have been revised—an investment in excess of 500 billion yuan ($16 billion) will enable the founding of six new CoWoS-focused facilities. TSMC is expected to make an official announcement next month—industry moles reckon that construction work will start in April. Two (of the six total) advanced packaging plants could become fully operational before the conclusion of 2024.

Lately, TSMC has initiated an ambitious recruitment drive—targeting around 6000 new workers. A touring entity is tasked with the attraction of "talents with high enthusiasm for semiconductors." The majority of new recruits are likely heading to new or expanded Taiwan-based facilities. The Economic Daily report proposes that Chiayi City's technological hub will play host to TSMC's new CoWoS packaging plants. A DigiTimes Asia news piece (from January) posited that TSMC leadership anticipates CoWoS output reaching 44,000 units by the end of 2024. This predicted tally could grow, thanks to the (rumored) activation of additional factories. CoWoS packaging is considered to be a vital aspect of AI accelerators—insiders believe that TSMC's latest investment will boost production of NVIDIA H100 GPUs. The combined output of six new CoWoS plants will assist greatly in the creation of next-gen B100 chips.

TrendForce highlights the current landscape of the HBM market, which as of early 2024, is primarily focused on HBM3. NVIDIA's upcoming B100 or H200 models will incorporate advanced HBM3e, signaling the next step in memory technology. The challenge, however, is the supply bottleneck caused by both CoWoS packaging constraints and the inherently long production cycle of HBM—extending the timeline from wafer initiation to the final product beyond two quarters.

The current HBM3 supply for NVIDIA's H100 solution is primarily met by SK hynix, leading to a supply shortfall in meeting burgeoning AI market demands. Samsung's entry into NVIDIA's supply chain with its 1Znm HBM3 products in late 2023, though initially minor, signifies its breakthrough in this segment.

Stability AI, the developers behind the popular Stable Diffusion generative AI model, have run some first-party performance benchmarks for Stable Diffusion 3 using popular data-center AI GPUs, including the NVIDIA H100 "Hopper" 80 GB, A100 "Ampere" 80 GB, and Intel's Gaudi2 96 GB accelerator. Unlike the H100, which is a super-scalar CUDA+Tensor core GPU; the Gaudi2 is purpose-built to accelerate generative AI and LLMs. Stability AI published its performance findings in a blog post, which reveals that the Intel Gaudi2 96 GB is posting a roughly 56% higher performance than the H100 80 GB.

With 2 nodes, 16 accelerators, and a constant batch size of 16 per accelerator (256 in all), the Intel Gaudi2 array is able to generate 927 images per second, compared to 595 images for the H100 array, and 381 images per second for the A100 array, keeping accelerator and node counts constant. Scaling things up a notch to 32 nodes, and 256 accelerators or a batch size of 16 per accelerator (total batch size of 4,096), the Gaudi2 array is posting 12,654 images per second; or 49.4 images per-second per-device; compared to 3,992 images per second or 15.6 images per-second per-device for the older-gen A100 "Ampere" array.

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."

Supermicro, Inc. (NASDAQ: SMCI), a Total IT Solution Provider for AI, Cloud, Storage, and 5G/Edge, delivers an expanded portfolio of purpose-built infrastructure solutions to accelerate performance and increase efficiency in 5G and telecom workloads. With one of the industry's most diverse offerings, Supermicro enables customers to expand public and private 5G infrastructures with improved performance per watt and support for new and innovative AI applications. As a long-term advocate of open networking platforms and a member of the O-RAN Alliance, Supermicro's portfolio incorporates systems featuring 5th Gen Intel Xeon processors, AMD EPYC 8004 Series processors, and the NVIDIA Grace Hopper Superchip.

"Supermicro is expanding our broad portfolio of sustainable and state-of-the-art servers to address the demanding requirements of 5G and telco markets and Edge AI," said Charles Liang, president and CEO of Supermicro. "Our products are not just about technology, they are about delivering tangible customer benefits. We quickly bring data center AI capabilities to the network's edge using our Building Block architecture. Our products enable operators to offer new capabilities to their customers with improved performance and lower energy consumption. Our edge servers contain up to 2 TB of high-speed DDR5 memory, 6 PCIe slots, and a range of networking options. These systems are designed for increased power efficiency and performance-per-watt, enabling operators to create high-performance, customized solutions for their unique requirements. This reassures our customers that they are investing in reliable and efficient solutions."

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.

NVIDIA today announced that Australia's Pawsey Supercomputing Research Centre will add the NVIDIA CUDA Quantum platform accelerated by NVIDIA Grace Hopper Superchips to its National Supercomputing and Quantum Computing Innovation Hub, furthering its work driving breakthroughs in quantum computing.

Researchers at the Perth-based center will leverage CUDA Quantum - an open-source hybrid quantum computing platform that features powerful simulation tools, and capabilities to program hybrid CPU, GPU and QPU systems - as well as, the NVIDIA cuQuantum software development kit of optimized libraries and tools for accelerating quantum computing workflows. The NVIDIA Grace Hopper Superchip - which combines the NVIDIA Grace CPU and Hopper GPU architectures - provides extreme performance to run high-fidelity and scalable quantum simulations on accelerators and seamlessly interface with future quantum hardware infrastructure.

GIGABYTE, a global leader in high-performance computing solutions, collaborates with industry partner Xenon at SupercomputingAsia 2024, held at the Sydney International Convention and Exhibition Centre from February 19 to 22. This collaboration showcases cutting-edge technologies, offering diverse solutions that redefine the high-performance computing landscape.

GIGABYTE's Highlights at SCA 2024
At booth 19, GIGABYTE presents the G593-SD0, our flagship AI server, and the industry's first Nvidia-certified HGX H100 8-GPU Server. Equipped with 4th/5th Gen Intel Xeon Scalable Processors, it incorporates GIGABYTE's thermal design, ensuring optimal performance within its density-optimized 5U server chassis, pushing the boundaries of AI computing. Additionally, GIGABYTE introduces the 2U 4-node H263-S62 server, designed for 4th Gen Intel Xeon Scalable Processors and now upgraded to the latest 5th Gen, tailored for hybrid and private cloud applications. It features a DLC (Direct Liquid Cooling) solution to efficiently manage heat generated by high-performance computing. Also on display is the newly released W773-W80 workstation, supporting the latest NVIDIA RTX 6000 Ada and catering to CAD, DME, research, data and image analysis, and SMB private cloud applications. At SCA 2024, explore our offerings, including rackmount servers and motherboards, reflecting GIGABYTE's commitment to innovative and reliable solutions. This offers a valuable opportunity to discuss your IT infrastructure requirements with our sales and consulting teams, supported by GIGABYTE and Xenon in Australia.

Providing a peek at the architecture powering advanced AI factories, NVIDIA released a video that offers the first public look at Eos, its latest data-center-scale supercomputer. An extremely large-scale NVIDIA DGX SuperPOD, Eos is where NVIDIA developers create their AI breakthroughs using accelerated computing infrastructure and fully optimized software. Eos is built with 576 NVIDIA DGX H100 systems, NVIDIA Quantum-2 InfiniBand networking and software, providing a total of 18.4 exaflops of FP8 AI performance. Revealed in November at the Supercomputing 2023 trade show, Eos—named for the Greek goddess said to open the gates of dawn each day—reflects NVIDIA's commitment to advancing AI technology.

Eos Supercomputer Fuels Innovation
Each DGX H100 system is equipped with eight NVIDIA H100 Tensor Core GPUs. Eos features a total of 4,608 H100 GPUs. As a result, Eos can handle the largest AI workloads to train large language models, recommender systems, quantum simulations and more. It's a showcase of what NVIDIA's technologies can do, when working at scale. Eos is arriving at the perfect time. People are changing the world with generative AI, from drug discovery to chatbots to autonomous machines and beyond. To achieve these breakthroughs, they need more than AI expertise and development skills. They need an AI factory—a purpose-built AI engine that's always available and can help ramp their capacity to build AI models at scale Eos delivers. Ranked No. 9 in the TOP 500 list of the world's fastest supercomputers, Eos pushes the boundaries of AI technology and infrastructure.

NVIDIA is reportedly working to set up a new business unit focused on designing semi-custom chips for some of its largest data-center customers, Reuters reports. NVIDIA dominates the AI HPC processor market, although even its biggest customers are having to shop from its general lineup of A100 series and H100 series HPC processors. There are reports of some of these customers venturing out of the NVIDIA fold, wanting to develop their own AI processor designs. It is to cater to exactly this segment that NVIDIA is setting up the new unit.

A semi-custom chip isn't just a bespoke chip designed to a customer's specifications. It is co-developed by NVIDIA and its customer, using mainly NVIDIA IP blocks, but also integrating some third-party IP blocks the customer may want; and more importantly, approach semiconductor fabrication companies such as TSMC, Samsung, or Intel Foundry Services as separate entities from NVIDIA for their wafer allocation. For example, a company like Google may have a certain amount of wafer pre-allocation with TSMC (eg: for its Tensor SoCs powering the Pixel smartphones), which it may want to tap into for a semi-custom AI HPC processor for its cloud business. NVIDIA assesses a $30 billion TAM for this specific business unit—that's all its current customers wanting to pursue their own AI processor projects, who will now be motivated to stick to NVIDIA.

Lenovo (HKSE: 992) (ADR: LNVGY) has today announced that the General Purpose Partition of the MareNostrum 5, a new pre-exascale supercomputer running on Lenovo's HPC infrastructure, has been classified as the top x86 general-purpose cluster on the recently published TOP500 list of the most powerful supercomputers globally.

Officially inaugurated at Barcelona Supercomputing Center on December 21st, MareNostrum 5 has been built for the European High Performance Computing Joint Undertaking (EuroHPC JU). The pre-exascale supercomputer will bolster the EU's mission to provide Europe with the most advanced supercomputing technology and accelerate the capacity for artificial intelligence (AI) research, enabling new scientific advances that will help solve global challenges. It aims to empower a wide range of complex HPC-specific applications, from climate research and engineering to material science and earth sciences, adeptly handling tasks that extend beyond the capabilities of cloud computing.

OpenAI is reported to be initiating R&D on a proprietary AI processing solution—the research organization's CEO, Sam Altman, has commented on the in-efficient operation of datacenters running NVIDIA H100 and A100 GPUs. He foresees a future scenario where his company becomes less reliant on Team Green's off-the-shelf AI-crunchers, with a deployment of bespoke AI processors. A short Reuters interview also underlined Altman's desire to find alternatives sources of power: "It motivates us to go invest more in (nuclear) fusion." The growth of artificial intelligence industries has put an unprecedented strain on energy providers, so tech firms could be semi-forced into seeking out frugal enterprise hardware.

The Financial Times has followed up on last week's Bloomberg report of OpenAI courting investment partners in the Middle East. FT's news piece alleges that Altman is in talks with billionaire businessman Sheikh Tahnoon bin Zayed al-Nahyan, a very well connected member of the United Arab Emirates Royal Family. OpenAI's leadership is reportedly negotiating with TSMC—The Financial Times alleges that Taiwan's top chip foundry is an ideal manufacturing partner. This revelation contradicts Bloomberg's recent reports of a potential custom OpenAI AI chip venture involving purpose-built manufacturing facilities. The whole project is said to be at an early stage of development, so Altman and his colleagues are most likely exploring a variety of options.

LaminiAI appears to be one of AMD's first customers to receive a bulk order of Instinct MI300X GPUs—late last week, Sharon Zhou (CEO and co-founder) posted about the "next batch of LaminiAI LLM Pods" up and running with Team Red's cutting-edge CDNA 3 series accelerators inside. Her short post on social media stated: "rocm-smi...like freshly baked bread, 8x MI300X is online—if you're building on open LLMs and you're blocked on compute, lmk. Everyone should have access to this wizard technology called LLMs."

An attached screenshot of a ROCm System Management Interface (ROCm SMI) session showcases an individual Pod configuration sporting eight Instinct MI300X GPUs. According to official blog entries, LaminiAI has utilized bog-standard MI300 accelerators since 2023, so it is not surprising to see their partnership continue to grow with AMD. Industry predictions have the Instinct MI300X and MI300A models placed as great alternatives to NVIDIA's dominant H100 "Hopper" series—AMD stock is climbing due to encouraging financial analyst estimations.

Mark Zuckerberg has shared some interesting insights about Meta's AI infrastructure buildout, which is on track to include an astonishing number of NVIDIA H100 Tensor GPUs. In the post on Instagram, Meta's CEO has noted the following: "We're currently training our next-gen model Llama 3, and we're building massive compute infrastructure to support our future roadmap, including 350k H100s by the end of this year -- and overall almost 600k H100s equivalents of compute if you include other GPUs." That means that the company will enhance its AI infrastructure with 350,000 H100 GPUs on top of the existing GPUs, which is equivalent to 250,000 H100 in terms of computing power, for a total of 600,000 H100-equivalent GPUs.

The raw number of GPUs installed comes at a steep price. With the average selling price of H100 GPU nearing 30,000 US dollars, Meta's investment will settle the company back around $10.5 billion. Other GPUs should be in the infrastructure, but most will comprise the NVIDIA Hopper family. Additionally, Meta is currently training the LLama 3 AI model, which will be much more capable than the existing LLama 2 family and will include better reasoning, coding, and math-solving capabilities. These models will be open-source. Later down the pipeline, as the artificial general intelligence (AGI) comes into play, Zuckerberg has noted that "Our long term vision is to build general intelligence, open source it responsibly, and make it widely available so everyone can benefit." So, expect to see these models in the GitHub repositories in the future.

Indian data center operator Yotta is reportedly set to spend big with another placed with NVIDIA—a recent Reuters article outlines a $500 million purchase of Team Green AI GPUs. Yotta is in the process of upgrading its AI Cloud infrastructure, and their total tally for this endeavor (involving Hopper and newer Grace Hopper models) is likely to hit $1 billion. An official company statement from December confirmed the existence of an extra procurement of GPUs, but they did not provide any details regarding budget or hardware choices at that point in time. Reuters contacted Sunil Gupta, Yotta's CEO, last week for a comment on the situation. The co-founder elaborated: "that the order would comprise nearly 16,000 of NVIDIA's artificial intelligence chips H100 and GH200 and will be placed by March 2025."

Team Green is ramping up its embrace of the Indian data center market, as US sanctions have made it difficult to conduct business with enterprise customers in nearby Chinese territories. Reuters state that Gupta's firm (Yotta) is: "part of Indian billionaire Niranjan Hiranandani's real estate group, (in turn) a partner firm for NVIDIA in India and runs three data centre campuses, in Mumbai, Gujarat and near New Delhi." Microsoft, Google and Amazon are investing heavily in cloud and data centers situated in India. Shankar Trivedi, an NVIDIA executive, recently attended Vibrant Gujarat Global Summit—the article's reporter conducted a brief interview with him. Trivedi stated that Yotta is targeting a March 2024 start for a new NVIDIA-powered AI data center located in the region's tech hub: Gujarat International Finance Tec-City.

During the recent IEDM conference, TSMC previewed its process roadmap for delivering next-generation chip packages packing over one trillion transistors by 2030. This aligns with similar long-term visions from Intel. Such enormous transistor counts will come through advanced 3D packaging of multiple chipsets. But TSMC also aims to push monolithic chip complexity higher, ultimately enabling 200 billion transistor designs on a single die. This requires steady enhancement of TSMC's planned N2, N2P, N1.4, and N1 nodes, which are slated to arrive between now and the end of the decade. While multi-chipset architectures are currently gaining favor, TSMC asserts both packaging density and raw transistor density must scale up in tandem. Some perspective on the magnitude of TSMC's goals include NVIDIA's 80 billion transistor GH100 GPU—among today's largest chips, excluding wafer-scale designs from Cerebras.

Yet TSMC's roadmap calls for more than doubling that, first with over 100 billion transistor monolithic designs, then eventually 200 billion. Of course, yields become more challenging as die sizes grow, which is where advanced packaging of smaller chiplets becomes crucial. Multi-chip module offerings like AMD's MI300X and Intel's Ponte Vecchio already integrate dozens of tiles, with PVC having 47 tiles. TSMC envisions this expansion to chip packages housing more than a trillion transistors via its CoWoS, InFO, 3D stacking, and many other technologies. While the scaling cadence has recently slowed, TSMC remains confident in achieving both packaging and process breakthroughs to meet future density demands. The foundry's continuous investment ensures progress in unlocking next-generation semiconductor capabilities. But physics ultimately dictates timelines, no matter how aggressive the roadmap.