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.

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.

The ecosystem around NVIDIA's technologies has always been verdant—but this is absurd. After a stunning premiere at the World Economic Forum in Davos, immersive artworks based on Refit Anadol Studio's Large Nature Model will come to the U.S. for the first time at NVIDIA GTC. Offering a deep dive into the synergy between AI and the natural world, Anadol's multisensory work, "Large Nature Model: A Living Archive," will be situated prominently on the main concourse of the San Jose Convention Center, where the global AI event is taking place, from March 18-21.

Fueled by NVIDIA's advanced AI technology, including powerful DGX A100 stations and high-performance GPUs, the exhibit offers a captivating journey through our planet's ecosystems with stunning visuals, sounds and scents. These scenes are rendered in breathtaking clarity across screens with a total output of 12.5 million pixels, immersing attendees in an unprecedented digital portrayal of Earth's ecosystems. Refik Anadol, recognized by The Economist as "the artist of the moment," has emerged as a key figure in AI art. His work, notable for its use of data and machine learning, places him at the forefront of a generation pushing the boundaries between technology, interdisciplinary research and aesthetics. Anadol's influence reflects a wider movement in the art world towards embracing digital innovation, setting new precedents in how art is created and experienced.

Intel's "AI Everywhere" hype campaign has generated the most noise in mainstream and enterprise segments. Team Blue's Gaudi—a family of deep learning accelerators—does not hit the headlines all that often. Their current generation model, Gaudi 2, is overshadowed by Team Green and Red alternatives—according to Intel's official marketing spiel: "it performs competitively on deep learning training and inference, with up to 2.4x faster performance than NVIDIA A100." Habana, an Intel subsidiary, has been working on optimizing Large Language Model (LLM) inference on Gaudi 1 and 2 for a while—their co-operation with Hugging Face has produced impressive results, as of late February. Siddhant Jagtap, an Intel Data Scientist, has demonstrated: "how easy it is to generate text with the Llama 2 family of models (7b, 13b and 70b) using Optimum Habana and a custom pipeline class."

Jagtap reckons that folks will be able to: "run the models with just a few lines of code" on Gaudi 2 accelerators—additionally, Intel's hardware is capable of accepting single and multiple prompts. The custom pipeline class: "has been designed to offer great flexibility and ease of use. Moreover, it provides a high level of abstraction and performs end-to-end text-generation which involves pre-processing and post-processing." His article/blog outlines various prerequisites and methods of getting Llama 2 text generation up and running on Gaudi 2. Jagtap concluded that Habana/Intel has: "presented a custom text-generation pipeline on Intel Gaudi 2 AI accelerator that accepts single or multiple prompts as input. This pipeline offers great flexibility in terms of model size as well as parameters affecting text-generation quality. Furthermore, it is also very easy to use and to plug into your scripts, and is compatible with LangChain." Hugging Face reckons that Gaudi 2 delivers roughly twice the throughput speed of NVIDIA A100 80 GB in both training and inference scenarios. Intel has teased third generation Gaudi accelerators—industry watchdogs believe that next-gen solutions are designed to compete with Team Green H100 AI GPUs.

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.

Huawei's subsidiary HiSilicon has made significant strides in the independent R&D of AI chips, launching the next-gen Ascend 910B. These chips are utilized not only in Huawei's public cloud infrastructure but also sold to other Chinese companies. This year, Baidu ordered over a thousand Ascend 910B chips from Huawei to build approximately 200 AI servers. Additionally, in August, Chinese company iFlytek, in partnership with Huawei, released the "Gemini Star Program," a hardware and software integrated device for exclusive enterprise LLMs, equipped with the Ascend 910B AI acceleration chip, according to TrendForce's research.

TrendForce conjectures that the next-generation Ascend 910B chip is likely manufactured using SMIC's N+2 process. However, the production faces two potential risks. Firstly, as Huawei recently focused on expanding its smartphone business, the N+2 process capacity at SMIC is almost entirely allocated to Huawei's smartphone products, potentially limiting future capacity for AI chips. Secondly, SMIC remains on the Entity List, possibly restricting access to advanced process equipment.

TrendForce's latest research into the HBM market indicates that NVIDIA plans to diversify its HBM suppliers for more robust and efficient supply chain management. Samsung's HBM3 (24 GB) is anticipated to complete verification with NVIDIA by December this year. The progress of HBM3e, as outlined in the timeline below, shows that Micron provided its 8hi (24 GB) samples to NVIDIA by the end of July, SK hynix in mid-August, and Samsung in early October.

Given the intricacy of the HBM verification process—estimated to take two quarters—TrendForce expects that some manufacturers might learn preliminary HBM3e results by the end of 2023. However, it's generally anticipated that major manufacturers will have definite results by 1Q24. Notably, the outcomes will influence NVIDIA's procurement decisions for 2024, as final evaluations are still underway.

NVIDIA today announced it has supercharged the world's leading AI computing platform with the introduction of the NVIDIA HGX H200. Based on NVIDIA Hopper architecture, the platform features the NVIDIA H200 Tensor Core GPU with advanced memory to handle massive amounts of data for generative AI and high performance computing workloads.

The NVIDIA H200 is the first GPU to offer HBM3e - faster, larger memory to fuel the acceleration of generative AI and large language models, while advancing scientific computing for HPC workloads. With HBM3e, the NVIDIA H200 delivers 141 GB of memory at 4.8 terabytes per second, nearly double the capacity and 2.4x more bandwidth compared with its predecessor, the NVIDIA A100. H200-powered systems from the world's leading server manufacturers and cloud service providers are expected to begin shipping in the second quarter of 2024.

According to sources close to The Information, Microsoft is supposed to unveil details about its upcoming custom silicon design for accelerating AI workloads. Allegedly, the incoming chip announcement is scheduled for November during Microsoft's annual Ignite conference. Held in Seattle from November 14 to 17, the conference is supposed to show all of the work that the company has been doing in the field of AI. The alleged launch of an AI chip will undoubtedly take center stage in the announcement, as the demand for AI accelerators has been so great that companies can't get their hands on GPUs. The sector is mainly dominated by NVIDIA, with its H100 and A100 GPUs powering most of the AI infrastructure worldwide.

With the launch of a custom AI chip codenamed Athena, Microsoft hopes to match or beat the performance of NVIDIA's offerings and reduce the cost of AI infrastructure. As the price of H100 GPU can get up to 30,000 US Dollars, building a data center filled with H100s can cost hundreds of millions. The cost could be winded down using homemade chips, and Microsoft could be less dependent on NVIDIA to provide the backbone of AI servers needed in the coming years. Nevertheless, we are excited to see what the company has prepared, and we will report on the Microsoft Ignite announcement in November.

TSMC Chairman Mark Liu was asked to comment on all things artificial intelligence-related at the SEMICON Taiwan 2023 industry event. According to a Nikkei Asia report, he foresees supply constraints lasting until the tail end of 2024: "It's not the shortage of AI chips. It's the shortage of our chip-on-wafer-on-substrate (COWOS) capacity...Currently, we can't fulfill 100% of our customers' needs, but we try to support about 80%. We think this is a temporary phenomenon. After our expansion of advanced chip packaging capacity, it should be alleviated in one and a half years." He cites a recent and very "sudden" spike in demand for COWOS, with numbers tripling within the span of a year. Market leader NVIDIA relies on TSMC's advanced packaging system—most notably with the production of highly-prized A100 and H100 series Tensor Core compute GPUs.

These issues are deemed a "temporary" problem—it could take around 18 months to eliminate production output "bottlenecks." TSMC is racing to bolster its native activities with new facilities—plans for a new $2.9 billion advanced chip packaging plant (in Miaoli County) were disclosed during summer time. Liu reckons that industry-wide innovation is necessary to meet growing demand through new methods to "connect, package and stack chips." Liu elaborated: "We are now putting together many chips into a tightly integrated massive interconnect system. This is a paradigm shift in semiconductor technology integration." The TSMC boss reckons that processing units fielding over one trillion transistors are viable within the next decade: "it's through packaging with multiple chips that this could be possible.".

We're at a once-in-a-generation inflection point in computing. The traditional ways of designing and building computing infrastructure are no longer adequate for the exponentially growing demands of workloads like generative AI and LLMs. In fact, the number of parameters in LLMs has increased by 10x per year over the past five years. As a result, customers need AI-optimized infrastructure that is both cost effective and scalable.

For two decades, Google has built some of the industry's leading AI capabilities: from the creation of Google's Transformer architecture that makes gen AI possible, to our AI-optimized infrastructure, which is built to deliver the global scale and performance required by Google products that serve billions of users like YouTube, Gmail, Google Maps, Google Play, and Android. We are excited to bring decades of innovation and research to Google Cloud customers as they pursue transformative opportunities in AI. We offer a complete solution for AI, from computing infrastructure optimized for AI to the end-to-end software and services that support the full lifecycle of model training, tuning, and serving at global scale.

Google Cloud Next—Google Cloud and NVIDIA today announced new AI infrastructure and software for customers to build and deploy massive models for generative AI and speed data science workloads.

In a fireside chat at Google Cloud Next, Google Cloud CEO Thomas Kurian and NVIDIA founder and CEO Jensen Huang discussed how the partnership is bringing end-to-end machine learning services to some of the largest AI customers in the world—including by making it easy to run AI supercomputers with Google Cloud offerings built on NVIDIA technologies. The new hardware and software integrations utilize the same NVIDIA technologies employed over the past two years by Google DeepMind and Google research teams.

Liu Qingfeng, the founder and chairman of Chinese AI firm iFlytek (or HKUST Xunfei according to ITHome) shared his opinions of incoming Huawei GPU technology at this year's Yabuli Entrepreneurs Forum. His team has been collaborating with key figures at the multinational technology corporation on a product that he reckons is just as capable as NVIDIA's very mature A100 tensor core accelerator. Liu referred to the model as a "compute GPU" which implies that this is an all-new product—Huawei has kept quiet on the AI hardware front since the 2019 launch of its Ascend 910 AI accelerator, so the iFlytek presentation has hinted about Huawei's ambitions to take on Team Green within the Chinese deep learning and artificial intelligence market sector.

NVIDIA's latest financial report for FY2Q24 reveals that its data center business reached US$10.32 billion—a QoQ growth of 141% and YoY increase of 171%. The company remains optimistic about its future growth. TrendForce believes that the primary driver behind NVIDIA's robust revenue growth stems from its data center's AI server-related solutions. Key products include AI-accelerated GPUs and AI server HGX reference architecture, which serve as the foundational AI infrastructure for large data centers.

TrendForce further anticipates that NVIDIA will integrate its software and hardware resources. Utilizing a refined approach, NVIDIA will align its high-end, mid-tier, and entry-level GPU AI accelerator chips with various ODMs and OEMs, establishing a collaborative system certification model. Beyond accelerating the deployment of CSP cloud AI server infrastructures, NVIDIA is also partnering with entities like VMware on solutions including the Private AI Foundation. This strategy extends NVIDIA's reach into the edge enterprise AI server market, underpinning steady growth in its data center business for the next two years.

Inventec, a global leader in high-powered servers headquartered in Taiwan, is launching its cutting-edge C805G6 server for data centers based on AMD's newest 4th Gen EPYC platform—a major innovation in computing power that provides double the operating efficiency of previous platforms. These innovations are timely, as the industry worldwide faces converse challenges—on one hand, a growing need to reduce carbon footprints and power consumption, while, on the other hand, the push for ever higher computing power and performance for AI. In fact, in 2022 MIT found that improving a machine learning model tenfold will require a 10,000-fold increase in computational requirements.

Addressing both pain points, George Lin, VP of Business Unit VI, Inventec Enterprise Business Group (Inventec EBG) notes that, "Our latest C805G6 data center solution represents an innovation both for the present and the future, setting the standard for performance, energy efficiency, and security while delivering top-notch hardware for powering AI workloads."

TrendForce reports that the HBM (High Bandwidth Memory) market's dominant product for 2023 is HBM2e, employed by the NVIDIA A100/A800, AMD MI200, and most CSPs' (Cloud Service Providers) self-developed accelerator chips. As the demand for AI accelerator chips evolves, manufacturers plan to introduce new HBM3e products in 2024, with HBM3 and HBM3e expected to become mainstream in the market next year.

The distinctions between HBM generations primarily lie in their speed. The industry experienced a proliferation of confusing names when transitioning to the HBM3 generation. TrendForce clarifies that the so-called HBM3 in the current market should be subdivided into two categories based on speed. One category includes HBM3 running at speeds between 5.6 to 6.4 Gbps, while the other features the 8 Gbps HBM3e, which also goes by several names including HBM3P, HBM3A, HBM3+, and HBM3 Gen2.

NVIDIA could be adjusting its enterprise-grade GPU production strategies for the Chinese market, according to an article published by MyDriver—despite major sanctions placed on semiconductor imports, Team Green is doing plenty of business with tech firms operating in the region thanks to an uptick in AI-related activities. NVIDIA offers two market specific accelerator models that have been cut down to conform to rules and regulations—the more powerful and expensive (250K RMB/~$35K) H800 is an adaptation of the western H100 GPU, while the A800 is a legal market alternative to the older A100.

The report proposes that NVIDIA is considering plans to reduce factory output of the A800 (sold for 100K RMB/~$14K per unit), so clients will be semi-forced into purchasing the higher-end H800 model instead (if they require a significant number of GPUs). The A800 seems to be the more popular choice for the majority of companies at the moment, with the heavy hitters—Alibaba, Baidu, Tencent, Jitwei and ByteDance—flexing their spending muscles and splurging on mixed shipments of the two accelerators. By limiting supplies of the lesser A800, Team Green could be generating more profit by prioritizing the more expensive (and readily available) model.

Intel has responded to the high demand for advanced chips in mainland China by bringing its processor, the Gaudi2, to the market. This move comes as the country grapples with US export restrictions, leading to a thriving market for smuggled NVIDIA GPUs. At a press conference in Beijing, Intel presented the Gaudi2 processor as an alternative to NVIDIA's A100 GPU, widely used for training AI systems. Despite US export controls, Intel recognizes the importance of the Chinese market, with 27 percent of its 2022 revenue generated from China. NVIDIA has also tried to comply with restrictions by offering modified versions of its GPUs, but limited supplies have driven the demand for smuggled GPUs. Intel's Gaudi2 aims to provide Chinese companies with various hardware options and bolster their ability to deploy AI through cloud and smart-edge technologies. By partnering with Inspur Group, a major AI server manufacturer, Intel plans to build Gaudi2-powered machines tailored explicitly for the Chinese market.

China's AI ambitions face potential challenges as the US government considers restricting Chinese companies access to American cloud computing services. This move could impede the utilization of advanced AI chips by major players like Amazon Web Services and Microsoft for their Chinese clients. Additionally, there are reports of a potential expansion of the US export ban to include NVIDIA's A800 GPU. As China continues to push forward with its AI development projects, Intel's introduction of the Gaudi2 processor helps country's demand for advanced chips. Balancing export controls and technological requirements within this complex trade landscape remains a crucial task for both companies and governments involved in the Chinese AI industry.

NVIDIA RTX is spinning new cycles for designs. Trek Bicycle is using GPUs to bring design concepts to life. The Wisconsin-based company, one of the largest bicycle manufacturers in the world, aims to create bikes with the highest-quality craftsmanship. With its new partner Lidl, an international retailer chain, Trek Bicycle also owns a cycling team, now called Lidl-Trek. The team is competing in the annual Tour de France stage race on Trek Bicycle's flagship lineup, which includes the Emonda, Madone and Speed Concept. Many of the team's accessories and equipment, such as the wheels and road race helmets, were also designed at Trek.

Bicycle design involves complex physics—and a key challenge is balancing aerodynamic efficiency with comfort and ride quality. To address this, the team at Trek is using NVIDIA A100 Tensor Core GPUs to run high-fidelity computational fluid dynamics (CFD) simulations, setting new benchmarks for aerodynamics in a bicycle that's also comfortable to ride and handles smoothly. The designers and engineers are further enhancing their workflows using NVIDIA RTX technology in Dell Precision workstations, including the NVIDIA RTX A5500 GPU, as well as a Dell Precision 7920 running dual RTX A6000 GPUs.

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.

The top technology companies in China have been ordering a lot of NVIDIA enterprise-grade GPUs, even though U.S. sanctions have prevented the shipment of A100 and H100 models (plus AMD's MI250 Instinct accelerator) to the nation in recent times. ByteDance - best known for developing TikTok - managed to grab plenty of Ampere enterprise units prior to last Autumn's cutoff period, and has continued to purchase Team Green's H800 GPU, which is a cut-down version of the H100 flagship. Smaller outfits are relying on less direct sources to acquire HBC GPUs—according to a Reuters investigative article, international trade restrictions have created a thriving black market for "top-end NVIDIA AI chips."

Their reporters carried out some on-site sleuthing: "Visiting the famed Huaqiangbei electronics area in the southern Chinese city of Shenzhen is a good bet - in particular, the SEG Plaza skyscraper whose first 10 floors are crammed with shops selling everything from camera parts to drones. The chips are not advertised but asking discreetly works...They don't come cheap. Two vendors there, who spoke with Reuters in person on condition of anonymity, said they could provide small numbers of A100 artificial intelligence chips made by the U.S. chip designer, pricing them at $20,000 a piece - double the usual price."

TikTok developer ByteDance, and other major Chinese tech firms including Tencent, Alibaba and Baidu are reported (by local media) to be snapping up lots of NVIDIA HPC GPUs, with even more orders placed this year. ByteDance is alleged to have spent enough on new products in 2023 to match the expenditure of the entire Chinese tech market on similar NVIDIA purchases for FY2022. According to news publication Jitwei, ByteDance has placed orders totaling $1 billion so far this year with Team Green—the report suggests that a mix of A100 and H800 GPU shipments have been sent to the company's mainland data centers.

The older Ampere-based A100 units were likely ordered prior to trade sanctions enforced on China post-August 2022, with further wiggle room allowed—meaning that shipments continued until September. The H800 GPU is a cut-down variant of 2022's flagship "Hopper" H100 model, designed specifically for the Chinese enterprise market—with reduced performance in order to meet export restriction standards. The H800 costs around $10,000 (average sale price per accelerator) according to Tom's Hardware, so it must offer some level of potency at that price. ByteDance has ordered roughly 100,000 units—with an unspecified split between H800 and A100 stock. Despite the development of competing HPC products within China, it seems that the nation's top-flight technology companies are heading directly to NVIDIA to acquire the best-of-the-best and highly mature AI processing hardware.

Inference software enables shift to NVIDIA A100 Tensor Core GPUs, delivering 7x throughput for the search giant. Jiusheng Chen's team just got accelerated. They're delivering personalized ads to users of Microsoft Bing with 7x throughput at reduced cost, thanks to NVIDIA Triton Inference Server running on NVIDIA A100 Tensor Core GPUs. It's an amazing achievement for the principal software engineering manager and his crew.

Tuning a Complex System
Bing's ad service uses hundreds of models that are constantly evolving. Each must respond to a request within as little as 10 milliseconds, about 10x faster than the blink of an eye. The latest speedup got its start with two innovations the team delivered to make AI models run faster: Bang and EL-Attention. Together, they apply sophisticated techniques to do more work in less time with less computer memory. Model training was based on Azure Machine Learning for efficiency.

People agree: accelerated computing is energy-efficient computing. The National Energy Research Scientific Computing Center (NERSC), the U.S. Department of Energy's lead facility for open science, measured results across four of its key high performance computing and AI applications.

They clocked how fast the applications ran and how much energy they consumed on CPU-only and GPU-accelerated nodes on Perlmutter, one of the world's largest supercomputers using NVIDIA GPUs. The results were clear. Accelerated with NVIDIA A100 Tensor Core GPUs, energy efficiency rose 5x on average. An application for weather forecasting logged gains of 9.8x.

Increasing its HPL score from 1.02 Eflop/s in November 2022 to an impressive 1.194 Eflop/s on this list, Frontier was able to improve upon its score after a stagnation between June 2022 and November 2022. Considering exascale was only a goal to aspire to just a few years ago, a roughly 17% increase here is an enormous success. Additionally, Frontier earned a score of 9.95 Eflop/s on the HLP-MxP benchmark, which measures performance for mixed-precision calculation. This is also an increase over the 7.94 EFlop/s that the system achieved on the previous list and nearly 10 times more powerful than the machine's HPL score. Frontier is based on the HPE Cray EX235a architecture and utilizes AMD EPYC 64C 2 GHz processors. It also has 8,699,904 cores and an incredible energy efficiency rating of 52.59 Gflops/watt. It also relies on gigabit ethernet for data transfer.