This year’s Nvidia GTC is packed with noteworthy revelations, including the introduction of the Blackwell B200 GPU. Branded as the ‘world’s most powerful chip’ for AI, it’s designed for its capacity to make AI models with trillions of parameters more accessible to a wider audience.

Jensen Huang, the CEO of Nvidia, inaugurated the company’s annual developer conference with a series of strategic announcements aimed at solidifying Nvidia’s supremacy in the AI sector.

Revolutionizing AI with the new Nvidia chip

Nvidia introduced the B200 GPU, boasting an impressive 20 petaflops of FP4 computing power, thanks to its 208 billion transistors. Additionally, Nvidia unveiled the GB200, which synergizes two B200 GPUs with a single Grace CPU, claiming it can enhance LLM inference workload performance by 30 times while also significantly boosting efficiency. This advancement is said to slash costs and energy usage by as much as 25 times compared to the H100 model.

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Previously, training a model with 1.8 trillion parameters required 8,000 Hopper GPUs and 15 megawatts of power. Now, Nvidia asserts that only 2,000 Blackwell GPUs are needed to achieve the same feat, reducing power consumption to just four megawatts.

The introduction of the GB200 “superchip” alongside the Blackwell B200 GPU marks a significant milestone. Nvidia reports that, in tests using the GPT-3 LLM with 175 billion parameters, the GB200 delivered seven times the performance and quadrupled the training speed of the H100.

A notable upgrade The Verge highlights is the second-generation transformer engine that enhances compute, bandwidth, and model capacity by utilizing four bits per neuron, halving the previous eight. The next-gen NVLink switch is a groundbreaking feature, enabling communication among up to 576 GPUs and providing 1.8 terabytes per second of bidirectional bandwidth. This innovation necessitated the creation of a new network switch chip, boasting 50 billion transistors and 3.6 teraflops of FP8 compute capability.

Bridging the gap: Nvidia’s new suite of software tools

Nvidia has also introduced a suite of software tools designed to streamline the sale and deployment of AI models for businesses, catering to a clientele that includes the globe’s tech behemoths.

These developments underscore Nvidia’s ambition to broaden its influence in the AI inference market, a segment where its chips are not yet predominant, as noted by Joel Hellermark, CEO of Sana.

Nvidia is renowned for its foundational role in training AI models, such as OpenAI’s GPT-4, a process that requires digesting vast data quantities, predominantly undertaken by AI-centric and large tech companies.

However, as businesses of various sizes strive to integrate these foundational models into their operations, Nvidia’s newly released tools aim to simplify the adaptation and execution of diverse AI models on Nvidia hardware.

According to Ben Metcalfe, a venture capitalist and founder of Monochrome Capital, Nvidia’s approach is akin to offering “ready-made meals” instead of gathering ingredients from scratch. This strategy is particularly advantageous for companies that may lack the technical prowess of giants like Google or Uber, enabling them to quickly deploy sophisticated systems.

For instance, ServiceNow used Nvidia’s toolkit to develop a “copilot” for addressing corporate IT challenges, demonstrating the practical applications of Nvidia’s innovations.

Noteworthy is Nvidia’s collaboration with major tech entities such as Microsoft, Google, and Amazon, which will incorporate Nvidia’s tools into their cloud services. However, prominent AI model providers like OpenAI and Anthropic are conspicuously absent from Nvidia’s partnership roster.

Nvidia’s toolkit could significantly bolster its revenue, as part of a software suite priced at US$4,500 annually per Nvidia chip in private data centers or US$1 per hour in cloud data centers.

Reuters suggests that the announcements made at GTC 2024 are pivotal in determining whether Nvidia can sustain its commanding 80% share in the AI chip marketplace.

These developments reflect Nvidia’s evolution from a brand favored by gaming enthusiasts to a tech titan on par with Microsoft, boasting a staggering sales increase to over US$60 billion in its latest fiscal year.

While the B200 chip promises a thirtyfold increase in efficiency for tasks like chatbot responses, Huang remained tight-lipped about its performance in extensive data training and did not disclose pricing details.

Despite the surge in Nvidia’s stock by 240% over the past year, Huang’s announcements did not ignite further enthusiasm in the market, with a slight decline in Nvidia’s stock following the presentation.

Tom Plumb, CEO and portfolio manager at Plumb Funds, a significant investor in Nvidia, remarked that the Blackwell chip’s unveiling was anticipated but reaffirmed Nvidia’s leading edge in graphics processing technology.

Nvidia has revealed that key clients, including Amazon, Google, Microsoft, OpenAI, and Oracle, are expected to incorporate the new chip into their cloud services and AI solutions.

The company is transitioning from selling individual chips to offering complete systems, with its latest model housing 72 AI chips and 36 central processors, exemplifying Nvidia’s comprehensive approach to AI technology deployment.

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Analysts predict a slight dip in Nvidia’s market share in 2024 as competition intensifies and major customers develop their chips, posing challenges to Nvidia’s dominance, especially among budget-conscious enterprise clients.

Despite these challenges, Nvidia’s extensive software offerings, particularly the new microservices, are poised to enhance operational efficiency across various applications, reinforcing its position in the tech industry.

Moreover, Nvidia is expanding into software for simulating the physical world with 3-D models, announcing collaborations with leading design software companies. This move and Nvidia’s capability to stream 3-D worlds to Apple’s Vision Pro headset marks a significant leap forward in immersive technology.

Nvidia’s automotive and industrial ventures

Nvidia also unveiled an innovative line of chips tailored for automotive use, introducing capabilities that enable chatbots to operate within vehicles. The tech giant has further solidified its partnerships with Chinese car manufacturers, announcing that electric vehicle leaders BYD and Xpeng will incorporate its latest chip technology.

Last year, BYD surpassed Tesla, becoming the top electric vehicle producer worldwide. The company plans to adopt Nvidia’s cutting-edge Drive Thor chips, which promise to enhance autonomous driving capabilities and digital functionalities. Nvidia also highlighted that BYD intends to leverage its technology to optimize manufacturing processes and supply chain efficiency.

Additionally, this collaboration will facilitate the creation of virtual showrooms, according to Danny Shapiro, Nvidia’s Vice President for Automotive, during a conference call. Shapiro indicated that BYD vehicles would integrate Drive Thor chips starting next year.

The announcement was part of a broader revelation of partnerships during Nvidia’s GTC developer conference in San Jose, California. Notably, Chinese automakers, including BYD, Xpeng, GAC Aion’s Hyper brand, and autonomous truck developers, have declared their expanded cooperation with Nvidia. Other Chinese brands like Zeekr, a subsidiary of Geely, and Li Auto have also committed to using the Drive Thor technology.

Nvidia extends partnership with BYD (Source – X)

These partnerships reflect a strategic move by Chinese auto brands to leverage advanced technology, offsetting their relatively lower global brand recognition. BYD and its competitors are keen on increasing their market presence in Europe, Southeast Asia, and other regions outside China, positioning themselves against Tesla and other established Western brands in their domestic market.

Shapiro emphasized the considerable number of Chinese automakers and highlighted the supportive regulatory environment and incentives fostering innovation and developing advanced automated driving technologies.

Nvidia also announced several other key partnerships in the automotive and industrial sectors, including a collaboration with U.S. software firm Cerence to adapt large language model AI systems for automotive computing needs. Chinese computer manufacturer Lenovo is also working with Nvidia to deploy large language model technologies.

Another development is Soundhound’s utilization of Nvidia’s technology to create a voice command system for vehicles, enabling users to access information from a virtual owner’s manual through voice commands, marking a step forward in enhancing user interaction with vehicle technology.

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Legal challenges, and market dynamics presents a fascinating narrative that shapes the fortunes of leading corporations. Among these, Nvidia, a titan in the field of AI, has recently been at the center of a noteworthy legal dispute while simultaneously experiencing an unprecedented surge in its market valuation.

This complex scenario provides a rich case study for examining the broader implications for the tech industry, market competition, and the legal frameworks that govern intellectual property rights.

Nvidia found itself embroiled in controversy when three authors—Brian Keene, Abdi Nazemian, and Stewart O’Nan—levied accusations against the company for allegedly using their copyrighted works without permission. These works were purportedly incorporated into a substantial dataset of approximately 196,640 books to advance the capabilities of Nvidia’s NeMo AI platform, a sophisticated system aimed at mimicking human language.

The fallout from these allegations led to the dataset’s removal in October, underscoring the legal complexities surrounding copyright infringement in the digital age.

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A meteoric rise in market valuation

Despite facing this legal hurdle, Nvidia has witnessed a surge in its market valuation, underscoring the intense investor interest in AI technologies. This rise is indicative of the broader trends in the semiconductor industry, where demand for AI chips, particularly those powering popular applications such as ChatGPT, has skyrocketed.

Within a span of nine months, Nvidia’s market value soared from US$1 trillion to over US$2 trillion, surpassing industry giants like Amazon.com, Google’s parent company Alphabet, and Saudi Aramco in the process. This meteoric rise has positioned Nvidia as a formidable contender in the race to become the world’s second-most valuable company, trailing closely behind Apple and Microsoft.

As reported by Reuters, Nvidia’s current market capitalization, standing at approximately US$2.38 trillion, exemplifies the fierce competition at the apex of the global corporate sector. This competitive landscape is not only defined by market valuations but also by the continuous drive for innovation and the development of high-quality products that resonate with consumers and enterprises alike. Apple’s journey to becoming the world’s most valuable company in 2011, bolstered by its array of successful products and services, highlights the critical role of brand loyalty and product innovation in achieving market dominance.

Nvidia’s mastery over legal hurdles and market cap peaks (Source – X)

On the other hand, Microsoft’s ascendance in 2024 to claim the title of the most valuable company globally emphasizes the significance of strategic investments in technology, particularly AI. With over 70% of computers worldwide running on Windows, according to Statcounter, Microsoft’s influence extends beyond its operating system. The company’s diversified portfolio, including the Office Suite, Azure cloud platform, Xbox consoles, and Surface devices, alongside a substantial investment in OpenAI, demonstrates its commitment to shaping the future of technology.

Nvidia’s stronghold over the high-end AI chip market, commanding 80% of the sector, combined with its significant stock performance, has propelled Wall Street to new heights this year. This success story is a testament to the investor enthusiasm for AI technologies, positioning Nvidia and Meta Platforms as leaders in a market increasingly focused on digital innovation.

Industry experts, such as Richard Meckler of Cherry Lane Investments, attribute Nvidia’s robust market performance to the solid fundamentals underpinning its business model and the speculative support from investors. This blend of strong business practices and market speculation has facilitated Nvidia’s steady climb in stock value throughout 2024, even as it faces legal challenges and stiff competition from tech giants like Apple and Microsoft.

Apple’s recent challenges with iPhone sales and the shift in market capitalization rankings underscore the dynamic nature of the tech industry, where companies continually vie for leadership positions. Meanwhile, Nvidia’s competitive forward price-to-earnings ratio and the insights from David Wagner of Aptus Capital Advisors suggest that Nvidia represents an attractively priced stock within the AI narrative, with the potential for significant growth in the coming years.

Facing the peaks: The Nvidia market cap challenges

However, as Nvidia’s stock approaches what some analysts believe to be its peak, the challenges of sustaining rapid growth in the face of increasing market capitalization become apparent. The speculative nature of stock valuations, coupled with the potential for innovation and market expansion, presents a nuanced picture of Nvidia’s future prospects. Should Nvidia continue to surpass analyst expectations, it could maintain or even enhance its market position, reflecting the intricate balance between innovation, legal challenges, and market dynamics.

Nvidia’s recent experiences offer valuable insights into the challenges and opportunities faced by leading tech companies today. As legal disputes unfold and market valuations fluctuate, the broader implications for the tech industry, intellectual property rights, and the ongoing pursuit of innovation remain subjects of keen interest. Nvidia’s journey through these complex landscapes underscores the dynamic interplay between legal considerations, market competition, and the relentless drive for technological advancement.

As the industry moves forward, the lessons learned from Nvidia’s story will undoubtedly influence future discussions on copyright law, market dynamics, and the role of AI in shaping the digital future.

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Nvidia’s CEO, Jensen Huang is stirring the pot again, folks. This time, he’s stepping up with a bold claim that’s set to redefine our understanding of coding. But he’s not just throwing this out into the void; he’s delivering his message to an audience with the power to broadcast it across the tech landscape.

Remember the good old days? Those were the times when wrestling with strings and arrays, spending hours diligently debugging, and trying to unravel complex algorithms were considered rites of passage for developers. Those challenging days are on the cusp of becoming historical footnotes, thanks to the revolutionary entrance of generative AI into the coding domain, signaling a transformative shift in the development process.

AI is no longer just a supporting act; it’s assuming the lead role, fundamentally altering the coding narrative. The advent of AI is moving us away from the precise details of programming languages, guiding us towards a broader horizon where problem-solving and innovation take precedence. The empowerment provided by AI is democratizing software development, enabling individuals with minimal tech exposure to create applications, a scenario that once seemed far-fetched.

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The evolution from coding challenges to AI solutions

TechRadar has recently illuminated Huang’s dialogue at the World Government Summit in Dubai, where he made a compelling argument that in the wake of AI’s rapid advancements, we may need to reassess the elevated status traditionally accorded to coding skills within the tech realm.

For a long time, mastering coding was seen as the golden ticket in the tech industry. Huang is challenging this long-standing paradigm, suggesting it’s time for a strategic pivot in how we prioritize skills for the future.

Huang is shaking things up, proclaiming that the era of prioritizing coding skills is over. Now, he suggests, we should focus on fields like agriculture and education. The rise of generative AI and natural language processing technologies is set to revolutionize our approach to technology, potentially redirecting the countless hours previously dedicated to learning programming languages towards gaining a deeper understanding of these critical areas.

Huang is on a quest to render technology so user-friendly that programming becomes an innate skill for everyone, achievable through the simplicity of our native languages. He envisions a future where the magic of artificial intelligence makes everyone a programmer, without the need for specialized coding languages.

However, Huang quickly points out that this doesn’t spell the end for coding. A foundational understanding of coding principles remains essential, particularly for leveraging AI programming effectively. He’s advocating for a shift towards upskilling, ensuring that individuals grasp the ‘how’ and the ‘when’ of employing AI in problem-solving.

His enthusiasm for natural language processing suggests a future where the barrier to coding is not the complexity of programming languages but rather the ability to communicate ideas clearly. This could potentially open up programming to a much wider audience.

Personal reflections on the coding journey

Looking back on my journey into the world of coding during my university days as a network engineer, I was captivated by the magic of creating something from nothing. The simplicity and immediacy of building websites with HTML and CSS were exhilarating, offering a tangible sense of creation from mere lines of code.

Java, on the other hand, presented a steeper learning curve. Unlike the more intuitive HTML and CSS, Java introduced a level of complexity that tested my resolve, demanding a deeper understanding and a more significant commitment from those who wished to master it.

Yet, the challenge of Java was also its reward. It served as a gateway to a broader understanding of programming concepts such as object-oriented programming and multithreading, enriching my coding repertoire.

Java was a learning curve, but it comes with rewards. (Source – Shutterstock).

The journey through learning Java, and programming in general, taught me an important lesson: the path to mastery varies greatly depending on your objectives. For those looking to get their feet wet, the learning curve is manageable. But for those aiming for proficiency, the road is fraught with complex concepts that require dedication.

Then came C++, a language that, in its complexity, offered a profound depth of understanding for classes, structs, memory manipulation, and foundational programming concepts. The journey to mastering C++ was a testament to the value of persistence and the transformative power of applying theoretical knowledge to practical projects.

The future of programmers in the age of AI

The emergence of AI in coding brings us to a pivotal question: will AI render programmers obsolete? My perspective leans towards skepticism. Despite the undeniable impact of AI, the nuances of legacy code, the necessity of oversight, and the intricacies of control suggest that programming jobs will evolve rather than disappear. The prospect of AI enabling lay users to create software through conversation opens new possibilities, but also underscores the enduring value of programming expertise – because if you create in your natural language, you’re not inherently challenged to know what has gone wrong, or why, when it inevitably does, and so don’t have the understanding to correct it.

As we navigate the initial stages of AI’s integration into coding, it becomes apparent that we are interacting with the nascent stages of AI’s capabilities. These early iterations, while impressive, are placeholders for the more sophisticated, efficient solutions that are yet to emerge.

As we stand on the precipice of this new era that Huang envisions, where AI could fundamentally alter our relationship with coding, we’re reminded of the journey that has brought us here. The core of technology—learning, applying, and exploring—remains as dynamic and exciting as ever, even as we venture into this uncharted territory.

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In online gaming, latency and ping are critical factors that significantly influence a player’s experience. These terms, which might not always be at the forefront for casual gamers, are essential in shaping the game’s responsiveness, fairness, and overall enjoyment. Addressing these vital aspects, Nvidia Reflex emerges as a game-changing technology.

By significantly reducing system latency on GeForce graphics cards and laptops, Nvidia Reflex ensures that players’ actions are registered and reflected on-screen more quickly. This enhancement is particularly beneficial in multiplayer matches, where having a competitive edge is crucial, and it also makes single-player titles more responsive and enjoyable.

Since its introduction in September 2020, Nvidia Reflex has revolutionized system latency reduction in over 100 games. It has become a widely adopted feature, with over 90% of GeForce gamers enabling Reflex in their settings. The technology is not limited to competitive shooters like Apex Legends, Call of Duty: Modern Warfare III, and Fortnite; it also extends to critically acclaimed titles across various genres, such as Cyberpunk 2077, The Witcher 3: Wild Hunt, and Red Dead Redemption 2.

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Nvidia Reflex’s widespread adoption in competitive and single-player games underscores its effectiveness in tackling the challenges of latency and ping, elevating the gaming experience to new heights.

In 2023, gamers equipped with GeForce graphics cards devoted over 10 billion hours to playing their favorite games, enjoying a noticeable enhancement in responsiveness thanks to the implementation of Nvidia Reflex’s system latency reduction technology.

Latest game titles embracing Nvidia Reflex

The trend toward adopting Reflex shows no signs of slowing, and we can expect to see its integration in a growing number of eagerly awaited games throughout 2024. Since Nvidia’s previous update, games such as Layers of Fear, SCUM, and Squad have embraced Reflex technology. Additionally, at CES 2024, it was revealed that Horizon Forbidden West and NAKWON: Last Paradise will include Reflex support from their respective launches.

Players anticipating the Horizon Forbidden West Complete Edition will find it launches with Reflex support integrated from day one. This sequel to Horizon Zero Dawn, highly regarded for its expansive world and narrative depth, invites players to explore new territories, confront formidable machines, and interact with diverse tribes. The game’s narrative centers on a land plagued by devastating storms and an unrelenting blight, posing a threat to the remnants of humanity. As the protagonist, Aloy, players will seek to unravel the mysteries behind these threats and strive to restore balance to the world. The Complete Edition also features the Burning Shores story expansion and additional content.

The psychological horror game Layers of Fear has received an update to include Nvidia Reflex, significantly reducing system latency and enhancing the gaming experience. This title, known for its impact on the narrative-driven horror genre, combines the original Layers of Fear, its sequel, and all downloadable content into a comprehensive package. This package now benefits from the latency-reducing capabilities of Reflex, ensuring a more immersive and responsive gameplay experience.

NAKWON: Last Paradise, an upcoming game by Mintrocket, is set in a post-apocalyptic Seoul overrun by zombies. This third-person stealth survival game emphasizes stealth and survival in a city where firearms are scarce, but hiding spots abound. Players must rely on their wits to survive against both AI-controlled zombies and other players in a PvPvE format. When it launches, NAKWON: Last Paradise will feature Reflex support from the outset, offering players enhanced responsiveness and performance.

SCUM, developed by Gamepires and Jagex, offers a unique survival experience where prisoners fight for survival and fame in a dystopian future. The game balances hardcore survival mechanics with optional PvP events, blending intricate planning and intense action. Recently, SCUM incorporated Reflex technology, significantly reducing system latency and improving the overall player experience.

Offworld Industries’ Squad, a large-scale multiplayer shooter, focuses on realism and team coordination. The game was recently upgraded to include Reflex support, reducing system latency and enhancing the responsiveness of its combat gameplay. This update is particularly beneficial in Squad‘s large-scale battles, where quick reactions and precise actions are essential.

The introduction of Reflex technology extends beyond game titles to gaming hardware. For instance, the launch of the HyperX Pulsefire Haste 2 Mini – Wireless Gaming Mouse, compatible with the Nvidia Reflex Analyzer, represents a step forward in gaming peripherals. This lightweight mouse, designed for optimal performance, can be used to measure system latency accurately, offering gamers insights into their setup’s responsiveness.

As Nvidia Reflex continues to expand its presence in the gaming world, more games and compatible devices are expected to be announced.

GeForce RTX 4070 Super game ready driver is out now

The introduction of Nvidia’s GeForce RTX 4070 Super, alongside the GeForce RTX 4080 Super and 4070 Ti Super, marks a significant enhancement in the GeForce RTX 40 Series. The GeForce RTX 4070 Super, available now, is designed to deliver exceptional performance, especially in graphically demanding games. Its launch is accompanied by the availability of a new game ready driver, essential for harnessing the full potential of this advanced GPU.

The GeForce RTX 4070 Super stands out for its significant core increase compared to the GeForce RTX 4070, making it a compelling choice for gaming at 1440p with maximum settings. Its performance, surpassing that of the GeForce RTX 3090 while being more power-efficient, is further enhanced by the inclusion of DLSS 3. This makes it an attractive option for gamers and creators upgrading from the GeForce RTX 3070 or RTX 2070, offering a significant leap in frame rates and overall gaming experience at 1440p.

RTX 4070 SUPER – 20% more cores (Source – Nvidia)

Media reviews of the GeForce RTX 4070 Super highlight its efficiency, performance, and quiet operation, underscoring its prowess as a 1440p gaming GPU. Available in both Founders Edition and custom models from various add-in card providers, the GPU caters to a range of preferences and requirements.

In addition to the hardware advancements, Nvidia’s focus extends to gaming experiences, exemplified by the upcoming Palworld game. Set to enter early access, Palworld is a multiplayer, open-world survival and crafting game featuring a unique monster-collection element. GeForce RTX gamers will have the advantage of activating DLSS 2 to enhance game performance, ensuring an optimal gaming experience.

GeForce Experience continues to support gamers with one-click optimal settings for over 1,000 titles, including recent additions like Apocalypse Party and Escape from Tarkov: Arena. This feature, along with the ability to capture and share gaming moments, underscores Nvidia’s commitment to enhancing the gaming experience.

The new GeForce game ready driver is available for download, providing the necessary support for the latest hardware and games. Nvidia encourages users to provide feedback on the driver through the GeForce.com driver feedback forum, ensuring continuous improvement and addressing any technical issues.

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The gaming industry is experiencing a surge of revolutionary updates, and it’s only the second week of the new year. Amid this excitement, anticipation is growing for the predicted release of the Nintendo Switch 2. Adding to the buzz, Nvidia, a leading GPU provider, has recently unveiled a new addition to its GeForce RTX family.

Introducing the Nvidia GeForce RTX 40 Super series GPUs

Nvidia’s latest announcement introduces the GeForce RTX 40 Super series family of GPUs, including the GeForce RTX 4080 Super, GeForce RTX 4070 Ti Super, and GeForce RTX 4070 Super. These GPUs, which form the backbone of AI-powered PCs, supercharge the latest games and facilitate the creation of new entertainment worlds and experiences. The GeForce RTX 4070 Super, starting at US$599, is set to transform gaming and content creation with impressive capabilities.

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The new Nvidia Ada Lovelace architecture-based GPUs deliver up to 52 shader TFLOPS, 121 RT TFLOPS, and 836 AI TOPS, significantly enhancing gaming and creative processes. With these GPUs, developing novel entertainment experiences becomes more accessible and powerful.

PC gamers who prioritize top-notch visual quality will find the AI-powered Nvidia Deep Learning Super Sampling (DLSS) Super Resolution, Frame Generation, and Ray Reconstruction, coupled with ray tracing, deliver stunning worlds in games like Diablo IV, Pax Dei, and Horizon Forbidden West. DLSS allows for AI generation of seven out of eight pixels, accelerating full ray tracing by up to four times while maintaining superior image quality.

GeForce RTX 40 Series laptops image – Slide image featuring new laptop announcements from major manufacturers including Acer, ASUS, DELL, HP, Lenovo, MSI, Razer and Samsung. (Source – Nvidia).

Matt Wuebbling, vice president of global GeForce marketing at Nvidia, notes, “For everyone from gaming enthusiasts to creative professionals, GeForce RTX Super GPUs are simply awesome upgrades. GeForce RTX Super cards support over 500 RTX games and applications and will have users prepared for the wave of generative AI apps coming to PC.”

The GeForce RTX Super GPUs mark a significant advancement in AI-powered PC computing. Specialized AI Tensor Cores deliver up to 836 AI TOPS, revolutionizing AI applications in gaming, creativity, and everyday productivity. The rich software ecosystem built on RTX GPUs further accelerates AI’s potential.

Nvidia TensorRT, a high-performance deep learning inference software suite, includes an inference optimizer and runtime that ensures low latency and high throughput. The open-source library TensorRT-LLM for Windows optimizes large language model inference performance. In AI tasks, the GeForce RTX 4080 Super generates video over 1.5 times faster and images over 1.7 times faster than the RTX 3080 Ti.

AI-powered DLSS enhances in-game immersion for gamers, while creative professionals, such as those using Adobe Photoshop, benefit from Tensor Cores for increased productivity. Nvidia Broadcast also improves productivity by removing background noise and offering seamless virtual backgrounds. GeForce RTX Super GPUs enable users to harness the full potential of AI on Windows PCs.

The GeForce RTX 4080 Super, available from January 31 starting at US$999, is basically a powerhouse for 4K gaming. It outperforms the GeForce RTX 3080 Ti by 1.4 times without DLSS Frame Generation and doubles its speed with this technology. With more cores and faster memory, it offers a significant performance edge.

The RTX 4070 Ti Super, ideal for high-frame-rate gaming at 1440p and up to 4K, surpasses the RTX 4070 Ti with more cores, a 16GB frame buffer, and a 256-bit memory bus. This boosts memory bandwidth to 672 GB/sec, making it 1.6 times faster than the RTX 3070 Ti and 2.5 times with DLSS 3. It will be available starting January 24 at US$799.

Launching on January 17 at US$599, the RTX 4070 Super offers a 20% increase in cores over the RTX 4070, outperforming the RTX 3090 with lower power consumption. With DLSS 3, its performance lead extends to 1.5 times faster.

Nvidia unveils ACE: a leap in interactive gaming

Nvidia also introduced production microservices for its Nvidia Avatar Cloud Engine (ACE), enabling game developers, tool creators, and middleware integrators to incorporate cutting-edge generative AI models into digital avatars in their games and applications.

ACE microservices allow developers to build interactive avatars using AI models such as Nvidia Audio2Face (A2F), which animates facial expressions from audio sources, and Nvidia Riva Automatic Speech Recognition (ASR), for customizable multilingual speech and translation applications.

Nvidia ACE microservices demo (Source – Nvidia).

Game development companies like Charisma.AI, Convai, Inworld, miHoYo, NetEase Games, Ourpalm, Tencent, Ubisoft, and UneeQ are already using ACE to enhance their gaming experiences.

Keita Iida, Nvidia’s vice president of developer relations, highlights the transformative impact of generative AI technologies in game creation and gameplay. With Nvidia ACE, developers can populate their worlds with lifelike digital characters, moving beyond pre-scripted dialogue and creating more immersive in-game interactions.

Transforming gaming characters with Nvidia ACE

Top game developers and interactive avatar creators are pioneering new ways to use ACE and generative AI technologies, transforming how players interact with non-playable characters (NPCs) in games and applications.

Tencent Games, a significant player in the gaming industry, acknowledges this as a pivotal moment for AI in games. They believe that Nvidia ACE, in collaboration with Tencent Games, will set a foundation for introducing digital avatars with unique, lifelike personalities and interactions into video games.

Nvidia ACE is bringing a new dimension to game characters. Traditionally, NPCs were designed with predetermined responses and animations, limiting player interactions to short, transactional exchanges often overlooked by players.

Purnendu Mukherjee, founder and CEO at Convai, points out the immense potential of generative AI-powered characters in virtual worlds. Convai uses Riva ASR and A2F to create NPCs with low-latency response times and high-fidelity, natural animations, enhancing the gaming experience.

Nvidia’s collaboration with Convai to expand the Nvidia Kairos demo showcases the transformative capabilities of ACE. In this enhanced version, Riva ASR and A2F are extensively used to improve NPC interactivity. Convai’s framework allows NPCs to engage in conversations, interact with objects, and guide players through game worlds, creating a more dynamic and interactive gaming environment.

This latest iteration of Kairos demonstrates how generative AI can revolutionize NPC interactions. NPCs can now converse among themselves, recognize and manipulate objects, and even guide players to objectives, offering a more immersive and interactive gaming experience.

The Nvidia GeForce RTX Super GPUs and ACE microservices are set to revolutionize gaming and PC computing. By harnessing the power of AI, Nvidia is creating a future where gaming and creative processes are more immersive, efficient, and engaging than ever before.

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In the fast-paced world of GPUs and semiconductor innovation, the rivalry between AMD and Nvidia spans decades. The longstanding competition has transformed gaming and creative workloads, and the emergence of generative AI has added a new dimension to the battle. While Nvidia has traditionally dominated AI, AMD is now introducing strong contenders, challenging Nvidia’s historical supremacy.

The battle between AMD and Nvidia is characterized by a continuous cycle of technological advancements. Each company strives to outdo the other in terms of performance, energy efficiency, and feature sets. This race has led to the introduction of increasingly powerful GPUs capable of rendering immersive gaming experiences and handling complex AI workloads.

At a mid-week “Advancing AI” event in San Jose, California, AMD said it will go head to head with Nvidia in the AI chips market by launching a new lineup of accelerator processors. The AMD Instinct™ MI300 series accelerators, according to CEO Lisa Su, are designed to outperform rival products in running AI software.

AMD Instinct M1300 AI chips. Source: AMD’S website.

The launch marks a pivotal moment in AMD’s five-decade history, positioning the company for a significant face-off with Nvidia in the thriving AI accelerators market. These chips play a crucial role in AI model development, excelling at processing data compared to conventional computer processors.

“The truth is we’re so early,” Su said, according to Bloomberg‘s report. “This is not a fad. I believe it.” The event signals that AMD is growing confident that its MI300 lineup can attract major tech players, potentially redirecting significant investments to the company. During the Wednesday AMD investor event, Meta, OpenAI, and Microsoft announced their adoption of AMD’s latest AI chip, the Instinct MI300X. 

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The move signals a notable shift among tech companies seeking alternatives to Nvidia’s costly graphics processors, which have traditionally been crucial for developing and deploying AI programs like OpenAI’s ChatGPT. On the same day, Nvidia experienced a 2.3% decline in shares to US$455.03 in New York, reflecting investor concerns about the perceived threat posed by the new chip. 

However, AMD did not witness a proportional share surge, with a modest 1.3% decrease to $116.82 on a day when tech stocks were generally underperforming. To recall, Nvidia’s market value surpassed US$1.1 trillion this year, fueled mainly by the increasing demand for its chips from data center operators. 

However, the looming question around Nvidia’s dominance in the accelerator market is how long it can maintain its current position without significant competition. The launch of the AMD Instinct MI300 range will push that question to its limits.

AMD vs Nvidia: who has better AI chips right now?

The MI300 Series of processors, which AMD calls “accelerators,” was first announced nearly six months ago when Su detailed the chipmaker’s strategy for AI computing in the data center. AMD has built momentum since then. 

This week’s announcement means that MI300X and MI300A are shipping and in production. The MI300X, designed for cloud providers and enterprises, is built for generative AI applications and outperforms Nvidia’s H100 GPU in two key metrics: memory capacity and memory bandwidth. That lets AMD deliver comparable AI training performance and significantly higher AI inferencing performance.

In other words, the MI300X GPU boasts over 150 billion transistors, surpassing Nvidia’s H100, the current market leader, with 2.4 times the memory. It also reaches 5.3 TB/s peak memory bandwidth, which is 1.6 times more than Nvidia’s H100’s 3.3 TB/s. 

Specs comparisons. Source: AMD’S website.

“AMD sees an opening: large language models — used by AI chatbots such as OpenAI’s ChatGPT — need a huge amount of computer memory, and that’s where the chipmaker believes it has an advantage,” a Bloomberg report reads.

“It’s the highest performance accelerator in the world for generative AI,” Su said at the company’s event. In a briefing beforehand, Brad McCredie, AMD’s corporate vice president of data center GPU, said the MI300X GPU will still have more memory bandwidth and memory capacity than Nvidia’s next-generation H200 GPU, which is expected next year.

In November, when Nvidia revealed its intentions for the H200, the company specified that it would incorporate 141 GB of HBM3 memory and boast a memory bandwidth of 4.8 TB/s. So naturally, McCredie expects fierce competition between the two companies on the GPU front.

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“We do not expect (Nvidia) to stand still. We know how to go fast, too,” he said. “Nvidia has accelerated its roadmap once recently. We fully expect it to keep pushing hard. We will keep going hard, so we fully wish to stay ahead. Nvidia may leapfrog. We’ll leapfrog back.”

The AMD Instinct MI300A APU comes equipped with 128GB of HBM3 memory. Compared to the prior M250X processor, the MI300A delivers 1.9 times more performance per watt on HPC and AI workloads, according to the company. 

Forrest Norrod, executive vice president and general manager of AMD’s data center solutions business group highlighted that at the node level, the MI300A achieves twice the HPC performance-per-watt compared to its closest competitor. This allows customers to accommodate more nodes within their overall facility power budget, aligning with sustainability goals.

Source: Lisa Su’s X

AMD’s outlook

In her address, Su emphasized that AI is the most transformative technology in the last 50 years, surpassing even the introduction of the internet. Notably, she highlighted the accelerated adoption rate of AI compared to other groundbreaking technologies. Last year, AMD projected a 50% growth in the data center AI accelerated market from US$30 billion in 2023 to US$150 billion in 2027. 

Su revised this estimate, now predicting an annual growth of more than 70% over the next four years, with the market reaching over US$400 billion in 2027. Su also underscored the pivotal role of generative AI, stressing the substantial investment required for new infrastructure to facilitate training and inference, describing the market as “tremendously significant.”

The post Can AMD disrupt Nvidia’s AI reign with its latest MI300 chips? appeared first on Tech Wire Asia.

Over the last two decades, Singapore has made significant strides in solidifying its global data center hub position, capitalizing on its strategic location, robust fiber broadband connectivity, cloud services availability, and pro-business policies. Today, the city-state has formed a formidable digital infrastructure featuring 100 data centers, 1,195 cloud service providers, and 22 network fabrics. So it’s unsurprising that Nvidia Corp. saw 15% of its revenue come from Singapore in the recently concluded third quarter.

According to a US Securities and Exchange Commission filing, Singapore played a significant role in the US chip giant’s recent financial success, contributing US$2.7 billion of its US$18 billion revenue for the quarter ending October. The amount was a remarkable increase of 404.1% from the US$562 million recorded in the same quarter the previous year, surpassing Nvidia’s overall revenue growth of 205.5% from a year ago.

The power of Singapore – revealed. Source: Securities and Exchange Commission (SEC)

The growth puts Singapore ahead of every country except the US (35%), Taiwan (24%), and China, including Hong Kong (22%), based on CNBC’s observation. In the third quarter, 80% of Nvidia’s sales, as disclosed in the SEC filing, originated from the data center segment. The remaining portion was attributed to gaming, professional visualization, automotive, and other sectors.

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“Cloud service providers drove roughly half of data center revenue, while consumer internet companies and enterprises comprised approximately the other half,” said Nvidia in the filing. That said, Singapore had its advantages, considering it is a global data center hub, hosting significant players including Amazon Web Services, Microsoft Azure, IBM Softlayer, and Google Cloud. 

What’s more, due to a robust network supported by 24 submarine cables, the country is also the landing site for a dense network of undersea cables, connecting it to other parts of Asia, Europe, Africa, Australia, and the US. A quick check on the Speedtest Global Index by Ookla shows Singapore has the world’s highest median fixed broadband speed.

Even Citi analysts acknowledged in a November 27 report that “Singapore is also a growing area of specialized CSPs standing up data centers in the region. The contrast becomes more pronounced when accounting for Singapore’s size. On a per capita basis, Singapore spent US$600 on Nvidia chips in the quarter, whereas the US spent only US$60 and China spent approximately US$3 per capita.

“That’s the billing location of the customer and not necessarily the point of consumption,” said Srikanth Chandrashekhar on LinkedIn, responding to a post by former Temasek director Sang Shin. Sang Shin had suggested the chips might be bound for data centers in Singapore, which seems a reasonable idea, since most Nvidia chips are headed for data centers, and Singapore has many such facilities.

The irony of building data centers in Singapore is exploded by the benefits the city-state brings. Source: LinkedIn

What’s next for Singapore’s data center sector?

According to an article by ASEAN Briefing, 7% of total electricity consumption in Singapore goes to data centers, and it is projected to reach 12% by 2030. In short, the city-state will likely attract more players in the market, especially after lifting a moratorium on data centers in January 2022. Initially enacted in 2019, this moratorium responded to the considerable energy consumption associated with data centers.

Singapore has rapidly emerged as a prime destination for this pivotal industry due to its technological prowess, regulatory strength, and enticing incentives.

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Firstly, the Pioneer Certificate Incentive (PC) program encourages companies, including those in the data center sector, to enhance their capabilities and undertake new or expanded activities in Singapore. 

The incentive is aimed at companies involved in global or regional headquarters (HQ) activities, managing, coordinating, and controlling business operations for a group of companies. Designed to drive substantial investment contributions and foster advancements in leading industries, the PC aligns with the characteristics and potential of the data center sector. 

The incentive is a win-win situation for both companies and the city-state as to qualify; businesses must introduce advanced technology, skill sets, or know-how, surpassing prevailing standards in Singapore. Additionally, they should engage in pioneering activities that substantially contribute to the economy.

Another allure of incentives includes GST waivers on importing data center equipment and covering servers, networking gear, and cooling systems. Then there’s Singapore’s dedication to sustainability, that stands out through initiatives such as the SS 564 Green Data Centers Standard and the Data Center Carbon Footprint Assessment (DC-CFA) program. 

The nation’s commitment to data security and privacy is also reflected in its regulatory framework, notably the Personal Data Protection Act (PDPA) and the Cybersecurity Act, fostering a trustworthy environment for data center operations.

The post Singapore: the powerhouse behind Nvidia’s revenue appeared first on Tech Wire Asia.

In cloud computing and artificial intelligence, the strategic collaboration between AWS and Nvidia has been a cornerstone of innovation for over a decade. Specializing in groundbreaking GPU-based solutions across domains such as AI/ML, graphics, gaming, and high-performance computing, this alliance has continually evolved to adapt to the latest technological advancements, extending its reach from the cloud, with Nvidia GPU-powered Amazon EC2 instances, to the edge, with services like AWS IoT Greengrass.

Evolving partnership: bridging cloud and AI with AWS and Nvidia

Building upon this rich history, AWS and Nvidia have recently announced a significant expansion of their strategic collaboration to deliver the most advanced infrastructure, software, and services to power generative AI innovations. This expanded collaboration will unite the best of both Nvidia and AWS technologies. It will incorporate Nvidia’s latest multi-node systems featuring next-generation GPUs, CPUs, and AI software, as well as AWS’s cutting-edge Nitro system for virtualization and security, the Elastic Fabric Adapter (EFA) interconnect, and UltraCluster scalability.

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These technologies are ideally suited for training foundation models and constructing generative AI applications.

This latest development in the AWS-Nvidia partnership is an extension of their longstanding relationship and a leap into the future of generative AI. It builds on a foundation that has already fueled the generative AI era by providing early machine learning pioneers with the computing performance necessary to push the boundaries of these technologies.

In the expanded partnership to advance generative AI across various sectors:

• AWS will be the first to offer Nvidia GH200 Grace Hopper superchips in the cloud, featuring the new multi-node NVLink technology. The GH200 NVL32 platform links 32 Grace Hopper superchips into a single instance, available on Amazon EC2 instances. This setup, supported by Amazon’s EFA networking, AWS Nitro system, and EC2 UltraClusters, allows customers to scale to thousands of GH200 superchips.
• Nvidia and AWS will launch Nvidia DGX Cloud on AWS, the first to utilize GH200 NVL32. This AI-training-as-a-service will provide massive shared memory, accelerating the training of advanced generative AI and large language models with over 1 trillion parameters.
• The companies are collaborating on Project Ceiba to create the world’s fastest GPU-powered AI supercomputer. Hosted on AWS with GH200 NVL32 and Amazon EFA, this 65 exaflop-capable system, featuring 16,384 GH200 superchips, will drive Nvidia’s generative AI innovations.
• AWS is introducing three new EC2 instances: P5e instances with Nvidia H200 Tensor Core GPUs for extensive generative AI and HPC workloads; G6 and G6e instances powered by Nvidia L4 and L40S GPUs, respectively, suitable for a range of applications including AI, graphics, video workloads, and 3D applications using Nvidia Omniverse.

Revolutionizing cloud AI: introducing the Nvidia GH200 superchips in AWS

These advancements build upon a collaborative history that began with the world’s first GPU cloud instance. Today, Nvidia and AWS provide the most extensive array of Nvidia GPU solutions for diverse workloads, encompassing graphics, gaming, high-performance computing, machine learning, and now, generative AI.

Reflecting on the impact of these innovations, Adam Selipsky, CEO of AWS, emphasized AWS’s ongoing efforts with Nvidia to make AWS the top platform for running advanced GPUs. This effort includes integrating next-generation Nvidia Grace Hopper superchips with AWS’s robust EFA networking, the hyperscale clustering capabilities of EC2 UltraClusters, and the advanced virtualization offered by the AWS Nitro system.

“Generative AI is transforming cloud workloads and putting accelerated computing at the foundation of diverse content generation,” said Jensen Huang, founder and CEO of Nvidia. “Driven by a common mission to deliver cost-effective state-of-the-art generative AI to every customer, Nvidia and AWS are collaborating across the entire computing stack, spanning AI infrastructure, acceleration libraries, foundation models, to generative AI services.”

AWS and Nvidia have taken significant strides in their ongoing quest to revolutionize generative AI by introducing new Amazon EC2 instances. These instances feature the cutting-edge Nvidia GH200 Grace Hopper superchips with multi-node NVLink technology, marking AWS as the first cloud provider to offer this advanced capability. Each GH200 superchip combines an Arm-based Grace CPU and an Nvidia Hopper architecture GPU, integrated into the same module.

Supercomputing meets cloud

A standout feature of a single Amazon EC2 instance equipped with GH200 NVL32 is its ability to provide up to 20 TB of shared memory, essential for powering terabyte-scale workloads.

These instances use the power of AWS’s third-generation Elastic Fabric Adapter (EFA) interconnect, providing each superchip with up to 400 Gbps of networking throughput. This high performance is crucial for scaling to thousands of GH200 superchips within EC2 UltraClusters, enabling customers to efficiently manage large-scale AI/ML workloads.

The GH200-powered EC2 instances will also feature 4.5 TB of HBM3e memory, significantly enhancing the current generation of H100-powered EC2 P5d instances. This improvement is not just in memory size but also training performance, thanks to a CPU-to-GPU memory interconnect that offers vastly superior bandwidth compared to PCIe.

These instances will be the first in AWS’s AI infrastructure to incorporate liquid cooling, ensuring optimal performance in densely packed server environments. They also benefit from the AWS Nitro system, enhancing I/O functions, performance, and security.

AWS and Nvidia partnership deepens with GPU clusters (Nitro enabled) (Source – X).

Under this expanded collaboration, AWS will also host Nvidia DGX Cloud powered by the GH200 NVL32 NVLink infrastructure. The Nvidia DGX Cloud is an AI supercomputing service that gives enterprises rapid access to multi-node supercomputing capabilities. This service is critical for training complex LLM and generative AI models, and is complemented by integrated Nvidia AI Enterprise software and direct access to Nvidia AI experts.

In parallel, AWS and Nvidia are working on the massive Project Ceiba supercomputer. Integrated with AWS services like Amazon VPC and Amazon Elastic Block Store, this supercomputer will play a pivotal role in Nvidia’s research and development across various fields, including LLMs, digital biology, and autonomous vehicles.

AWS is set to introduce new EC2 instances like P5e, G6, and G6e, each powered by Nvidia’s latest GPUs. The P5e instances, equipped with H200 GPUs, are tailored for large-scale generative AI and HPC tasks.

Meanwhile, the G6 and G6e instances, powered by Nvidia L4 and L40S GPUs, are designed for various applications, including AI fine-tuning, graphics, and video workloads. The G6e instances are suitable for developing complex 3D workflows and digital twins using Nvidia Omniverse. These instances reflect AWS and Nvidia’s commitment to providing cost-effective, energy-efficient solutions for various needs.

Nvidia’s software innovations on AWS

To complement these hardware advancements, Nvidia has also announced new software on AWS to bolster generative AI development. This includes the Nvidia NeMo Retriever microservice and BioNeMo, which are set to transform the creation of chatbots, summarization tools, and accelerate drug discovery.

Nvidia BioNeMo is set to transform the creation of chatbots, summarization tools, and accelerate drug discovery. (Source – Nvidia).

These software solutions are helping Amazon innovate its services and operations, as seen in Amazon Robotics’ use of Nvidia Omniverse Isaac for digital twin development and Amazon’s use of the Nvidia NeMo framework for training Amazon Titan LLMs.

Through these collaborative efforts, AWS and Nvidia are not only supercharging generative AI, HPC, design, and simulation capabilities but also democratizing access to state-of-the-art AI technologies for a broad range of companies and developers.

The post AWS becomes the first cloud provider to launch Nvidia GH200 Superchips with NVLink for AI cloud infrastructure appeared first on Tech Wire Asia.

Nvidia is spearheading the integration of AI into its chip design processes by incorporating chatbots, a strategy reflecting the larger tech industry’s shift towards more AI-centric solutions. This initiative shows Nvidia’s commitment to AI (and to staying ahead of the pack), and represents a pivotal change in how AI can simplify and streamline complex engineering tasks.

By introducing chatbots into its chip design framework, Nvidia aims to refine its design methodologies and establish a new precedent in the application of AI in technology development. The company is betting that using AI to design smarter chips will significantly influence the future of AI-assisted engineering.

Nvidia’s recent research is focused on using chatbots capable of generating conversational, human-like responses in semiconductor design. This research illuminates how companies in niche sectors can tailor large language models (LLMs) to their own datasets, creating specialized assistants that enhance operational efficiency.

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The AI revolution in semiconductor design

Semiconductor design is an extraordinarily complex endeavor. The intricate architecture of state-of-the-art chips like Nvidia’s H100 Tensor Core GPU can be compared to a densely populated city under a microscope, consisting of billions of transistors, each a fraction of the width of a human hair.

The design of modern microchips, involving the strategic placement of tens of billions of transistors on a silicon wafer, is among the most challenging tasks in the tech industry. To construct such a digital metropolis, diverse engineering teams often collaborate for up to two years. These teams are responsible for various tasks ranging from the overall architectural blueprint, to crafting microscopic circuits, and thoroughly testing their functionalities. Each of these roles demands specialized methods, software, and computer languages.

Nvidia’s chips, recognized for their complexity, are pivotal in powering advanced technologies, including AI systems like ChatGPT. This complexity necessitates continual innovation and precision in design and execution.

The study conducted by Nvidia discovered that incorporating extensive, specific internal data enabled even basic chatbots to outperform their advanced counterparts in accuracy. This approach not only optimizes performance but also helps manage and control system costs.

How AI can benefit engineering

Nvidia’s using AI to design new chips.

One of the standout features demonstrated by Nvidia is the use of AI in generating code. Bill Dally, Nvidia’s chief scientist, pointed out that engineers often spend considerable time identifying and diagnosing faults in chip components. AI systems can alleviate this burden by rapidly writing code scripts and facilitating testing.

Mark Ren, an Nvidia research director, believes that in the future, large language models will be instrumental across all facets of semiconductor design.

“This is an important first step in applying LLMs to the complex work of designing semiconductors,” said Dally. He emphasized the feasibility of highly specialized sectors using their own data to train effective AI models.

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Nvidia’s engineers have created ChipNeMo, a bespoke LLM tailored for their specific design needs, trained on the company’s confidential data. ChipNeMo is a pioneering project investigating the potential applications of LLMs in industrial chip design.

The team opted for unique domain adaptation techniques rather than using generic LLMs. This included deploying custom tokenizers and domain-specific training to enhance the model’s performance in specific applications like engineering assistance, script generation, and bug analysis.

These techniques have already shown promising results, significantly improving the model’s efficacy and suggesting possible reductions in model size while maintaining or enhancing performance in various design tasks.

Nevertheless, a noticeable disparity remains between the current achievements and the ideal outcomes. Nvidia’s team is confident that further exploring domain-adapted LLMs could help bridge this gap, leading to even more sophisticated and efficient chip design methodologies.

Looking ahead, Nvidia engineers want to apply generative AI throughout the chip design process, potentially unlocking substantial improvements in productivity and innovation. With a career spanning over two decades in electronic design automation (EDA), Ren foresees this AI integration as a game-changer in the semiconductor industry.

Among the innovations, the bug maintenance tool has drawn significant acclaim. This tool, which automates the upkeep of known bug descriptions, has proved invaluable in streamlining the debugging process.

A prototype chatbot designed to respond to questions regarding GPU architecture and design showed potential in early tests, letting engineers access pertinent technical documents rapidly. In development, a code generator already produces short snippets of software in two languages used by chip designers. This generator is set to be integrated into current design tools, offering engineers a convenient assistant for ongoing projects.

ChipNeMo in action. (Source – Nvidia)

Nvidia and the future of AI in chip development

The research paper essentially discusses the team’s endeavor to gather and utilize Nvidia’s design data to construct a specialized generative AI model. While initially focused on chip design, this process has implications far beyond, indicating its potential applicability across various industries.

The project began with a base model, further developed using Nvidia NeMo. The foundational model boasts 43 billion parameters, indicative of its pattern recognition capabilities, and was trained using over a trillion tokens, comprising words and symbols from texts and software.

ChipNeMo exemplifies how a specialized technical team can enhance a pre-existing model using their unique data. (Source – Nvidia)

Subsequent training phases involved around 24 billion tokens of Nvidia’s design data, followed by a mixture of approximately 130,000 examples of design conversations and layouts.

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This pioneering work is among the early instances of research and proof-of-concept demonstrations of generative AI in the semiconductor industry, signaling the beginning of a new era in technological innovation and AI application. As this field continues to evolve, it’s clear that Nvidia’s contributions and explorations will not only reshape the landscape of chip design but also illustrate the vast, untapped potential of AI across diverse sectors.

The post How Nvidia is using AI chatbots to craft smarter chips appeared first on Tech Wire Asia.

The realm of robotics is advancing astonishingly, with robots now capable of executing intricate tasks such as dexterous ‘spinny pen’ tricks with their mechanical hands. These advancements are rapidly closing the gap between human and robotic abilities. A recent blog by NVIDIA highlights an AI agent that employs Large Language Models (LLMs) to craft reward algorithms automatically, a crucial step in training robots to undertake complex tasks.

NVIDIA Research has developed a new AI agent capable of instructing robots in sophisticated skills, achieving a milestone by training a robotic hand to execute fast-paced pen-spinning tricks on par with human proficiency.

The video accompanying the announcement captures the mesmerizing sleight of hand, just one of nearly 30 tasks Eureka has mastered. Eureka is an AI system designed to independently formulate reward algorithms, an essential aspect of robotic training, without human intervention.

Beyond gaming: The NVIDIA AI teaches robots real-world tricks

The system’s repertoire extends beyond just pen-spinning. Eureka is adept at instructing robots in various skills crucial for real-world applications, from opening drawers and handling scissors to the delicate art of tossing and catching balls precisely.

NVIDIA is open-sourcing Eureka’s research, including an in-depth paper and the AI algorithms driving the project, through NVIDIA Isaac Gym. This platform serves as a reference application for reinforcement learning research, and it’s built on NVIDIA Omniverse, known for its versatile, collaborative platform for creating 3D tools and applications.

Driving Eureka’s advanced capabilities is the GPT-4 large language model, showcasing the system’s cutting-edge technical foundation. 

Anima Anandkumar, NVIDIA’s Senior Director of AI Research and a co-author of the Eureka paper, recognizes the substantial progress reinforcement learning has seen over the years. Still, she emphasizes the continuing challenges, particularly in reward design, which often hinges on inefficient trial-and-error methods.

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“Eureka is a first step toward developing new algorithms that integrate generative and reinforcement learning methods to solve hard tasks,” Anandkumar elaborates.

The research underscores Eureka’s effectiveness, with its generated reward programs facilitating robot learning through experimentation, outperforming human-written counterparts in more than 80% of tasks. This superiority isn’t just marginal; it’s substantial, translating to an average performance improvement of over 50% for the robots trained under Eureka’s guidance.

The NVIDIA Eureka AI: Rewarding robots, surpassing humans

Eureka breaks away from traditional methodologies by employing GPT-4 LLM and generative AI in unison to create code. This code is unique, offering reward signals for robots engaged in reinforcement learning. The process eliminates the necessity for task-specific instructions or pre-defined reward templates, representing a significant departure from established practices.

Moreover, Eureka’s approach welcomes human feedback, allowing the system to hone its reward signals, and ensuring they align more accurately with developers’ objectives and expectations. This feature signifies a notable advancement in AI-human collaboration, streamlining the training process, and enhancing outcome quality.

One of Eureka’s standout features is its ability to evaluate numerous reward candidates simultaneously efficiently. It achieves this through GPU-accelerated simulation in Isaac Gym, drastically reducing the time and resources required for such assessments. The system doesn’t just stop after the initial evaluation; it goes a step further, collating critical statistics from the training results and employing the LLM to fine-tune its reward function generation.

Diverse applications and in-depth evaluations

NVIDIA’s comprehensive research paper provides an exhaustive evaluation of 20 tasks trained by Eureka. These tasks are benchmarked using open-source dexterity standards that require a wide range of advanced manipulation skills from robotic hands. The results, visualized through nine Isaac Gym environments and rendered using NVIDIA Omniverse, illustrate Eureka’s effectiveness across various robotic forms, including quadrupeds, bipeds, dexterous hands, and collaborative robot arms.

The research also highlights Eureka’s unique ability to create novel, effective rewards. These rewards often exceed the performance of those designed by humans, demonstrating Eureka’s advanced learning and generative capabilities. In an intriguing turn, the rewards devised by Eureka sometimes show low or even negative correlation with human-created ones but still perform exceptionally well.

This phenomenon is particularly evident when a Shadow Hand is required to spin a pen continuously, maintaining specific spinning patterns. Eureka’s sophisticated fine-tuning enables the policy to achieve this goal for multiple cycles consecutively, a feat that policies pre-trained or learned from scratch fail to accomplish.

Linxi “Jim” Fan, a senior research scientist at NVIDIA, expresses excitement about Eureka’s potential. “We believe that Eureka will enable dexterous robot control and provide a new way to produce physically realistic animations for artists,” Fan asserts.

This pioneering work stands as a testament to NVIDIA Research’s commitment to pushing the boundaries of what AI can achieve. It aligns with their recent innovations, like Voyager, an AI entity built with GPT-4, capable of independent navigation within the virtual world of Minecraft.

Eureka amidst global AI advancements

While NVIDIA is making significant strides with Eureka, it’s not alone in its pursuit of advanced AI applications in robotics. Other tech leaders, like Google, are making their mark. Google’s Robotic Transformer (RT-2) is a testament to this, representing an evolution of the company’s vision-language-action (VLA) model.

RT-2 is designed to enhance robots’ visual and language pattern recognition, allowing them to understand instructions and identify the most suitable objects for various tasks. Its capabilities were demonstrated in an office kitchen scenario, where a robotic arm successfully identified an optimal makeshift hammer and chose an appropriate beverage for a tired person.

Google’s RT-2 picking up an object. (Source – Transhumanism Videos YouTube)

The RT-2 model benefits from the integration of web data and detailed robotics information. It draws upon advancements in large language models, like Google’s Bard, and combines this wealth of information with specific robotic data. This includes detailed movement patterns for different joints, as Google’s research paper outlines. Impressively, RT-2 isn’t limited to understanding instructions in English; it’s also proficient in processing commands in other languages, highlighting the model’s versatility and advanced comprehension skills.

The future of AI in robotics

The advancements showcased by NVIDIA’s Eureka and Google’s RT-2 signify a new era in robotics, where robots are not just passive executors of binary commands but active learners capable of nuanced understanding and complex decision-making.

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These systems’ ability to process extensive data, learn from it, and apply the knowledge practically breaks new ground in how robots can learn and function autonomously.

The success of Eureka’s pen-spinning exercise and the diverse tasks it has managed to master hint at a future where robots could seamlessly perform a range of complex actions, from everyday household tasks to intricate industrial processes, with precision and efficiency previously thought unattainable.

The post The NVIDIA Eureka masters human-like pen-spinning tricks, redefining AI learning appeared first on Tech Wire Asia.