Latest news with #GTC2025
Yahoo
06-05-2025
- Business
- Yahoo
Dell Technologies Recieves Bullish Calls as AI Infrastructure Demand Surges
May 6 - Dell Technologies (NYSE:DELL) is capturing investor attention in 2025, driven by its push into AI?powered data center hardware and enterprise solutions. Once best known for PCs, Dell has ramped up its cloud computing and high?performance server offerings. As companies boost tech spending and adopt AI, Dell's hybrid?cloud and AI infrastructure positioning stands out. Analysts maintain a "Strong Buy" consensus on DELL stock, though tariff worries and softer demand have led several to trim price targets. Year?to?date, DELL shares have slid about 19%, reflecting broader market pressures. Dell Technologies Recieves Bullish Calls as AI Infrastructure Demand Surges Bank of America's five?star analyst Wamsi Mohan reiterated a Buy rating and a $150 target, citing Dell's AI partnerships showcased at Nvidia's (NASDAQ:NVDA) GTC 2025 event. Dell's end?to?end AI stack, from AI?enabled PCs to GPU?powered racksearned special mention. At Citi, five?star analyst Asiya Merchant kept her Buy call but cut her target to $105, flagging weaker data center and PC spending despite potential tariff relief. Other firms, including Morgan Stanley and J.P. Morgan, have similarly lowered targets amid macro uncertainties. Dell Technologies Recieves Bullish Calls as AI Infrastructure Demand Surges Based on the consensus recommendation from 24 brokerage firms, Dell Technologies Inc's (NYSE:DELL) average brokerage recommendation is currently 2.0, indicating a "Outperform" status. The rating scale ranges from 1 to 5, where 1 signifies Strong Buy, and 5 denotes Sell. This article first appeared on GuruFocus.
Forbes
05-05-2025
- Business
- Forbes
Nvidia Builds An AI Superhighway To Practical Quantum Computing
At the GTC 2025 conference, Nvidia announced its plans for a new, Boston-based Nvidia Accelerated Quantum Research Center or NVAQC, designed to integrate quantum hardware with AI supercomputers. Expected to begin operations later this year, it will focus on accelerating the transition from experimental to practical quantum computing. 'We view this as a long-term opportunity,' says Tim Costa, Senior Director of Computer-Aided Engineering, Quantum and CUDA-X at Nvidia. 'Our vision is that there will come a time when adding a quantum computing element into the complex heterogeneous supercomputers that we already have would allow those systems to solve important problems that can't be solved today.' Quantum computing, like AI (i.e., deep learning) a decade ago, is yet another emerging technology with an exceptional affinity with Nvidia's core product, the GPU. It is another milestone in Nvidia's successful ride on top of the technological shift re-engineering the computer industry, the massive move from serial data processing (executing instructions one at a time, in a specific order) to parallel data processing (executing multiple operations simultaneously). Over the last twenty years, says Costa, there were several applications where 'the world was sure it was serial and not parallel, and it didn't fit GPUs. And then, a few years later, rethinking the algorithms has allowed it to move on to GPUs.' Nvidia's ability to 'diversify' from its early focus on graphics processing (initially to speed up the rendering of three-dimensional video games) is due to the development in the mid-2000s of its software, the Compute Unified Device Architecture or CUDA. This parallel processing programming language allows developers to leverage the power of GPUs for general-purpose computing. The key to CUDA's rapid adoption by developers and users of a wide variety of scientific and commercial applications was a decision by CEO Jensen Huang to apply CUDA to the entire range of Nvidia's GPUs, not just the high-end ones, thus ensuring its popularity. This decision—and the required investment—caused Nvidia's gross margin to fall from 45.6% in the 2008 fiscal year to 35.4% in the 2010 fiscal year. 'We were convinced that accelerated computing would solve problems that normal computers couldn't. We had to make that sacrifice. I had a deep belief in [CUDA's] potential,' Huang told Tae Kim, author of the recently published The Nvidia Way. This belief continues to drive Nvidia's search for opportunities where 'we can do lots of work at once,' says Costa. 'Accelerated computing is synonymous with massively parallel computing. We think accelerated computing will ultimately become the default mode of computing and accelerate all industries. That is the CUDA-X strategy.' Costa has been working on this strategy for the last six years, introducing the CUDA software to new areas of science and engineering. This has included quantum computing, helping developers of quantum computers and their users simulate quantum algorithms. Now, Nvidia is investing further in applying its AI mastery to quantum computing. Nvidia became one of the world's most valuable companies because the performance of the artificial neural networks at the heart of today's AI depends on the parallelism of the hardware they are running on, specifically the GPU's ability to process many linear algebra multiplications simultaneously. Similarly, the basic units of information in quantum computing, qubits, interact with other qubits, allowing for many different calculations to run simultaneously. Combining quantum computing and AI promises to improve AI processes and practices and, at the same time, escalate the development of practical applications of quantum computing. The focus of the new Boston research center is on 'using AI to make quantum computers more useful and more capable,' says Costa. 'Today's quantum computers are fifty to a hundred qubits. It's generally accepted now that truly useful quantum computing will come with a million qubits or more that are error corrected down to tens to hundreds of thousands of error-free or logical qubits. That process of error correction is a big compute problem that has to be done in real time. We believe that the methods that will make that successful at scale will be AI methods.' Quantum computing is a delicate process, subject to interference from 'noise' in its environment, resulting in at least one failure in every thousand operations. Increasing the number of qubits introduces more opportunities for errors. When Google announced Willow last December, it called it 'the first quantum processor where error-corrected qubits get exponentially better as they get bigger.' Its error correction software includes AI methods such as machine learning, reinforcement learning, and graph-based algorithms, helping identify and correct errors accurately, 'the key element to unlocking large-scale quantum applications,' according to Google. 'Everyone in the quantum industry realizes that the name of the game in the next five years will be quantum error correction,' says Doug Finke, Chief Content Officer at Global Quantum Intelligence. 'The hottest job in quantum these days is probably a quantum error correction scientist, because it's a very complicated thing.' The fleeting nature of qubits—they 'stay alive' for about 300 microseconds—requires speedy decisions and very complex math. A ratio of 1,000 physical qubits to one logical qubit would result in many possible errors. AI could help find out 'what are the more common errors and what are the most common ways of reacting to it,' says Finke. Researchers from the Harvard Quantum Initiative in Science and Engineering and the Engineering Quantum Systems group at MIT will test and refine these error correction AI models at the NVAQC. Other collaborators include quantum startups Quantinuum, Quantum Machines, and QuEra Computing. They will be joined by Nvidia's quantum error correction research team and Nvidia's most advanced supercomputer. 'Later this year, we will have the center ready, and we'll be training AI models and testing them on integrated devices,' says Costa.
Yahoo
05-04-2025
- Business
- Yahoo
Innodata (NasdaqGM:INOD) Unveils Generative AI Platform At GTC 2025
Innodata recently announced the beta launch of its Generative AI Test & Evaluation Platform at the GTC 2025, leveraging NVIDIA's advanced technology to bolster AI safety and performance for enterprises. Despite this significant product development, Innodata's share price fell 20% over the past week. This decline coincided with a broader market downturn triggered by global trading tensions and tariffs, which saw the Dow and S&P 500 plunging significantly. The overall market conditions, including a tech-heavy Nasdaq entering a bear market, likely amplified the adverse impact on Innodata's shares during this turbulent period. Be aware that Innodata is showing 2 possible red flags in our investment analysis. Find companies with promising cash flow potential yet trading below their fair value. Over the past five years, Innodata (NasdaqGM:INOD) has delivered a remarkable total shareholder return of very large value, highlighting considerable growth compared to the general market performance. Recently, the company has aggressively enhanced its position in the generative AI space. Key developments included the introduction of a beta version of the Generative AI Test & Evaluation Platform on March 19, 2025, following a significant earnings increase, with Q4 2024 sales reaching US$59.18 million and net income climbing to US$10.3 million from the previous year. Further amplifying its growth trajectory, Innodata has engaged in lucrative partnerships, evidenced by the June 2024 announcement of new development programs with a leading Big Tech client, projected to generate US$44 million in annualized revenue. Despite facing a class action lawsuit in February 2024, the company's raised revenue guidance in late 2024 and early 2025, along with strategic buybacks of 5.8% of shares as of November 2024, have underscored its resilience and attractive longer-term prospects. Assess Innodata's future earnings estimates with our detailed growth reports. This article by Simply Wall St is general in nature. We provide commentary based on historical data and analyst forecasts only using an unbiased methodology and our articles are not intended to be financial advice. It does not constitute a recommendation to buy or sell any stock, and does not take account of your objectives, or your financial situation. We aim to bring you long-term focused analysis driven by fundamental data. Note that our analysis may not factor in the latest price-sensitive company announcements or qualitative material. Simply Wall St has no position in any stocks mentioned. Companies discussed in this article include NasdaqGM:INOD. Have feedback on this article? Concerned about the content? with us directly. Alternatively, email editorial-team@ Sign in to access your portfolio
Yahoo
26-03-2025
- Business
- Yahoo
Nvidia's CPO Reveal Sets Stage for Wave of Optical Tech Announcements
J.P. Morgan expects more tech companies to announce co-packaged optics (CPO) plans following Nvidia's (NASDAQ:NVDA) reveal at GTC 2025. The bank is watching for new developments at the upcoming Optical Fiber Communications Conference, set to run from March 30 to April 3 in San Francisco. Warning! GuruFocus has detected 3 Warning Signs with NVDA. At GTC, Nvidia introduced a silicon photonics-based switch using CPO to support faster, short-range data links in AI data centers. CEO Jensen Huang said the company had developed the world's first 1.6 terabit per second CPO, using a micro ring resonator modulator and advanced TSMC (TSM) chip technology. J.P. Morgan analysts, led by Samik Chatterjee, noted in a client briefing that companies like Broadcom (NASDAQ:AVGO), Marvell (NASDAQ:MRVL), and potentially Cisco (NASDAQ:CSCO) may follow Nvidia's lead with similar announcements or product demos at OFC. Suppliers involved in Nvidia's optical efforts include Lumentum (NASDAQ:LITE), Coherent (NYSE:COHR), Corning (NYSE:GLW), and Fabrinet (NYSE:FN), which provide key components for its photonics infrastructure. Investors are focused on timelines for broader CPO adoption among major silicon vendors. This article first appeared on GuruFocus.
Forbes
25-03-2025
- Business
- Forbes
Nvidia Dynamo — Next-Gen AI Inference Server For Enterprises
Dynamo Inference Server At the GTC 2025 conference, Nvidia introduced Dynamo, a new open-source AI inference server designed to serve the latest generation of large AI models at scale. Dynamo is the successor to Nvidia's widely used Triton Inference Server and represents a strategic leap in Nvidia's AI stack. It is built to orchestrate AI model inference across massive GPU fleets with high efficiency, enabling what Nvidia calls AI factories to generate insights and responses faster and at a lower cost. This article attempts to provide a technical overview of Dynamo's architecture, features and the value it offers enterprises. At its core, Dynamo is a high-throughput, low-latency inference-serving framework for deploying generative AI and reasoning models in distributed environments. It integrates into Nvidia's full-stack AI platform as the operating system of AI factories, connecting advanced GPUs, networking, and software to enhance inference performance. Nvidia's CEO Jensen Huang emphasized Dynamo's significance by comparing it to the dynamos of the Industrial Revolution—a catalyst that converts one form of energy into another—except here, it converts raw GPU compute into valuable AI model outputs at an unparalleled scale. Dynamo aligns with Nvidia's strategy of providing end-to-end AI infrastructure. It has been built to complement Nvidia's new Blackwell GPU architecture and AI data center solutions. For example, Blackwell Ultra systems provide the immense compute and memory for AI reasoning, while Dynamo provides the intelligence to utilize those resources efficiently. Dynamo is fully open source, continuing Nvidia's open approach to AI software. It supports popular AI frameworks and inference engines, including PyTorch, SGLang, Nvidia's TensorRT-LLM and vLLM. This broad compatibility means enterprises and startups can adopt Dynamo without rebuilding their models from scratch. It seamlessly integrates with existing AI workflows. Major cloud and technology providers like AWS, Google Cloud, Microsoft Azure, Dell, Meta and others are already planning to integrate or support Dynamo, underscoring its strategic importance across the industry. Dynamo is designed from the ground up to serve the latest reasoning models, such as DeepSeek R1. Serving large LLMs and highly capable reasoning models efficiently requires new approaches beyond what earlier inference servers provided. Dynamo introduces several key innovations in its architecture to meet these needs: Dynamic GPU Planner: Dynamically adds or removes GPU workers based on real-time demand, preventing over-provisioning or underutilization of hardware. In practice, this means if user requests spike, Dynamo can temporarily allocate more GPUs to handle the load, then scale back, optimizing utilization and cost. LLM-Aware Smart Router: Intelligently routes incoming AI requests across a large GPU cluster to avoid redundant computations. It keeps track of what each GPU has in its knowledge cache (the part of memory storing recent model context) and sends each query to the GPU node best primed to handle it. This context-aware routing prevents repeatedly re-thinking the same content and frees up capacity for new requests. Low-Latency Communication Library (NIXL): Provides state-of-the-art, accelerated GPU-to-GPU data transfer and messaging, abstracting away the complexity of moving data across thousands of nodes. By reducing communication overhead and latency, this layer ensures that splitting work across many GPUs doesn't become a bottleneck. It works across different interconnects and networking setups, so enterprises can benefit whether they use ultra-fast NVLink, InfiniBand, or Ethernet clusters. Distributed Memory (KV) Manager: Offloads and reloads inference data (particularly 'keys and values' cache data from prior token generation) to lower-cost memory or storage tiers when appropriate. This means less critical data can reside in system memory or even on disk, cutting expensive GPU memory usage, yet be quickly retrieved when needed. The result is higher throughput and lower cost without impacting the user experience. Disaggregated serving: Traditional LLM serving would perform all inference steps (from processing the prompt to generating the response) on the same GPU or node, which often underutilized resources. Dynamo instead splits these stages into a prefill stage that interprets the input and a decode stage that produces the output tokens, which can run on different sets of GPUs. As AI reasoning models become mainstream, Dynamo represents a critical infrastructure layer for enterprises looking to deploy these capabilities efficiently. Dynamo revolutionizes inference economics by enhancing speed, scalability and affordability, allowing organizations to provide advanced AI experiences without a proportional rise in infrastructure costs. For CXOs prioritizing AI initiatives, Dynamo offers a pathway to both immediate operational efficiencies and longer-term strategic advantages in an increasingly AI-driven competitive landscape.
