Latest news with #TensorRT
Techday NZ
22-05-2025
- Business
- Techday NZ
NVIDIA expands AI tools for Windows RTX PCs & Azure users
NVIDIA and Microsoft have announced a series of updates aimed at advancing artificial intelligence capabilities on Windows RTX AI PCs and within Azure cloud services. According to NVIDIA, recent announcements cover new tools and integrations designed to make AI development and deployment more accessible. These range from local inference enhancements on RTX AI PCs to advanced model hosting and deployment through microservices in Azure. In a detailed blog post on the NVIDIA AI Garage, the company highlighted new support for TensorRT by Windows ML, as well as the introduction of NIM microservices. NIM microservices are described as pre-packaged, optimised AI models that can be used in widely adopted applications. Project G-Assist, NVIDIA's AI assistant for RTX PCs, is expanding its capabilities with new community plug-ins. These include integrations with Google Gemini web search, Spotify, Twitch, IFTTT, and SignalRGB. The company stated, "Those looking for a simple, no-code way to dive into AI development can tap into Project G-Assist - the RTX PC AI assistant in the NVIDIA app - with new community plug-ins now available, including Google Gemini web search, Spotify, Twitch, IFTTT and SignalRGB." During Microsoft Build, NVIDIA announced enhanced support for GeForce RTX GPUs in Windows ML. The company said that Windows ML will now automatically use the TensorRT for RTX inference library, aiming to deliver higher performance and more rapid deployment for AI applications. Compared to DirectML, NVIDIA claims that TensorRT offers more than 50% faster performance for AI workloads on PCs. Aside from performance, NVIDIA also emphasised ease of use for developers. The company explained, "Windows ML also delivers quality-of-life benefits for developers. It can automatically select the right hardware - GPU, CPU or NPU - to run each AI feature, and download the execution provider for that hardware, removing the need to package those files into the app. This allows for the latest TensorRT performance optimisations to be delivered to users as soon as they're ready." The updates also expand the AI ecosystem on Windows 11 PCs. NVIDIA outlined how application developers can take advantage of a set of software development kits (SDKs) to add new AI-powered features or enhance application performance. Recent updates from top applications include LM Studio upgrading to the latest CUDA version, resulting in a performance increase of over 30%. Topaz Labs is releasing a generative AI video model that uses CUDA technology for accelerated video quality enhancement. Chaos Enscape and Autodesk VRED are both adding DLSS 4 to their offerings for improved performance and image quality, while BiliBili is incorporating NVIDIA Broadcast features such as Virtual Background to enhance livestream quality. On the local AI front, NVIDIA announced that, during Computex 2025, it will release the FLUX.1-schnell NIM microservice, which is targeted at fast image generation. The FLUX.1-dev NIM microservice will also be updated to support a broader range of GeForce RTX 50 and 40 Series GPUs. The firm stated that these NIM microservices provide improved speeds due to TensorRT acceleration and the use of quantised models. Additionally, the company noted, "On NVIDIA Blackwell GPUs, they run over twice as fast as running them natively, thanks to FP4 and RTX optimisations. Azure Discovery will integrate NVIDIA ALCHEMI and BioNeMo microservices to accelerate materials science and drug discovery workflows." Project G-Assist is also becoming more accessible with the introduction of the Project G-Assist Plug-in Builder. This ChatGPT-based application allows for no-code or low-code development using natural language commands. This aims to simplify the process of creating plug-ins for Project G-Assist, NVIDIA's experimental AI assistant integrated in its app. NVIDIA has also made available new open-source plug-in samples on GitHub, demonstrating ways on-device AI can improve PC and gaming workflows. Highlights include an updated Gemini plug-in featuring real-time web search capabilities via Google's free-to-use large language model, an IFTTT plug-in for creating automations across internet-connected endpoints, and a Discord plug-in to enable users to share game highlights or messages directly to Discord servers.
Yahoo
20-05-2025
- Yahoo
NVIDIA以搭載GeForce RTX顯示卡的PC加速推動AI應用，Project G-Assist助理可透過外掛擴充功能
日前公布入門定位的GeForce RTX 5060顯示卡與相關設計筆電將於美國當地時間5月19日正式上市後，NVIDIA更進一步說明如何藉由搭載GeForce RTX顯示卡的PC裝置加速推動人工智慧應用。 其中，隨著升級第5代Tensor Core設計，以及第4代RT Core設計，並且換上GDDR7顯示記憶體，PCIe連接埠規格也換成Gen 5規格的GeForce RTX 5060，讓更多入門級別的PC裝置可藉由DLSS 4技術加持，讓更多預算成本有限的遊戲玩家也能透過人工智慧技術升頻獲得更好遊戲畫面表現，並且能在1080P解析度下最高畫質設定，仍可實現100fps以上穩定畫面輸出。 而在搭載GeForce RTX 5060、同樣支援DLSS 4技術、厚度控制在1.49公分內的筆電陸續進入市場，更代表以Blackwell顯示架構加速運算的人工智慧應用體驗能廣泛進入更多用戶族群。 為了進一步推動人工智慧應用發展，NVIDIA先前已經針對遊戲與應用服務提供AI PC軟體堆疊資源，同時也強調在其TensorRT加速之下，將比使用微軟的DirectML能發揮更高人工智慧執行效能。 此次宣布將於6月推出的TensorRT for RTX開發工具，標榜能比微軟的DirectML提高2倍執行效能，並且對應所有RTX GPU，同時也做好GPU運作最佳化，更以僅有1/8資料庫檔案大小加快執行效率。 另一方面，NVIDIA也說明其AI SDK工具資源已經協助強化超過150款創作及人工智慧應用服務。 其中，可在電腦端以API形式呼叫各類大型語言模型的LM Studio，已經藉由新版CUDA工具提升30%執行效能，而透過人工智慧將影片最佳化調整的Topaz Video AI，同樣也以CUDA工具提高影片生成效率，而包含Autodesk VRED 3D可視化軟體，以及Chaos的3D即時渲染軟體Enscape均以DLSS 4技術提升畫面表現，Bilibili (嗶哩嗶哩)則是藉由NVIDIA的廣播特效技術增加線上直播體驗。 至於先前提出的加速推理微服務NVIDIA NIM，此次也宣布進一步對應至所有RTX GPU，藉此讓更多搭載GeForce RTX顯示卡的PC裝置，都能藉由TensorRT加快各類人工智慧微服務運作，並且相容主流的人工智慧應用工具資源，更可讓開發者能更容易將其人工智慧應用服務佈署於終端PC裝置或雲端。 NVIDIA同時也宣布推出更多NVIDIA微服務與NVIDIA AI Blueprints人工智慧參考工作流程，其中包含Flux AI的flux.1 dev及flux.1 schnell圖像生成模型，以及可讓3D圖像正確生成的參考工作流程。 另外，針對以Ace技術、微服務建構的NVIDIA遊戲內助理服務Project G-Assist則是增加更多外掛程式，使其可串接Discord、Google Gemini、Twitch、IFTTT、Spotify等服務，更允許開發者透過G-Assist Builder工具打造適用於G-Assist的外掛程式，使其增加更多應用功能。 更多 Intel實際展示以18A製程生產的「Panther Lake」處理器，預計2025年下半年量產 透過兩大方針打造全新Intel，陳立武：挹注技術力打造更好處理器、傾聽客戶需求 Intel推出更具性價比、標榜能彈性擴充的工作站繪圖加速卡Arc Pro B50、B60
Yahoo
11-05-2025
- Business
- Yahoo
Revenue Powerhouse Nvidia (NVDA) Plots Return to Stock Stardom
Whenever I analyze Nvidia (NVDA), I see a company perfectly poised to reap the tremendous value of AI growth today. Nvidia isn't simply along for the ride. It's creating the tools by making meaningful chips, software, and services that power AI systems. I'm enthusiastic and bullish about its potential because of its leadership position and pioneering attitude. However, investors must monitor changing economic trends, emerging AI advancements, and geopolitical risks. Discover companies with rock-solid fundamentals in TipRanks' Smart Value Newsletter. Receive undervalued stocks, resilient to market uncertainty, delivered straight to your inbox. Just yesterday, DBS analyst Fang Boon Foo maintained his bullish stance on NVDA stock by reiterating his Buy rating and citing NVDA's dominance in the AI-chip market as a 'significant factor' underpinning the company's lead in cutting-edge GPUs for high-powered applications such as AI and machine learning. The analyst also sees NVDA's plans to regularly upgrade its AI accelerators with Blackwell chips as a reason to expect eight consecutive quarters of substantial revenue increases when NVDA reports Q1 earnings later this month. Nvidia hardware dominates now, but emerging open-source AI tools like DeepSeek's large language models (LLMs) and Mixture-of-Experts (MoE) architectures present intriguing possibilities. DeepSeek constructed a strong model similar to GPT-4 with considerably fewer GPUs—only 2,048 Nvidia H800 units, while tens of thousands are normally required. DeepSeek's method dramatically reduces the computing power needed by using only essential model parts for each request. This could slow down the future need for GPUs. Now, more people can use AI, which helps Nvidia, but there might be less demand later if these methods are widely used. In particular, companies may choose smaller, improved AI systems, leading to buying fewer GPUs for each use. These cheaper offerings are the product of optimized algorithms and model advancements, including 4-bit quantization and sparsity. However, Nvidia's forward investment in software infrastructure, such as TensorRT and Triton Inference Server, continues to make its hardware appealing despite such efficiency gains. From a macroeconomic perspective, President Trump's and Treasury Secretary Scott Bessent's re-election provides compelling reasons for Nvidia to be hopeful. Their policies, particularly reduced corporate taxes, accelerated depreciation, and encouragement of technological research and development, are likely to yield significant benefits. Specifically, permitting businesses to fully depreciate their technology expenditures will enhance the demand for Nvidia's hardware as enterprises ramp up investments in AI infrastructure. Bessent's latest remarks reveal a strategy favoring tax benefits for industries like AI and quantum computing, underscoring the government's pro-growth stance. Lower business taxes and sensible expense guidelines might substantially drive Nvidia's net income and earnings per share upward, boosting the company's intrinsic value. In addition, expected deregulation to streamline infrastructure projects would help Nvidia, as it would lower the hurdles for its data center customers. More straightforward approvals for major infrastructure could lead to more AI projects, driving Nvidia hardware sales. Also, bullish expectations for monetary policy, including possible interest rate cuts in late 2025, could lower financing costs for Nvidia's customers, thus lengthening the AI investment cycle. Yet, Nvidia's future is not free of peril, especially from geopolitical risk. Trump's unpredictable trade policy (slapping triple-digit tariffs on Chinese imports and baseline universal tariffs of 10%) poses a clear and present danger to Nvidia's operating strategy. Tariffs have also been suggested for Taiwan, Nvidia's main ally for manufacturing. These tariffs could deeply affect Nvidia's normally high margins or require passing the cost on to purchasers, potentially affecting top-line growth. Reshuffling the supply chain, including moving assembly to the U.S., will eventually mitigate the tariff impact, but it requires time. Additionally, worsening U.S.-China tensions jeopardize Nvidia's Chinese market, which accounts for about 20% of its data center revenue. China's possible retaliatory actions, such as preferring local AI chip vendors like Huawei's HiSilicon, could meaningfully dent Nvidia's long-term growth outlook in the region. Therefore, Nvidia's geopolitical risk is a key consideration for investors. My base-case projection sees Nvidia steady as the initial AI hype fades away, landing at approximately $5 in TTM non-GAAP EPS by the middle of May 2026. With a 32x non-GAAP P/E multiple (a conservative investor sentiment adjustment), Nvidia could be at about $160 per share. In a best-case situation, Nvidia makes money from its expanding software and services supported by good economic policies. With steady earnings growth to possibly $5.50 a share, a higher valuation multiple of approximately 35x is warranted, which puts the stock price at around $190. On the other hand, a bear case accounts for strong tariff effects, deeper market share losses from efficiency improvements, and increased global tensions that can notably reduce Nvidia's earnings and access to markets. In this instance, earnings can dip to around $4.75 EPS, leading to a more conservative 25x multiple, which gives a stock price of $120. On Wall Street, Nvidia has a consensus Strong Buy rating based on 34 Buys, five Holds, and just one Sell rating acquired over the past three months. The average NVDA price target is $164.35, indicating a 40% upside potential over the next 12 months. As an investor, I believe Nvidia is strongly positioned in the emerging AI landscape, supported by solid economic fundamentals and a robust ecosystem strategy. That said, I'm closely watching shifts in the broader economy, productivity gains from initiatives like DeepSeek, and geopolitical developments that could alter the company's growth path. Investors should pay attention to trends in gross margins, the pace of revenue expansion from AI software, and the evolving dynamics of U.S.-China trade, all of which will shape the outlook in the months ahead. At present, I remain highly bullish — Nvidia is my largest holding, representing 15% of my portfolio. I'm confidently targeting $160 per share over the next 12 months. 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Techday NZ
07-05-2025
- Business
- Techday NZ
NVIDIA launches AI Blueprint for 3D image creation in Blender
NVIDIA has introduced a new AI Blueprint that enables users to generate images guided by 3D scenes using Blender. This tool combines basic 3D scene design with depth mapping to help control image composition more precisely during AI generation. At the core of the process is FLUX.1-dev, an AI model developed by Black Forest Labs, which interprets the scene's spatial layout alongside text prompts to produce visuals that match the intended design. Depth maps play a crucial role by providing the spatial context needed for the model to understand scene structure. This technique simplifies the process by removing the need for detailed textures or complex objects, instead relying on general spatial information. With the scenes rendered in 3D, users have the flexibility to move elements and adjust camera angles to suit their creative goals. The Blueprint includes an NVIDIA NIM microservice that helps deploy the FLUX.1-dev model efficiently on RTX GPUs, using TensorRT for faster inference. It's packaged with an installer and comprehensive deployment instructions, making it accessible for AI artists looking to integrate generative tools into their workflow. Beyond entry-level users, the Blueprint is also designed to accommodate advanced developers. It offers a customisable pipeline that can be modified for more sophisticated needs. NVIDIA provides supporting materials like sample assets, detailed documentation, and a preconfigured environment to help streamline experimentation and creation. Optimised for NVIDIA RTX AI PCs and workstations, the solution benefits from the company's Blackwell architecture. The FLUX.1-dev model has been fine-tuned using TensorRT and quantised to FP4 precision, resulting in more than double the inference speed compared to traditional FP16 PyTorch implementations. There are also FP8 model versions tailored for GPUs based on the Ada Lovelace architecture, further expanding compatibility and performance. Quantising to FP4 significantly reduces the model size, lowering memory requirements while maintaining high performance, cutting VRAM needs by more than half compared to FP16. Currently, NVIDIA offers ten NIM microservices across areas like image generation, natural language processing, speech AI, and computer vision. The company plans to continue building out its portfolio with more AI blueprints and services designed to accelerate creative and technical workflows.
Globe and Mail
11-04-2025
- Business
- Globe and Mail
Nvidia Corporation: KeyBanc Capital Markets Reiterates 'Bullish' Outlook
Nvidia (NVDA) On April 9, 2025, KeyBanc Capital Markets issued a research report and reaffirmed its 'Buy' rating on Nvidia Corp (NVDA) and maintained its 12-month price target of USD 190 per share, reflecting the firm's continued confidence in Nvidia's AI leadership, strong earnings momentum, and dominant position in high-performance computing. Why the Bullish Call from KeyBanc? 1. AI Demand Continues to Outpace SupplyKeyBanc sees continued global demand for Nvidia's AI GPUs, particularly the H100 and upcoming B100 chips, as hyperscalers like Microsoft, Amazon, Meta, and Google expand their AI infrastructure. The firm expects this trend to drive revenue and margin expansion into late 2025 and beyond. 2. Strong Data Center GrowthNvidia's data center segment is now its largest revenue driver, and KeyBanc believes it has longer-term durability. The bank expects consistent spending from cloud providers, sovereign AI initiatives, and enterprise adoption of generative AI models to support double-digit growth. 3. Expanding Ecosystem & Software MonetizationThe firm also highlighted Nvidia's growing software ecosystem — including CUDA, TensorRT, and DGX Cloud — as underappreciated drivers of future earnings. These platforms position Nvidia not just as a chipmaker, but as a core AI infrastructure provider. Valuation Perspective Rapid earnings growth Strong free cash flow generation High visibility into AI hardware demand The 12 month target forecast of $190 per share reflects a premium multiple on Nvidia's expected FY2026 earnings, supported by bullish expectations for sequential revenue growth and operating leverage. Outlook KeyBanc's reaffirmation of Nvidia as a top AI play suggests continued institutional confidence in the stock's long-term trajectory. For investors looking for exposure to AI, cloud infrastructure, and advanced computing, Nvidia remains a key holding — and the updated $190 target signals that analysts still plenty of upside for the stock.
