Ryan Sagare’s Post

𝗬𝗼𝘂 𝗮𝗶𝗻'𝘁 𝘀𝗲𝗲𝗻 𝗻𝗼𝘁𝗵𝗶𝗻' 𝘆𝗲𝘁! When giants like AMD and NVIDIA team up, you know the tech world is about to be 𝗿𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗶𝘇𝗲𝗱.🔥 This isn't just another partnership; it's a 𝗴𝗮𝗺𝗲-𝗰𝗵𝗮𝗻𝗴𝗲𝗿 for AI and computing performance. Imagine the computing power when AMD's EPYC CPUs join forces with NVIDIA's GPUs. We're talking about 𝗻𝗼 𝗵𝗼𝗹𝗱𝗶𝗻𝗴 𝗯𝗮𝗰𝗸 levels of efficiency and scalability. Why should you care? Because this integration is set to redefine AI workloads and data-heavy applications. 🚀 It's like they've taken the ultimate strengths of both companies—AMD's CPU architecture and NVIDIA's GPU dominance—and created the most 𝗱𝘆𝗻𝗮𝗺𝗶𝗰 𝗱𝘂𝗼 of our times. And let's not overlook AMD's strategic alliance with Intel to turbocharge the x86 architecture. This isn't merely about staying relevant—it's about leading the charge in 𝗯𝗿𝗲𝗮𝗸𝗶𝗻𝗴 𝗻𝗲𝘄 𝗴𝗿𝗼𝘂𝗻𝗱 in technology. Here's the bottom line: If you're a tech enthusiast or professional, you need to keep your eyes on this! 👀 The synergy between AMD and NVIDIA is just the beginning of what's possible when industry leaders prioritize innovation through collaboration. Remember, in the world of tech, standing still means falling behind. 🚀 So, let's embrace this powerhouse collaboration and get ready for the 𝗻𝗲𝘅𝘁 𝗯𝗶𝗴 𝗹𝗲𝗮𝗽 in innovation! #AMD #NVIDIA #Innovation #TechRevolution

Intel Unveils New Chips to Compete with AMD, NVIDIA, and Qualcomm Intel has recently made notable strides in the semiconductor industry with the unveiling of its latest chip generation. This announcement marks a significant milestone for the company as it seeks to bolster its position in an increasingly competitive landscape dominated by industry giants such as AMD, NVIDIA, and Qualcomm.... Find out more on: https://lnkd.in/dxhx_Ssy #StudyTime #SmartLearning #QuckLearn #DailyLearning #LearnEveryday #LearningMadeEasy #StudyShorts #EduShorts #LearnWithMe #LearningIsFun #BrainTeasers #OnlineClasses #OnlineLearning #Tech #Technology #TechReview #Gadgets #TechNews #TechTips #TechTalk #TechTrends #TechVideos #Innovation #TechCommunity #TechLife #FutureTech #TechReviews #GadgetReview #ai #deeplearning

NVIDIA+Qualcomm+Google+Samsung vs Intel+AMD The recent collaboration between NVIDIA, Qualcomm, Google, and Samsung at the RISC-V Summit focuses on the open-source RISC-V architecture as a potential competitor to established players like Intel and AMD. -NVIDIA: it has been integrating RISC-V into its GPU microcontrollers for nearly a decade. Their presentation highlighted the architecture’s flexibility and its ability to cater to a wide range of applications, from gaming to AI. -Qualcomm: it showcased advancements in refining the RISC-V instruction set and emphasized the architecture’s role in enhancing AI and secure computing. -Samsung: it highlighted its use of RISC-V in embedded systems, leveraging Samsung Foundry to improve chip performance and expand the application of RISC-V technology. -Google: its DeepMind discussed their experiences with RISC-V in developing Tensor Processing Units (TPUs), showcasing the architecture's potential to transform AI hardware. Emerging Applications: While RISC-V chips may take time to penetrate mainstream PCs and servers, they are already making significant strides in AI and automotive sectors, with potential impacts on high-performance computing and generative AI. Semiconductor /drone broker: YM Innovation Technology (Shenzhen) Co.,Ltd Email: [email protected] [email protected] #electronicindustry #electronicmanufacturing #RISCV #embedded  https://lnkd.in/gkiS9k-i

🚀 Exciting news from the world of AI! 🤖💥 As the Director of Innovation, I'm always on the lookout for the latest advancements in technology. And today, I'm thrilled to share with you the groundbreaking developments announced by AMD at the Computex trade show in Taiwan. 🌍 Lisa Su, the CEO of Advanced Micro Devices, took the stage to unveil AMD's CPU, NPU, and GPU strategy for AI data centers and AI PCs. [2] This is a game-changer for the industry, as AMD is set to challenge the market leader Nvidia with its cutting-edge technology. 💪 One of the highlights of the announcement is the upcoming Instinct MI325X accelerators, set to be available in the fourth quarter of 2024. [3] [1] These AI accelerators will revolutionize data centers, enabling faster and more efficient AI processing. 🚀 But that's not all! AMD also revealed its plans for the Instinct MI350-series, powered by the CDNA4 architecture, which is set to launch next year. This is a clear indication that AMD is committed to pushing the boundaries of AI technology and staying ahead of the curve. 📈 In addition to AMD's impressive lineup of AI chips, we also have exciting news from Baidu, China's largest search engine. Baidu has made a breakthrough in AI technology by developing a system that can meld GPUs from different brands into one training cluster. This innovation will help sidestep shortages and further accelerate the development of AI technologies. 🌐 The pace at which tech companies are advancing in the field of AI is truly remarkable. It's an exciting time to be at the forefront of innovation, and I can't wait to see what the future holds. 💡 I would love to hear your thoughts on these groundbreaking announcements. How do you think these advancements will shape the future of AI? Let's start a conversation and share our insights! 🗣️ #AI #AMD #Nvidia #Baidu #Innovation #Computex #Technology #Future #DataCenters #AIChips #CDNA4 #GPUs #Insights References: [1] AMD announces MI325X AI accelerator, reveals MI350 and MI400 plans at Computex: https://lnkd.in/dmuYfZ4G [2] AMD unveils new AI chips to challenge Nvidia: https://lnkd.in/dZyCAPUy [3] Baidu's AI breakthrough can meld GPUs from different brands into one training cluster — company says new tech fuses thousands of GPUs together to help sidestep shortages: https://lnkd.in/dJdMCAmh

#inthemeantime how #green is your company #ceo #strategy #climategoals hopping on the AI bandwagon with #nvidia and #zuckerberg? Nvidia’s 3.76 million GPU shipments could consume as much 14,384 GWh (14.38 TWh ). The 14.4 TWh is equivalent to the annual power needs of more than 1.3 million households in the US. This also does not include AMD, Intel, or any of Big Tech’s custom silicon, nor does it take into account existing GPUs deployed or upcoming Blackwell shipments in 2024 and 2025. As such, the total energy consumption is likely to be far higher by the end of the year. https://lnkd.in/exV4BiCu

🔥 𝗡𝘃𝗶𝗱𝗶𝗮 𝗮𝗻𝗱 𝗔𝗠𝗗 𝗨𝗻𝘃𝗲𝗶𝗹 𝗡𝗲𝘅𝘁-𝗚𝗲𝗻 𝗔𝗜 𝗖𝗵𝗶𝗽𝘀! 🚀 Nvidia and AMD have launched their next-generation AI chips in Taiwan, intensifying the competition with Intel. 𝗡𝘃𝗶𝗱𝗶𝗮'𝘀 𝗥𝘂𝗯𝗶𝗻 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺: • New GPUs, Vera CPU, and advanced networking chips. • Available in 2026, following the Blackwell platform. • Jensen Huang: “A major shift in computing.” 𝗔𝗠𝗗'𝘀 𝗠𝗜325𝗫 𝗔𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗼𝗿: • Available in Q4 2023. • Annual product updates, including MI350 in 2025 and MI400 in 2026. • Lisa Su: “AI is our number one priority.” Both companies aim to dominate the booming AI semiconductor market, crucial for generative AI applications like ChatGPT. Stay tuned as we explore how these advancements can drive innovation in your business! 🔗 - https://lnkd.in/gnpBEirg #AI #TechInnovation #Nvidia #AMD #ArtificialIntelligence #Semiconductors #DataAnalytics #TechNews

AMD's recent advancements in AI technology position it as a serious competitor against Nvidia in the AI chip market. - 🚀 Leveraging high-bandwidth memory (HBM) and processor-in-memory (PIM) for enhanced performance. - ⚡ Achieving shorter server latency and lower energy consumption with innovative designs. - 🔥 New MI300X AI GPU shows promising potential in AI inference workloads. #AI #TechInnovation #ChipWar - AMD's new technologies reduce data movement, increasing efficiency. - High-bandwidth memory integration leads to significant performance gains. - Innovations from Xilinx acquisition boost AMD's AI capabilities. - AMD's MI300X AI GPU is set to challenge Nvidia's market dominance. AMD just won the AI arms race https://lnkd.in/gg6fQan8

AMD launched a new Artificial Intelligence chip on #Thursday that is taking direct aim at Nvidia’s data center graphics processors, known as #GPUs. Just read an insightful article about #AMD's latest leap into the #AI world with the #launch of its Instinct #MI325X chip, directly challenging #Nvidia’s dominance in the data center #GPU market. With #AI demand soaring, it’s fascinating to see how AMD is positioning itself to capture a significant share of this $500 billion market by #2028. Exciting times for the AI and semiconductor industries! #AI #Semiconductors #TechNews #Innovation #AMD #Nvidia #DataCenters

Intel Officially Launches Gaudi3 AI Chip: Slower Than NVIDIA H100, But at a Lower Cost Intel has officially launched its Gaudi3 accelerator for AI workloads. While the new chip is slower than NVIDIA’s highly popular H100 and H200 GPUs (designed for AI and HPC), Intel is betting on Gaudi3’s success by offering it at a lower price and with a lower total cost of ownership (TCO). Intel’s Gaudi3 processor features two chips, incorporating 64 Tensor Processing Cores (TPCs) with 256x256 MAC structures using FP32 accumulators, eight Matrix Multiplication Engines (MMEs) with 256-bit-wide vector processors, and 96MB of on-chip SRAM cache, providing a bandwidth of 19.2TB/s. In addition, Gaudi3 integrates 24 200GbE network interfaces and 14 media engines capable of handling H.265, H.264, JPEG, and VP9 to support vision processing. The processor comes with 128GB of HBM2E memory, divided into eight memory stacks, delivering a substantial bandwidth of 3.67TB/s. Compared to Gaudi2, Intel’s Gaudi3 represents a significant upgrade. Gaudi2 had 24 TPCs, two MMEs, and 96GB of HBM2E memory. However, it seems Intel has streamlined its TPCs and MMEs, as the Gaudi3 processor only supports FP8 matrix operations, as well as BFloat16 matrix and vector operations (dropping support for FP32, TF32, and FP16). In terms of performance, Intel claims Gaudi3 can deliver up to 1,856 BF16/FP8 matrix TFLOPS and up to 28.7 BF16 vector TFLOPS, with a TDP of around 600W. When compared to NVIDIA’s H100, Gaudi3’s BF16 matrix performance is slightly lower (1,856 vs. 1,979 TFLOPS), its FP8 matrix performance is half as fast (1,856 vs. 3,958 TFLOPS), and its BF16 vector performance is significantly lower (28.7 vs. 1,979 TFLOPS). More important than raw specifications is Gaudi3’s real-world performance. It will need to compete against AMD’s Instinct MI300 series, as well as NVIDIA’s H100 and upcoming B100/B200 chips. This remains to be seen, as much will depend on software and other factors. For now, Intel has presented slides claiming that Gaudi3 offers a significant value advantage over NVIDIA’s H100. Earlier this year, Intel stated that an accelerator kit with eight Gaudi3 chips would cost $125,000, translating to around $15,625 per chip. In comparison, the NVIDIA H100 currently sells for $30,678, so Intel is clearly aiming to undercut its competitors on price. However, with NVIDIA’s Blackwell-based B100/B200 GPUs potentially offering massive performance gains, whether Intel can maintain its competitive edge remains to be seen. 📖▶️: https://lnkd.in/ggnhUfnf

The competition in the AI chip market is heating up as Nvidia, Intel, and AMD battle to outpace each other. These companies are creating breakthrough performance with faster GPUs and CPUs for high-performance computing in servers and data centres. This progress and pace are increasing, set to redefine the AI landscape further. As specialised hardware advances, constructing massive AI clusters necessitates servers capable of handling immense computational demands. These servers require advanced cooling, high-speed interconnects, and robust power delivery. Integrating Next-Gen CPUs: Key Steps for Data Centres Assessment and Planning: Evaluate AI and HPC workloads and review existing infrastructure for bottlenecks. Hardware Integration and Selection: Choose compatible servers, implement cooling solutions, and ensure adequate power supply. Software Updates: Update OS and firmware, and optimise applications. Testing and Validation: Conduct benchmarking and stress tests. Deployment and Monitoring: Use phased rollout strategies and comprehensive monitoring tools. Training and Support: Train IT staff and establish support channels. Overcoming Challenges: Power and Sustainability Address power consumption and sustainability challenges by adhering to regulations, adopting energy-efficient technologies, and minimising environmental impact. Embracing Innovation Continuous adaptation and strategic integration are crucial. Data centres that embrace innovation and invest in the latest technologies will unlock the full potential of modern computational workloads and stay ahead in the evolving AI landscape. #AI #HPC #Nvidia #DataCentres #Innovation #Tech #AMD