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The Decoder writes about the latest hardware benchmarks for AI model training and predicting (inferencing) MLPerf being dominated by Nvidia and Nvidia "competing against itself" https://lnkd.in/eMSxW85X. #mlperf #artificialintelligence #nvidia #benchmark #thedecoder

Wow! The NVIDIA AI #H200 #GPU is an absolute beast with 141GB of HBM3e RAM which brings the GPU’s memory bandwidth to 4.8 terabytes per second.

Stability AI has published a new blog post that offers an AI benchmark showdown between Intel Gaudi 2 & NVIDIA's H100 and A100 GPU accelerators. The benchmarks show that Intel's solutions offer great value and can be seen as a respected alternative for customers who are eyeing a fast & readily available solution compared to NVIDIA's offerings. #iamintel #ai #GPU

💰 AMD launched Instinct MI300X in late 2023, claiming it to be the fastest AI chip in the world. However, with high specs, it may not enjoy the high margin, as AI chip giant NVIDIA does! 🔎 According to the results of MLPerf tests, in practical deployment for inference production applications, NVIDIA’s H200 outperforms the MI300X by over 40%. 💡Is NVIDIA’s high margin justifiable then? More: https://buff.ly/4cV0yj8 🔗 #AMD #MI300X #NVIDIA’s #H200

OpenAI Sora video tool deployed with 720k NVIDIA GPUs. #AI 🤝 Follow us on Discord 🔜: https://lnkd.in/gt823Zd3 🤝 Follow us on Whatsapp 🔜 https://wapia.in/wabeta _ ❇️ Summary: OpenAI's new text-to-video tool, Sora, requires a massive amount of AI GPU computing power, with Factorial Funds estimating that 720,000 NVIDIA H100 AI GPUs would be needed for peak times. This would cost $21.6 billion and consume 504,000,000W of power. NVIDIA's dominance in the AI GPU market is evident, with the company's record-breaking revenue pushing its market cap over $2.1 trillion. The demand for AI GPUs is expected to increase as Sora is adopted by major companies and individuals. The tool's potential impact on popular video platforms like TikTok and YouTube is significant, with an estimated 89,000 NVIDIA H100 GPUs needed to support creators on these platforms. However, this number may be higher due to factors such as FLOPS utilization, peak demand, and video generation processes. Hashtags: #chatGPT 1. #OpenAI Sora 2. #NVIDIA H100

Intel introduced its Gaudi 3 AI accelerators, dramatically undercutting Nvidia’s GPUs by up to 50%. The Gaudi 3 accelerators provide comparable performance in AI training and inference workloads. Despite the competitive pricing, Intel faces challenges in convincing enterprises to shift from Nvidia's CUDA platform. #Gaudi #Accelerators #Nvidia

Numerous alternatives to #NVIDIA's GPUs have emerged, able to address similarly demanding #AI and #ML workloads. Competitors' ecosystems are maturing, and publicly announced deployments of #Intel and #AMD hardware by AI players like Stability AI and Lamini, respectively, suggest that similar performance, as well as lower prices and lead times, are pushing companies to implement alternatives. But NVIDIA's head start is significant, and their innovation continues.

The First AI Benchmarks Pitting AMD Against Nvidia Rated horsepower for a compute engine is an interesting intellectual exercise, but it is where the rubber hits the road that really matters. We finally have the first benchmarks from MLCommons, the vendor-led testing organization that has put together the suite of MLPerf AI training and inference benchmarks, that pit the AMD Instinct “Antares” MI300X GPU against Nvidia’s “Hopper” H100 and H200 and the “Blackwell” B200 GPUs. The results are good in that they show the MI300X is absolutely competitive with Nvidia’s H100 GPU on one set of AI inference benchmarks, and based on our own estimates of GPU and total system costs can be competitive with Nvidia’s H100 and H200 GPUs. But, the tests were only done for the Llama 2 model from Meta Platforms with 70 billion parameters. This is useful, of course, but we had hoped to see a suite of tests across different AI models as the MLPerf test not only allows but encourages. https://lnkd.in/gyduVEbP

NVIDIA CEO Jensen Huang personally hand-delivered the first NVIDIA DGX H200 to OpenAI. You can see Greg Brockman, president of OpenAI, Huang posing with Brockman,and chief Sam Altman. The new GPU could be a much-needed addition to OpenAI’s arsenal as the organization is currently working on GPT-5 and plans to make Sora publicly available this year. NVIDIA introduced DGX H200 last year. The upgraded GPU, succeeding the highly sought-after H100, boasts 1.4 times more memory bandwidth and 1.8 times more memory capacity. These enhancements significantly enhance its capability to manage demanding generative AI tasks. Moreover, the H200 has a faster memory specification known as HBM3e, elevating its memory bandwidth to 4.8 terabytes per second from the H100’s 3.35 terabytes per second. Its total memory capacity also rises to 141GB, up from the 80GB of its predecessor. The setup achieves an impressive 1 exaflop of performance and offers 144 terabytes of shared memory, marking a significant leap from the previous generation NVIDIA DGX A100 introduced in 2020, which had considerably less memory. #NVIDIA #OpenAI

CUDOS Intercloud is changing the landscape of GPU-as-a-Service by utilising the capabilities of NVIDIA’s top-tier GPUs, including the A40, A6000, and V100. CUDOS Intercloud provides scalable and cost-effective solutions, making it ideal for high-performance computing tasks like AI and DePIN projects. By leveraging these powerful GPUs, CUDOS Intercloud offers a decentralized and efficient approach to GPU resources, paving the way for advancements in various computational fields. Dive into the article to explore the potential of this technology. Read my latest article on this topic:-https://lnkd.in/dtvnyQuP Try CUDOS Intercloud:- Intercloud.cudos.org #CUDOS #DePIN #Nvidia #AI