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Seamless & Performant AI Training on AMD Instinct ! Landing AI’s cutting-edge LVM training code is designed for versatility. Developed in PyTorch, it runs flawlessly on AMD GPUs without any code modifications using AMD’s open-source ROCm. Dive into the blog to see how Landing AI is making AI training more accessible and efficient for everyone! #AI #MachineLearning #PyTorch #LandingAI #AMD #Instinct #ROCm #LVM #TechInnovation

Landing AI benchmarks AMD vs. NVIDIA GPUs for computationally intensive LVM training, with surprising results. Whether it's AMD’s MI250 or NVIDIA's A100, see which GPU fits your AI projects. Explore our blog ➡️ https://lnkd.in/gMyHicHK #MachineLearning #GPUBenchmarking #NVIDIA #AMD #GPU

NVIDIA (#NVDA) just announced the release of #Blackwell, the most powerful GPU in the market. With an AI Superchip and 208B transistors, Blackwell features a 2nd Gen Transformer Engine, 5th Generation NVLink, RAS Engine, Secure AI, and Decompression Engine, making it the most advanced GPU on the market. Analysts estimate that Blackwell could potentially be 10-100x faster than NVIDIA's current Hopper/A100 GPUs for very large transformer model workloads 

They say when everyone is looking for gold, the one who sells shovels and picks gets rich. That's exactly what's happening here. Look at the stock growth of NVIDIA (a producer of AI processing GPUs) compared to tech giants in the past year. Here are some key takeaways: NVIDIA's stock price has increased by over 100% in the past year, while the tech giants have seen little to no growth. This is because NVIDIA is a key player in the rapidly growing AI market.The demand for AI processing GPUs is high, and NVIDIA is well-positioned to capitalize on this demand.

🚀 Exciting news in the world of AI and Large Language Models (LLMs)! NVIDIA's latest TensorRT-LLM optimizations are setting new benchmarks in LLM inference performance, combining high-compute throughput with extensive memory capabilities. Key Highlights: 🏎️ A stunning 6.7x speedup in LLM inference on NVIDIA H200 GPUs, especially for the Llama 2 70B model. 🤖 The ability to run massive models like Falcon-180B, previously requiring eight NVIDIA A100 GPUs, now on a single H200 Tensor Core GPU. 💾 Featuring 141 GB of HBM3e memory and nearly 5 TB/s of memory bandwidth. The secret sauce? An optimized technique called Grouped Query Attention (GQA) - enhances the multi-head attention mechanism central to models like Llama 2 70B. Plus, custom INT4 AWQ advancements are making these leaps possible. What does this mean? Faster, more efficient processing of some of the largest LLMs on fewer GPUs, opening new doors in AI research and applications. #NVIDIA #AI #LLMs #TensorRT #Innovation #TechNews

NVIDIA has emerged as a dominant force, particularly in the realm of AI and machine learning. NVIDIA's dominance in the AI GPU market is not just about having the best hardware. It's about creating a comprehensive solution that integrates seamlessly into the AI development pipeline. Their GPUs are the go-to choice for data centers, research institutions, and tech giants alike. NVIDIA's share price has seen exponential growth, reflecting investor confidence in their continued dominance. As AI continues to grow, the demand for high-performance GPUs will only increase, further driving NVIDIA's market position. #NVIDIA #AI #MachineLearning #TechInnovation #Investing #GPUs #ArtificialIntelligence #TechMonopoly

The proposed supercomputer will reportedly utilize 100,000 Nvidia H100 GPUs (graphic processing units) and be at least four times larger than the biggest GPU clusters currently in existence, such as those “used by Meta to train its AI models.” This immense power is necessary to train and run the ever-evolving Grok language model. https://lnkd.in/dg9ciig2

💻 NVIDIA is going full-in on Generative AI with their TensorRT support for Stable Diffusion that can generate images up to 10x faster than Apple M2 chips! 👉 What is TensorRT? Short for Tensor Runtime, it is a deep learning inference optimizer and runtime library developed by NVIDIA. It's designed to optimize and accelerate the inference process of deep learning models on NVIDIA GPUs. 👉 What's new? The latest #stablediffusion update significantly enhances performance on NVIDIA RTX GPUs through TensorRT integration. To utilize this feature, install the necessary extension and create optimized engines (check link). This upgrade supports versions 1.5, 2.1, SDXL, SDXL Turbo, and LCM of Stable Diffusion. For optimal performance with the more resource-intensive SDXL and SDXL Turbo, a GPU with at least 12 GB of VRAM is recommended. 🔗 Try it out now: https://lnkd.in/dCxYXU64 #ai #nvidia #generatieveai #aiart #gpu #gpuwar #ml #artificialintelligence #machinelearning

QCT latest AI platforms are built to scale #GenAI at scale. Powered by NVIDIA Grace Blackwell and Grace Hopper Superchips, these systems also support NVIDIA NVLink to allow up to 72 GPUs interconnected as one for the most demanding AI challenges. Learn more: https://bit.ly/3JnUGmp

Learn why the NVIDIA Jetson AGX Orin is the leading platform for running transformer models like GPT-J, vision transformers, and stable diffusion at the edge.