Rutvi Rajesh’s Post

🚀 Breakthrough in AI Acceleration: Introducing FlashAttention-3! 🚀 FlashAttention has already revolutionized Transformer models by making attention 4-8x faster. Now, FlashAttention-3 takes it to the next level: ✨ 1.5-2x faster on FP16 compared to previous versions ✨ Achieves up to 740 TFLOPS on NVIDIA H100 GPUs (75% utilization) ✨ FP8 precision pushing close to 1.2 PFLOPS! Harnessing the power of modern GPUs, this update brings significant speedups in training and inference for large language models and other AI systems relying on attention mechanisms. What does this mean for AI development? Faster training More efficient inference Capability to work with even larger models The implications for research and practical applications are incredibly exciting! #AIInnovation #MachineLearning #FlashAttention #GPUComputing #TransformerModels

🤖 Scaling AI innovation globally is no small feat, but LetzAI made it possible by partnering with Gcore. Facing global GPU shortages, LetzAI's CEO and co-founder, Misch Strotz turned to Gcore’s NVIDIA H100 GPUs and Everywhere Inference to power their next-gen AI image generation platform. 💻 Learn how Gcore enables businesses to unlock AI’s full potential ➡️ https://buff.ly/40mWrbs #AI #edgeAI #artificialintelligence #nvidiaGPU #innovation #generativeAI

When we scale our training to multiple NVIDIA GPUs, the batch size that we assign to each GPU is NOT the overall "effective" batch size. It should be the size of the mini-batch multiplied by the number of GPUs. As a result, when more GPUs are used, the effective batch size can become very large (closer to the full batch size), which leads to less variance. So, technically, can we increase the batch size indefinitely? Let's examine the relationship between batch size, throughput, and convergence. ✅ Throughput: Yes, there is a positive relationship. We will always achieve more throughput (images/s, token/s, etc.) as we increase the number of GPUs, regardless of the neural network models. ❌ Convergence: Wait! There are limits. Ideally, as we increase the batch size, we would expect the number of steps required for convergence to decrease, right? But beyond a certain batch size (let’s say, greater than 2^5, as shown in the figure below), the number of steps for convergence flattens out, regardless of the neural network models. Well, what do you think about this phenomenon? Can you also describe the impact of batch size on accuracy? 🤔💡 Read the original paper: https://lnkd.in/gFrDTq4P #artificialintelligence #ai #machinelearning #deeplearning #generativeai #nvidia #lintasarta #gpu

GPU-accelerated fraud detection? Yes! See how ArangoDB + NVIDIA GPUs make it possible. Register today to unlock new possibilities in graph analytics! https://okt.to/P2kX8N #Innovation #GraphDatabase #AI #MachineLearning #GraphTech #GraphProcessing #DataScience #NetworkX

🖥️ Best NVIDIA GPUs for AI Inference: A Complete Guide 🚀 📢 New Medium Article Alert! 📝 I just published a new article on Medium where I dive deep into the best NVIDIA GPUs for AI inference. If you're working in AI development, machine learning, or LLM inference and need help choosing the right GPU for your setup, this guide will provide the insights you need! 💡 https://lnkd.in/d-pnNE-Q I’d love to hear your thoughts on which GPUs you’ve found to be most effective for your AI workloads. Let’s connect and discuss in the comments! hashtag #AI hashtag #MachineLearning hashtag #GPU hashtag #Tech hashtag #NVIDIA hashtag #AIInference hashtag #DeepLearning hashtag #LLM hashtag #CloudComputing

GPU-accelerated fraud detection? Yes! See how ArangoDB + NVIDIA GPUs make it possible. Register today to unlock new possibilities in graph analytics! https://okt.to/gSP8xJ #Innovation #GraphDatabase #AI #MachineLearning #GraphTech #GraphProcessing #DataScience #NetworkX

GPU-accelerated fraud detection? Yes! See how ArangoDB + NVIDIA GPUs make it possible. Register today to unlock new possibilities in graph analytics! https://okt.to/S2kUyv #Innovation #GraphDatabase #AI #MachineLearning #GraphTech #GraphProcessing #DataScience #NetworkX

Deep Learning Hardware: Powering AI Revolution Deep learning thrives on specialized hardware for faster computations and energy efficiency. Key options include: GPUs: Ideal for parallel processing 🖥️ TPUs: Google’s chips for AI workloads ⚡ FPGAs: Customizable for specific tasks 🛠️ ASICs: Optimized for deep learning 🚀 Choosing the right hardware accelerates model training and inference, making AI solutions smarter and faster. 📞 +1-929-672-1814 | ✉️ info@genai-training.com | 🌐 www.genai-training.com #DeepLearning #AIHardware #GPUs #TechInnovation #AI

GPU-accelerated fraud detection? Yes! See how ArangoDB + NVIDIA GPUs make it possible. Register today to unlock new possibilities in graph analytics! https://okt.to/QnxZjD #Innovation #GraphDatabase #AI #MachineLearning #GraphTech #GraphProcessing #DataScience #NetworkX