Altair’s Post

Maximizing application performance, thermal efficiency, and battery life is key to unlocking new and enhanced generative AI experiences. By utilizing an appropriate processor in conjunction with an NPU, heterogeneous computing achieves just that. CPUs provide sequential control and immediacy, GPUs are great for streaming parallel data, and NPUs handle core AI workloads with scalar, vector, and tensor math. Learn more about the importance of NPUs in unlocking on-device generative AI experiences here: https://lnkd.in/gKVyPuE6

🚀 20 Petaflops 🚀 208 billion transistors 🚀 1 trillion parameter models can be trained 🚀 based on a 4nm architecture all in one GPU? #nvidia CEO Jensen Huang presented yesterday at the GTC the new GPU architecture Blackwell. It comes with a new data type FP4 and is inferencing and will allow the fastest computing of smaller packages data and deliver the results back much faster. Crazy performance, compared to my master thesis, were i worked with a super cluster computer with 32CPU`s (but to be fair it was in 2006 😊). The future is now, it will exponential increase the power and possibilities of simulations. Be curious! #stammtischtalk #ai

Attend this upcoming webinar to dive into NPU architecture, including how it works, its features, advantages, and capabilities in accelerating neural network computations on Intel Core Ultra processors. Machine learning engineer Alessandro Palla will walk you through the practical aspects of deploying performant LLM apps, as well as fast LLM prototyping with the Intel NPU Acceleration Library. Register now: https://intel.ly/3VsuIUq #ArtificialIntelligence #LargeLanguageModel #NeuralProcessingUnit

Together, CDW and NVIDIA bring you GPU deep learning for data centers, virtualization and visualization to fuel the next era of computing. Explore what we can do to help you transform your business today. #DigitalTransformation

Attention, as the core layer of the ubiquitous Transformer architecture, is the bottleneck for large language models and long context applications. FlashAttention (and FlashAttention-2) pioneered a way to accelerate attention on GPUs by minimizing memory reads/writes, and it is now being used by most libraries to speed up Transformer training and inference. This has led to a dramatic increase in LLM context lengths over the past two years, from 2-4K (GPT-3, OPT) to 128K (GPT-4), and even 1M (Llama 3). However, despite its success, FlashAttention has not fully leveraged the new capabilities of modern hardware, achieving only 35% of the theoretical peak FLOP utilization on H100 GPUs with FlashAttention-2. In this blog post, we describe three key techniques for accelerating attention on Hopper GPUs: (1) leveraging the asynchronicity of Tensor Cores and TMA by overlapping bulk computation and data movement through warp-specialization, (2) interleaving blocked matmul and softmax operations, and (3) taking advantage of hardware support for low-precision FP8 inconsistent processing. #AI

Vector Search is THE key enabler for today's hot use-cases like Generative AI, Recommender Systems and many feature store implementations. GPUs perform vector similarity search at lower latency, and achieve higher throughput for every level of recall for both online and batch processing. Accelerating Vector Search: Using GPU-Powered Indexes with RAPIDS RAFT | NVIDIA Technical Blog

Accelerating the Most Important Work of Our Time NVIDIA A100 Tensor Core GPU delivers unprecedented acceleration at every scale to power the world’s highest-performing elastic data centers for AI, data analytics, and HPC. Powered by the NVIDIA Ampere Architecture, A100 is the engine of the NVIDIA data center platform. A100 provides up to 20X higher performance over the prior generation and can be partitioned into seven GPU instances to dynamically adjust to shifting demands. The A100 80GB debuts the world’s fastest memory bandwidth at over 2 terabytes per second (TB/s) to run the largest models and datasets. #IC-CHIPS #AI #components#EMS#OEM#DEM

Looking to speed up #ML development with #SyntheticData? ⚡ Learn from Edge Impulse and NVIDIA Omniverse experts in this webinar on May 14.

Day 2 of Forum Teratec in Paris Today at 3pm James Coomer will shed some light on "How to Build the World’s Fastest and Most Efficient AI Systems" in his keynote. Highly recommended to watch! Or stop by the DDN Storage booth to find out more about the refercence architecture of DDN A3I solutions with NVIDIA DGX BasePOD and SuperPOD. #AI #HPC #datastorage

NVIDIA-powered data science workstations are tested and optimized with special software built on NVIDIA CUDA-X AI - a collection of over 15 libraries - that enable modern computing applications to benefit from NVIDIA’s GPU-accelerated computing platform. Click on the following link to learn more: https://bit.ly/2BVkgzT . . . . . . . . . . . . . . . . . . . . . . . #Tyrone #Netweb #NVIDIA #data_science #workstations #GPUaccelerated #computing #technology #datascienceplatform #acceleratedcomputing #libraries #optimization #performance #AI #machinelearning #computingpower #computingapplications #techinnovations