SES AI Corp’s Post

GTC is almost here, and I'm excited to share that I'll be presenting a session on developing generative models that achieve exceptional quality and efficiency. Join me to explore the transformative potential of neural architecture search for generating resource-efficient LLMs such as DeciLM, and learn how to achieve high throughput, low latency, and cost-effectiveness on commonly available GPUs. If you're wondering whether high-performance generative AI can be cost-effectively deployed, this session on March 19 at 4:30 p.m. PDT (11:30 p.m. GMT) is a must-attend. See you there! Learn more here > https://lnkd.in/dz4mnzjg #gtc2024 #gtc #generativeai

Curious about the performance of Deep Neural Networks (DNN) on different hardware? Join Mirabilis Design at DAC 2024 on June 26th at 10 AM. Our session on "Comparing the Performance of DNN on CPU, GPU, and TPU" leverages VisualSim to provide detailed performance metrics. VisualSim enables accurate simulation of neural network workloads, allowing designers to choose the optimal hardware configuration for their AI applications. Visit Booth 2342 at DAC 2024! Checkout for more info: https://lnkd.in/gxEp2ekD #DNN #AI #PerformanceAnalysis #VisualSim

VMware Private AI with Intel - Chris Wolf and Das Kamhout is a very interesting support for OpenVINO. To illustrate what all OpenVINO enables, here is a summary of the thesis ACCELERATION OF FACE RECOGNITION ALGORITHM WITH NEURAL COMPUTE STICK 2 by my daughter Eva Micankova, with her permission here is a brief summary. The acceleration speed using NCS2, the Facenet model accuracy 97,08 % achieved a frame rate of 15.35 FPS and a latency of 65.164 ms – NUC Maxtang NX6412. https://lnkd.in/dBR5ayuE #vmwareexplore #vmwarelife #techevents #vmware #vmwareexplore2023 https://lnkd.in/dMvRCgU9

Qualcomm is leading the way with on-device NPUs, which will be critical in scaling secure edge AI. Imagine the near-future of spatial computing headsets where the core AI processing and functions are all happening on device. #generatieveai #npu #edgeai

📌Weight quantization🎃 is the process of reducing the precision of the parameters (weights) in a neural network. In typical neural networks, weights are represented as floating-point numbers with relatively high precision, often 16 bits (e.g., fp16 and bf16 formats). However, this level of precision requires significant GPU memory resources. 👉Weight quantization aims to represent these weights with a smaller number of bits, such as 8-bit or even 4-bit integers. This reduction in precision can significantly reduce the memory requirements, making it feasible to deploy LLMs on a smaller number of GPUs. #ai #awq #gptq

Qualcomm enables multitude of AI use cases with its leaving NPU capabilities. Learn more from this insightful whitepaper!

NVIDIA and HP Speed Up Data Science and AI on PCs https://lnkd.in/gTXrmJkd NVIDIA and HP have partnered to accelerate data science and generative AI on workstations. Their cooperation brings together NVIDIA CUDA-X data processing libraries and HP AI workstation solutions, improving data preparation and processing for generative AI research. This collaboration heralds a new era in the field of AI-powered computing! #NVIDIA #DataScience #GenerativeAI #ArtificialIntelligence #AI #AINews #AnalyticsInsight #AnalyticsInsightMagazine

NVIDIA’s latest GPU architecture is named after David Harold Blackwell, an eminent American statistician and mathematician – who has made significant contributions to various fields, including game theory, probability theory, information theory, and statistics. NVIDIA’s Blackwell is a game-changing AI platform for trillion-parameter scale generative AI. The B200 GPU delivers 20 petaflops of power, and the GB200 offers 30x LLM inference workload performance, enabling efficiency to reach new heights. Read more here- https://lnkd.in/dEaK-GzM Jensen Huang #nvidia #gpu #nvidiagtc #llm

Learn why optical I/O will be essential to the future of generative AI #semiconductor #AI #hpc #generatieveai #photonics

Generative AI is enabling transformative outcomes, but model complexity is growing exponentially. Traditional interconnects create a bottleneck for data, forcing GPUs to remain idle a majority of the time. We need a new approach to generative AI architecture: optically connect nodes at scale so they effectively work like a single, giant GPU. Discover about how Ayar Labs is shattering the barriers to AI at scale: https://bit.ly/3sXM7cE #semiconductor #optical #photonics #AI