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The computational requirements of ML based algorithms can be great, which has limited the practical use cases of such algorithms on small edge compute to the smaller, simple models. However, by introducing customization to RISC-V processors, a greater range of algorithms can be facilitated at the edge. Check out this demo to see custom instructions in action.

The Cost of Building A SOTA Foundation Model Is Trending Down Dramatically! With $15M you can hire 10 engineers, get enough GPUs and train a Sonnet 3.5 class model. We will see dozens of these models being built over time. Multi-modal LLMs will be ubiquitous and plentiful ~ Bindu Reddy CEO of Abacus. AI

Visit Bredec.com today Bredec Group Bredec Group Ask HN: Why can't Nvidia compete in the inference market?: Hey HN! Lately I have been thinking about how the inference market will involve. We saw that llama 3 with groq was showing great results. Once question I had was why can't nvidia compete in the inference market? In the video below chamath was saying how nvida won't be able to compete in inference. https://lnkd.in/det-VTVg is here just chilling his bag or are there some truth to this? What are fundamental reason that this would be the case? --- Comments URL: https://lnkd.in/d9atTibE Points: 1 # Comments: 0 info@bredec.com Inquiry@bredec.com

TechSnack: Machine Learning – More GPU. More Edge. Discover a new edition of our exciting TechSnacks! In this session, we're diving deep into the dynamic realm of machine learning, where innovation knows no bounds. Get ready to explore the cutting-edge fusion of "More GPU" and "More Edge", a convergence that's reshaping the landscape of artificial intelligence. Watch our latest TechSnack video with Volker Gimple, Group Manager of Software Development & Research. https://lnkd.in/eM4bETYU #leadingvision #machinevision #embeddedsystems

Facts by the numbers: ·      6 containers that make up the NVIDIA AI Enterprise long term supported branches ·      4,471 OSS, 3rd party & NVIDIA packages ·      9,256 dependencies across the containers ·      36 months duration for each branch ·      1 gigantic task to maintain, patch and support the branch ·      100s of enterprises betting their business on AI with NVIDIA AI Enterprise The image below is a visualization of all packages & their dependencies across the Triton Inference Server container.

With 2x the peak performance for FP16 and BF16 data types compared to the previous A100 GPU, along with new FP8 data format offering 4x the compute throughput of FP16, the H100 is a game-changer. It also introduces a Transformer Engine for optimised hardware and software, delivering up to 9x higher performance on AI training and 30x faster inference workloads. With HBM3 memory and support for up to seven Multi-Instance GPU (MIG) instances, it's a powerhouse for AI tasks. Stay ahead in the tech game with Nort Labs! #NVIDIAGPU #AI #TechInnovation #NortLabs Let us know your thoughts in the comments!

We just released the TensorRT Model Optimizer library (previously under the codename AMMO) and a collection of optimization examples for GenAI and fully compatible with #TensorRT/ #TensorRT_LLM . More techniques are coming for #hopper and #blackwell ! Di Wu Erin HoChenjie LuoLucas LiebenweinChenhan YuZhiyu (Edward) ChengAsma Beevi K TKai XuKeval MorabiaJingyu X.Wei-Ming ChenRiyad IslamAjinkya Rasane

Network, could you please suggest a tool for model explainability similar to WIT in TensorBoard? We would like to use WIT, but due to the way our model is built and how we pass input vectors, we are unable to adjust it to our needs. We have also tried the Notebook widget version, but we encountered difficulties in using it, and there is limited information available on how to resolve these issues. Thank you in advance. #tensorflow #tensorboard #witwidget #explainableai"

Easy to use and fast inference for everyone.

What features do prioritize in a modern laptop for your hybrid workforce? ✅Best-in-class CPU performance ✅ On-device AI ✅ Long battery life ✅ All of the above Built for AI, Snapdragon X Elite delivers all and more. Watch this video for a quick intro.