GIGABYTE’s Post

Introducing DeGirum AI Hub – the easiest way to fast-track your edge AI development. Start accelerating your AI projects today at hub.degirum.com!

🚀 Introducing the DeGirum AI Hub – your go-to solution for simplifying and accelerating edge AI development.   With AI Hub, you can prototype and test AI models without needing hardware, explore a vast library of pre-trained models, and evaluate them across multiple hardware options. Key features include:   👉 Evaluate models directly in your browser – no hardware setup required. 👉 Access a model zoo of 1000+ AI models for applications like hand and face detection, object recognition, and more. 👉 Test models on preconfigured hardware like DeGirum Orca, Google Edge TPU, Intel CPU/GPU/NPU, Rockchip, and more. 👉 Leverage our hardware-agnostic PySDK for maximum flexibility across platforms.   Formerly known as DeGirum Cloud Platform, the AI Hub builds on the same foundation with even more features to empower your AI projects.   Discover how AI Hub can accelerate your development journey at hub.degirum.com.   #AIHub #EdgeAI #AIPrototyping #PySDK #AIInnovation

🔥 AI computing demand has reached historic highs, yet many GPU suppliers still face underutilization. Recent reports show that 📊 50-70% of GPU infrastructure remains idle, despite the surging need for AI power. At #Axlflops, we’re tackling this with 100% utilization through our decentralized network, empowering suppliers to maximize revenue 💰 while accelerating AI innovation 🤖. 🚀 Ready to unlock the full potential of your GPUs? Join the future of AI computing with Axlflops. 🔗https://axlflops.ai #AI #GPU #Decentralization #AIcomputing #MachineLearning

🚨 Exciting read alert 🚨 In a new contributed article on insideHPC, NextSilicon founder and CEO Elad Raz dives deep into the limitations of traditional GPUs and the pressing need for new, more flexible alternatives. It's time to move beyond the pain of porting and embrace a future of innovative new architectures. More news on this coming soon!💡 #HPC #AI #Innovation #NextSilicon

Cool article about the challenges of HPC!

Exciting strides in #AI infrastructure! #OpenAI is working with Broadcom to develop its custom AI chip, projected for release in 2026. Designed to optimize efficiency and reduce costs, this chip aims to scale operations while lowering dependence on external GPUs. It will bring faster processing, more predictable latency, and better control over AI workloads, reshaping the future of affordable, scalable AI services for developers worldwide. With this development, OpenAI’s goal is to meet growing demand while enhancing performance and delivering more reliable AI solutions. The custom chip’s efficiency gains could drive broader accessibility to advanced AI capabilities. #customaichip #aiinfo

🚨 This contributed article in insideHPC is a great read! 🗞️ NextSilicon founder and CEO Elad Raz dives deep into the limitations of traditional GPUs and the need for flexible alternatives. Look out for more exciting news to come very soon!💡 #client #nextsilicom #innovation #AI #GPUs

A new Kid(of father of all kids) in the market which bets for 500,000 tokens per second. Meet Sohu, the fastest AI chip ever: 500,000 tokens/sec with Llama 70B. One 8xSohu server equals 160 H100s, revolutionizing AI product development. Sohu’s specialization in transformer models delivers >10x the speed and cost-efficiency of NVIDIA’s next-gen GPUs, setting a new standard in AI https://lnkd.in/ge5rCUP7 #AI #genAI #Chips

"Intel Xeon - Processor designed for AI”. Yes, that's true - a General-purpose compute which can handle almost all your AI workloads. With all the GenAI boom in last few years, there is a very huge surge in the demand for specialized compute or accelerates which do AI training very well. But AI model training is only one (and a very small) part of overall AI cycle. There is data preparation, training, and deployment (or inferencing). Most of that work is done on Xeon processors today (e.g. classical ML, Predictive Analytics etc.) and with advancements (such as AMX) in latest generation of Xeon processors, training of certain size models can be done on Xeons too. You can get GPU performance at CPU price, so before you start spending a lot of money on proprietary hardware which you may not use all the time, check what a general-purpose compute can do for your AI workloads. Here are some more facts: https://lnkd.in/dqgfHfyr Report from IDC : https://lnkd.in/dX26YZvx Had the opportunity to talk on this topic, in detail, to our customer and partners at recent AI Summit, in Singapore. #IamIntel #Xeon

Attending the NVIDIA AI Summit in DC and discussing our co-developed AI solutions and integrations to accelerate generative AI adoption was great. AI is revolutionizing industries by boosting efficiency and delivering faster insights, but it also demands advanced compute infrastructure and observability. OpsRamp now supports full-stack AI workload-to-infrastructure observability, including NVIDIA GPUs, AI clusters, DGX Systems, Mellanox InfiniBand, and Spectrum switches. IT teams can monitor AI infrastructure performance, health, and power consumption alongside their entire data center, all visualized in a unified service map. OpsRamp's new operations copilot leverages NVIDIA’s accelerated computing platform to analyze large datasets, improving productivity, while our integration with CrowdStrike provides real-time security insights. #NVIDIA #AIOps #OpsRamp #GenerativeAI #DigitalTransformation #HybridCloud #AI