Solidigm unveils AI Central Lab for storage and AI research

From storage to intelligence: How does Dell’s AI Data Platform bridge the gap to the Innovation Layer? Dell’s AI Data Platform delivers an open, modular, and enterprise-governed data foundation for AI initiatives, integrating disaggregated architecture, advanced search, GPU acceleration, and agentic analytics. https://buff.ly/fo6uXbV

As AI inference drives energy demand, data centers are struggling to keep up.💡 Enter RDUs: 🌱 Dramatically reduced energy consumption for AI inference workloads 🦾 Improved performance through innovative dataflow architecture and three-tiered memory design 🚀 Ability to deliver fast and efficient AI inference in power-constrained data centers Read more in our blog:  https://lnkd.in/gKHc7Q8W

SambaNova’s RDU: The Key to Sustainable AI Scaling ♻️ The AI inference boom is triggering a data center power crisis most aren’t prepared for. Consider this: ➡️ Microsoft & Google used 48 TWh in 2023 (more than 100 countries!). ➡️ AI data center power demand is projected to grow 31x by 2035 (Deloitte). ➡️ Next-gen GPUs require up to 600kW per rack + exotic liquid cooling – straining grids and water resources. The bottleneck isn't just compute – it's power, cooling, and water. Building new data centers or power plants can't solve this fast enough. SambaNova offers the efficient architecture power-constrained data centers need: The Reconfigurable Dataflow Unit (RDU). Unlike GPUs designed for training, RDUs are purpose-built for efficient inference: 🔋 Radical Energy Efficiency: The SN40L RDU’s dataflow architecture & operator fusion drastically reduce memory calls and energy use. 🧊 Inherent Cooling Optimization: Lower power draw = far less heat generated, slashing cooling energy demands. 💧 Reduced Water Footprint: Significantly lower heat output directly reduces the massive water consumption required for cooling systems (evaporative cooling, chillers). Peter Rutten, IDC VP, nailed it: "AI infrastructure is in its incandescent phase... SambaNova invented LED technology." SambaNova isn't just accelerating AI performance; it's enabling sustainable scaling by maximizing energy efficiency, optimizing cooling, and reducing water use – critical for our power-constrained future. #AI #DSPGen #Sustainability #DataCenters #EnergyEfficiency #GreenTech #SambaNova #ArtificialIntelligence #WaterConservation #Inference #GreenAI #Semiconductors #Innovation

1、AI inference is rapidly outpacing training in power demand. 2、Traditional GPU infrastructure can’t scale within power limits. 3、RDUs offer higher throughput and better energy efficiency.

NetApp teased a new architecture for AI at last year's NetApp Insight event. This year they are rolling it out as NetApp AFX. The newly announced AFX Product will use a disaggregated architecture and GPU acceleration, specifically aimed at AI and AI datasets. Being able to manage can curate data on array will be big for AI datasets. #AFX #disaggregated #storage #Netappinsight #AI #GPU

Novel AI Hardware Architectures for Graph Processing What do graphs have to do with novel hardware architectures for AI workloads? Graph processing is the key to unlocking new architectures, as much as new architectures can boost execution of graph-oriented workloads. As machine learning-powered applications are proliferating, the workloads that are created in order to serve their requirements are taking up an ever increasing piece of the compute pie. A recent IDC study found that Data Management, Application Development & Testing, and Data Analytics workloads represented more than half of all IaaS and PaaS spending already in 2018. IDC notes that this was driven in part by initial adoption of artificial intelligence (AI) and machine learning (ML) capabilities. As adoption grows, data and AI/ML workloads will dominate. This is why we see a renaissance of novel hardware architectures designed from the ground up to serve the needs of data and AI/ML workloads. More specifically for data analytics, understanding relationships among data points is a challenging but essential capability. Graph analytics has emerged as an approach by which analysts can efficiently examine the structure of the large networks and draw conclusions from the observed patterns. This is why DARPA set out to develop a graph analytics processor with the HIVE Project. Furthermore, all ML models are best expressed as graphs -- this is how ML libraries such as TensorFlow work. Efficient processing of graph-based networks involves large sparse data structures that consist of mostly zero values, and next generation architectures should avoid unnecessary processing. This panel aims to explore the interrelationship between graph processing and novel AI hardware architectures. https://lnkd.in/dyjFFfE7 -- Hosted by ZDNet's Tiernan Ray with panelists from some of the most groundbreaking AI hardware companies: Blaize, Determined AI / HPE, Graphcore, and SambaNova. Tiernan Ray. Contributing Writer, ZDNet Val G. Cook. Chief Software Architect, Blaize Carlo Luschi. Director of Research, Graphcore Raghu Prabhakar. Software Engineer, SambaNova Evan Sparks. Founder, Determined AI, an HPE Company -- Welcome to Connected Data London's #ThrowbackThursday Every Thursday at 3pm GMT, we are releasing gems from our vault on #YouTube Tune in and learn from leaders and innovators; subscribe to our channel and watch premieres as they are released! #knowledgegraph #graphdatabase #graph #AI #datascience #analytics #semtech #ontology

Across this four-part series, we have systematically covered all the core architectural pillars of the AI data center. Now, we solve the only thing that matters: The Latency Constraint. Training is 10% of the cost; Inference is 90% of the operation. The architecture shifts from maximizing throughput to achieving real-time responsiveness. In our latest deep dive, we reveal the strategic architecture of the AI product: • Efficiency over Power: Why specialized ASICs and FPGAs beat general-purpose GPUs on Cost-per-Inference. • Software Warfare: How Continuous Batching (vLLM) and advanced Quantization fight the sequential nature of LLMs. • The Last Mile: Fighting the Speed of Light with Decentralized Edge Deployment to achieve sub-10ms responses. Success is measured in milliseconds. Learn how to design for profitability and global scale: https://lnkd.in/dZSHw3sh #AIInference #EdgeAI #DataCenter #AIinfrastructure #RediMinds #CreateTheFuture

👏 AI without silos? Yes! Watch the Cisco and NVIDIA AI fireside chat to see how, together, they bring seamless AI to enterprise networks with end-to-end simplicity—from training to inferencing. 📺 Watch now 🔗👇 https://cs.co/6040Asata #CiscoDCC NVIDIA Data Center

📡🌐 #AI adoption is accelerating, and #datacenters must keep pace. Rising density, complexity and tighter tolerances are pushing engineers to rethink hardware and infrastructure strategies. 👉 Explore how innovation in fiber design is shaping scalable, future-ready AI deployments in our recent 'Voices of the Industry' #article with Data Center Frontier: https://bit.ly/4n5siXG #WeAreAFL #hyperscale #artificialintelligence

📡🌐 #AI adoption is accelerating, and #datacenters must keep pace. Rising density, complexity and tighter tolerances are pushing engineers to rethink hardware and infrastructure strategies. 👉 Explore how innovation in fiber design is shaping scalable, future-ready AI deployments in our recent 'Voices of the Industry' #article with Data Center Frontier: https://bit.ly/4n5siXG #WeAreAFL #hyperscale #artificialintelligence