During AI Field Day, Solidigm’s Ace Stryker and Alan Bumgarner illustrated the pivotal role of SSDs in AI applications, showcasing how they cater to the high data demands of models and workflows with increased efficiency. They highlighted the superiority of SSDs over HDDs in terms of performance and Total Cost of Ownership, emphasizing the tangible benefits from greater data density to reduced physical infrastructure needs. The presentation honed in on the importance of storage in AI, linking Solidigm’s advanced SSD solutions with scalable and power-efficient AI server operations, resonating with sustainability goals and operational cost reduction.
Google Cloud, the Preferred Platform for Building Competitive AI Models
At AI Field Day, Google Cloud’s Brandon Royal showcased the giant’s comprehensive strategy for meeting today’s burgeoning AI demands, leveraging one of the industry’s most extensive digital infrastructures. Emphasizing the significance of AI infrastructure in conjunction with generative AI (GenAI), Google Cloud highlighted their commitment to innovation, asserting their platform as the superhighway for AI-forward companies. With Google Cloud providing robust compute power off its own infrastructure, businesses can harness AI’s opportunities without the traditionally high entry barriers of infrastructure costs and expertise. Read more in this article by Sulagna Saha for Gestalt IT.
Deciding When to Use Intel Xeon CPUs for AI Inference
At AI Field Day, Intel offered insights into strategic decision-making for AI inference, highlighting scenarios where Intel Xeon CPUs outshine traditional GPU solutions on both on-premises and cloud servers. By evaluating the specific requirements of AI inference workloads, Intel guides users to make informed choices that enhance value while optimizing their existing server infrastructure. This approach emphasizes efficiency and practicality in deploying AI capabilities, ensuring that organizations can navigate the complex landscape of hardware selection for their AI initiatives. Read more in this Futurum Research Analyst Note by Alastair Cooke.
Hammerspace Shows Storage Acceleration for AI Training
At AI Field Day, Hammerspace showcased its innovative storage acceleration solution, demonstrating how Hyperscale NAS can be leveraged to enhance the performance of current scale-out NAS systems, particularly in training large language models (LLM) efficiently. This storage boost not only improves speed but also optimizes resource allocation during the intensive LLM training process. Hammerspace’s advancement offers organizations the opportunity to amplify their AI training capabilities without the need to overhaul their existing storage infrastructure. Read more in this Futurum Research Analyst Note by Alastair Cooke.
VAST Data Soars With Industry Heavyweights
As Allyson Klein writes, VAST Data is revealing a major shift in AI strategy, joining forces with NVIDIA and Supermicro to bolster its role as a forward-thinking AI data platform. By embracing a novel architecture that eschews traditional x86 design for a powerful GPU-centric platform with NVIDIA DPUs, VAST Data is poised to redefine data storage, promising significant energy efficiency and enhanced performance for AI workloads. The company’s move shifts the AI training landscape towards GPU-native frameworks and sets VAST Data as a key innovator in an infrastructure industry ripe for disruption.
Does Storage Matter in AI Inferencing? What About the SSD?
Keith Townsend reacts to Solidigm’s presentation at AI Field Day, considering the role of storage systems in AI inferencing and the impact of SSD selection on AI system design. This video underscores the significance of considering storage performance and reliability when devising robust AI inferencing architectures. Solidigm’s discussion reflected a deeper industry focus on the intricate relationship between storage solutions and AI capabilities, suggesting that the choice of SSDs could be a pivotal factor in optimizing AI inferencing operations.
VMware Private AI at AI Field Day
VMware’s presentation with Intel at AI Field Day centered on optimizing on-premises AI workloads, highlighting the capability of Intel Sapphire Rapids CPUs with Advanced Matrix Extensions (AMX) to efficiently perform large language model (LLM) AI inference, traditionally a task for GPUs. Demonstrating that AI can be resource-effective on CPUs, the discussion covered the technical prerequisites for harnessing AMX in vSphere environments and the ongoing integration of these accelerators into popular AI frameworks. With CPUs increasingly capable of handling AI tasks through built-in matrix math acceleration, VMware showcases a sustainable, cost-effective approach, potentially reshaping the hardware strategies for mixed workload servers. Read more in this analyst note for The Futurum Group by Alastair Cooke.
Gemma and Building Your Own LLM AI
At AI Field Day 4, Intel invited the Google Cloud AI team to showcase their Gemma large language model (LLM), revealing insights into the advanced infrastructure used for building such models on Google Cloud. The presentation underlined Gemma’s efficiency with fewer parameters for inference, highlighting Google Cloud’s strength in analytics and AI, particularly in managing differing resource needs between model training and application inference phases. Google Cloud’s integration of AI in products was illustrated with Google Duet, an AI-based assistant that aids in software development, exemplifying the potential future where AI handles more coding tasks, freeing up developers for high-level problem-solving and design. Read more in this analyst note for The Futurum Group by Alastair Cooke.
Intel Xeon CPUs on VMware vSphere – A Powerful and Cost-Effective Twosome for AI/ML Workloads
With AI ingrained in our daily routines, Forward Networks delivered a strategic approach at Networking Field Day, demonstrating how even complex networking data can be made manageable through AI integration. Their platform uses a data-first principle, enabling AI to interact effectively with a digital twin of network infrastructure, simplifying tasks for network engineers. The innovative AI Assistant within Forward Networks’ ecosystem assists in constructing queries for the Network Query Engine, fostering trust through verifiable, human-readable outputs, and providing a gateway for more intuitive network management. Read more in this article by Sulagna Saha on Gestalt IT.
Defeating Data Gravity? – Hammerspace
According to Keith Townsend, Hammerspace presented a compelling argument for a shift in overcoming data gravity by moving data closer to accelerated computing resources at AI Field Day. Their solution, a parallel file system, acts as a bridge between dispersed data sources, offering a unified metadata view that streamlines data preparation for AI tasks. While Hammerspace’s technology appears to enhance user experience, it also requires strategic GPU placement and considerations around data governance and movement across geopolitical boundaries.
Taking on AI Inferencing With 5th Gen Intel Xeon Scalable Processors
Intel’s 5th Generation Xeon Scalable Processor, known as Emerald Rapids, offers an advantageous solution for AI inferencing, providing a compelling alternative to GPUs in certain applications. Highlighted during the AI Field Day event, Intel showcased the processor’s suitability for general-purpose AI workloads, especially for private AI deployments requiring lower latency and mixed workloads. In his presentation, Ro Shah illustrated that Xeon CPUs are well-equipped to handle AI models with fewer than 20 billion parameters, making them a cost-effective and efficient choice for many enterprises. Read more in this article from Gestalt IT.
Insights From the AI Field Day: A Futurum Group Overview
In this LinkedIn Pulse article, Paul Nashawaty of The Futurum Group summarizes all of the AI Field Day presentations, highlighting VMware’s deep dive into Private AI in collaboration with industry giants like NVIDIA and IBM, and Intel’s focus on deploying AI inference models with Xeon CPUs across diverse environments. Next-generation AI-infused storage solutions from Solidigm and SuperMicro underscored the critical role of optimized storage in AI, while Vast Data focused on addressing the growing data demands of AI and HPC workloads. Google Cloud’s session on AI platforms and infrastructures showcased innovative approaches with Kubernetes at the core, paving the way for accessible and powerful AI development and deployment.
From Server Farm to Table: How Nature Fresh Uses AI and CPUs to Improve Crop Yields
At AI Field Day 4, Keith Bradley from Nature Fresh Farms highlighted the practical application of AI in agriculture, revealing how the company utilizes AI models and data from IoT devices to boost crop yields significantly. As Jim Davis writes, Nature Fresh Farms’ unique approach relies heavily on edge computing with Intel Xeon CPUs for AI inferencing, debunking common misconceptions that AI always requires cloud connectivity or GPU resources. Bradley’s presentation emphasized the critical importance of IT system reliability in the agricultural sector, where even brief periods of downtime can result in substantial losses.
Qlik Presents About AI on AIFD4
At AI Field Day, Qlik unveiled their comprehensive AI strategy, focusing on enabling customers to tackle the complexity and scale of AI integration into their operations with solutions like predictive analytics, AutoML, and their 170K-strong model portfolio. With initiatives like Qlik Staige, the company facilitates the use of AI for those new to the field, using established algorithms and bringing structured and unstructured data together for added value. Read more about the Qlik presentation in this LinkedIn Pulse article by Gina Rosenthal!
VAST Data Upends Storage in the AI Era
Allyson Klein offers an insightful look at VAST Data’s innovative approach to the AI data pipeline, a process crucial for preparing data for AI training, through their advanced NAS solution spotlighted at AI Field Day. VAST Data’s platform addresses the challenges organizations face with data prep and movement in AI, proven by their success in the HPC sphere and their natural progression into supporting AI training clusters.
Google Cloud Talks AI Trends at AIFD4
During AI Field Day 4, Google Cloud positioned AI as the next major platform shift, emphasizing its role following the internet and mobile, and highlighted their impressive data store capabilities including every search query since the inception of their browser. Google Cloud’s adoption of generative AI and the introduction of Google Gemini and DeepMind’s open model Gemma signal a strong continuation of their open-source ethos, enhancing AI scalability and accessibility. As noted by Allyson Klein in this article, the presentation also underscored Google’s commitment to supporting a diverse AI ecosystem, showcasing extensive compatibility across major silicon platforms, even as the event focused on Intel’s technology.
VAST Data Operationalizing AI
At AI Field Day 4, Keith Townsend engaged with VAST Data’s John Mao and Neeloy Bhattacharyya to discuss the company’s innovative architecture that separates the persistent data layer from stateless logic, optimizing access patterns and improving data preparation efficiency for AI applications. Gina Rosenthal highlights their global namespace approach and the unique “VAST DataBase” system, they are streamlining data availability and scalability across the pipeline.
Google Cloud at Intel Day @ #AIFD4
At AI Field Day, Google Cloud’s Brandon Royal presented insights into AI platforms and infrastructure, emphasizing the shift from the internet to mobile and AI. He introduced Gemini, Google’s multi-modal model, and unveiled Gemma, a family of lightweight models. Royal discussed safety measures and highlighted GKE’s role in AI, covering CPUs, GPUs, and TPUs. A demo showcased Gemma on GKE supporting 8K tokens. Ameer Abbas, Senior Product Manager at Google Cloud, focused on Developer and Operational Productivity with Duet AI, demonstrating code development and transformation capabilities for enterprise builders and developers. Read more in this LinkedIn Pulse article by Gina Rosenthal.
The Bedrock of AI Is Data With Nick Magnuson and Clive Bearman of Qlik
The latest Utilizing Tech podcast episode dives into the critical role of enterprise data integration for the success of generative AI, featuring insights from Qlik’s Nick Magnuson and Clive Bearman prior to their presentation at AI Field Day 4. They discuss the challenges enterprises face, such as preparing quality data for AI models and using machine learning to enhance data organization and tagging. The conversation also explores the potential of large language models to democratize data querying, underscoring the importance of curated data in the AI-driven future.
