In the rapidly advancing field of AI, efficiently managing checkpoints during model training is crucial, and Solidigm’s QLC drives offer a solution that mitigates the risk of slow storage becoming a bottleneck. Their high-performance drives support the significant read/write operations required for frequent checkpointing, enabling data scientists to maintain efficient workflows and reduce training costs. Solidigm’s dense storage enclosures optimize data centre space while providing the necessary infrastructure for high-capacity AI datasets, proving that fast storage is the unsung hero in the race towards AI innovation. Read more in this article by Ben Young, reacting to AI Field Day.
Deploying AI Cost-Effectively at Scale With Kamiwaza
At AI Field Day, Kamiwaza introduced their open-source stack, designed to enable GenAI to scale elastically, addressing the common hurdles of infrastructure cost and operational scale faced by enterprises. With a vision to empower businesses to achieve a trillion inferences a day and ignite the 5th industrial revolution, Kamiwaza’s stack facilitates AI deployment across various environments, from cloud to edge, guaranteeing security and manageability of dispersed data. The stack’s compatibility with Intel CPUs ensures that enterprises can harness efficient AI inferencing power with minimal energy consumption, making sophisticated AI accessible and sustainable for organizations of all sizes. Read more in this Gestalt IT article by Sulagna Saha.
Compute Requirements in the AI Era With Intel’s Lisa Spelman
In this TechArena interview, Allyson Klein explores with Intel’s Lisa Spelman the evolving compute demands as enterprises gear up for the AI revolution and strive for widespread AI integration. They delve into the current state of AI adoption across industries while highlighting the critical role of software, tools, and standards in scaling AI solutions effectively. This insightful discussion underscores the thriving synergy between hardware advancements and software ecosystems necessary to power the next generation of AI applications.
At AI Field Day, Qlik Shows AI-Based Analysis Added to Its Platform
At AI Field Day, Qlik unveiled a wizard-based AI feature that simplifies the process of leveraging on-premises data for insightful analytics, integrating smoothly with Qlik’s cloud services. This enhancement to their analytics platform aims to democratize AI’s benefits, making advanced data analysis accessible to a broader range of users with varying expertise. Qlik’s initiative reflects a commitment to user-friendly, AI-powered analytics, facilitating deeper insights while streamlining the experience for its customers. Read more in this analyst note for The Futurum Group by Alastair Cooke.
Why Storage Matters for AI: Solidigm Shares POV at AI Field Day
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.
