Intel Xeon CPUs – How Efficiency Makes It a Top Contender for AI Inference

In the AI arms race, Intel’s latest 5th generation Xeon processors have emerged as a powerful and efficient alternative to GPUs for certain AI workloads, according to Ben Young’s AI Field Day article. With a 36% performance boost in specialized AI tasks enabled by Advanced Matrix Extensions (AMX), these CPUs accelerate deep learning particularly for AI inference, offering a cost-effective option for applications with lighter and less frequent processing needs. Intel’s dedication is clear as they bolster their CPUs’ AI capabilities and facilitate developers with tools like the OpenVINO toolkit, positioning Xeon CPUs as a feasible choice for an array of AI workloads, balancing the scale with GPUs based on the specific requirements of the task. Read more in this Gestalt IT article by Ben Young, sponsored by Intel.

Nature Fresh Farms – Optimizing Indoor Farming Practices With AI and Intel

At AI Field Day, Nature Fresh Farms, a leader in greenhouse farming in North America, revealed how the strategic use of Intel AI solutions revolutionized their farming practices towards precision agriculture. Keith Bradley highlighted the transition from traditional to high-tech farming, with on-premises Intel-based infrastructure enabling real-time data analytics for improved yield, resource efficiency, and operational control. Emphasizing sustainability and the growing importance of AI in agriculture, Bradley shared how Nature Fresh Farms harnesses predictive AI models on the farm, leveraging technology to optimize every aspect from irrigation to packaging and contributing to a marked increase in yield per square meter annually. Read more in this Gestalt IT article by Sulagna Saha.

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.

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.

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.

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.

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.

VMware by Broadcom Presents Private AI With Intel at AI Field Day 4

During AI Field Day 4, VMware stepped up to showcase how Intel AMX CPUs can be leveraged for Large Language Models (LLM) on vSphere, presenting a CPU-centric approach to AI tasks commonly handled by GPUs. As discussed by Gina Rosenthal, Earl Ruby of Broadcom (VCF) demonstrated the potential of Intel’s AMX technology in both older Ice Lake and newer Sapphire Rapids systems, achieving model fine-tuning and inference without the use of GPUs. This approach champions using CPUs for AI when feasible, reserving GPU use for scenarios demanding lower latency, and highlights the compatibility requirements for effectively implementing AI with Intel’s hardware in a vSphere environment.

Intel Modestly Lays Its Case for AI — Tech Arena

As Allyson Klein writes, Intel addressed their strategy for AI workloads at AI Field Day 4, discussing the strengths of CPUs in AI inference and the growing role of accelerators—a notably humble stance for the tech giant in the face of intensified competition in the AI silicon sector. With a focus on where CPUs excel and integrated AI acceleration technologies like Intel AMX, Intel is positioning themselves to cater to mid-market organizations looking for an accessible AI solution. Amid a rapidly evolving landscape, Intel’s efforts to balance core CPU advancements with accelerator development highlight their pursuit to maintain relevance and leadership within the AI optimization space.

Intel Day Kicks Off at AI Field Day 4

As AI Field Day 4 continues into day two, Gina Rosenthal turns her attention to the capabilities of Intel Xeon CPUs in AI, particularly in inference workflows, as presented by Ro Shah, AI Product Director at Intel. Shah delineated the growing trend towards generative AI with large models, while recognizing that enterprises are more inclined to adopt smaller language models, positioning Xeon as a suitable solution for these scenarios. Intel’s commitment to serving the AI market extends beyond hardware, showcasing their strategy of enabling AI across the board through developer support, tool extensions, and a strong partnership ecosystem.

The Year of AI at AI Field Day 4

AI Field Day returns on February 21st-23rd, giving a broad perspective on AI’s foundational technologies in a year touted to be pivotal for artificial intelligence. Attendees can expect in-depth sessions with industry giant Intel as well as key players VMware by Broadcom, Qlik, Hammerspace, Solidigm, VAST Data, and many more. This event explores revolutionary AI applications and their infrastructure demands, and will be broadcast live for a global audience. Watch live on LinkedIn and the Tech Field Day website and catch the recordings on YouTube!

Solving Environmental Sustainability Problems at the Edge With Intel’s Elastic and Energy Proportional Edge Computing Infrastructure

Sulagna Saha reviews the Intel Energy Elastic Systems. The blueprint unveils a futuristic system design that will be able to intelligently offload demanding workloads to stations with greater access to renewable energy, and help cut back dependency on non-renewable power supply. The result is eco-friendly deployments at the edge. Read about it at Gestalt IT or watch the presentation here on the website.

Network Field Day – Day 2 – Intel

In this YouTube video, Jordan Villarreal takes viewers on a journey into the world of Intel’s IPU and data center compute load balancing technology as part of Networking Field Day 33. Learn about AI networking and more by watching this reaction video recorded on-site!

AI Workload Networking With Intel

Tim Bertino discusses the challenges and requirements of networking for AI workloads, as presented by Intel Corporation at Networking Field Day. Intel highlights the difficulty in monitoring and predicting issues in AI networks, emphasizing the need for advanced anticipation and pre-emptive solutions. The article also illustrates Intel’s architectural perspective, favoring the use of Ethernet for managing these high-demand networks.