Videos

Event: Cloud Field Day 25

Appearance: The Internet Archive Presents at Cloud Field Day 25

Company: Internet Archive

Video Links:

• Vimeo: Knowledge and culture retained for all by the Internet Archive
• YouTube: Knowledge and culture retained for all by the Internet Archive

Personnel: Joy Chesbrough

Internet Archive is a non-profit library of millions of free texts, movies, software, music, websites, and more. Joy Chesbrough introduces us to the Internet Archive’s mission and accomplishments before examining how this public-good service is funded and operated. Joy, who leads the organization’s philanthropy efforts, explained that the Internet Archive was founded by technologist Brewster Kahle nearly 30 years ago as a non-profit to ensure knowledge remained open, free, and accessible to everyone, using an open-source platform. As a global public service, it is one of the world’s most frequently visited websites, attracting 2.2 million daily users who access a vast array of content from books and magazines to historical tech manuals.

A cornerstone of the Internet Archive’s work is the Wayback Machine, lauded as a “time machine for the web” that prevents digital content from disappearing. This tool has been critical for journalists, capturing government websites during presidential transitions (e.g., end-of-term crawls), and preserving cultural heritage during crises, such as the Ukraine war, and digitizing Aruba’s culture. Beyond the Wayback Machine, the Internet Archive’s mission is to provide universal access to all knowledge, much like a modern Library of Alexandria. It houses an astounding 250 petabytes of data, 113 million public media items, and over one trillion web pages, making it ten times larger than the U.S. Library of Congress. Other vital projects include “Archive-It” for institutional digital preservation, “Democracy’s Library” for archiving government documents globally, “Community Webs” to ensure marginalized voices are historically recorded, and “Open Library,” which provides millions of accessible books, working to overcome the statistic that only 7% of published works are in accessible formats. They also combat website “link rot” through partnerships with platforms like Wikipedia and WordPress, ensuring enduring access to linked content.

The Internet Archive operates as a purpose-driven, independent non-profit, committed to privacy by not tracking users, displaying ads, or monetizing its content. Its $30 million annual operating budget, while a small fraction of the U.S. nonprofit sector’s over $592 billion, is used efficiently without extensive marketing, as its brand recognition often stems from the Wayback Machine. Joy’s philanthropy team has significantly expanded its donor base, attracting nearly 250,000 unique individual donors over the past year. Supporters are deeply loyal, with an average donation higher than most nonprofits, reflecting the perceived value of the Internet Archive in providing a stable foundation for truth and combating misinformation and vanishing culture in an increasingly digital and volatile world. The organization is dedicated to ensuring this invaluable library endures for future generations, preserving the world’s culture and history into perpetuity.

Event: Cloud Field Day 25

Appearance: VMware by Broadcom Presents at Cloud Field Day 25

Company: VMware by Broadcom

Video Links:

• Vimeo: Why VCF Networking NSX Is Essential Even in a VXLAN World with VMware by Broadcom
• YouTube: Why VCF Networking NSX Is Essential Even in a VXLAN World with VMware by Broadcom

Personnel: Dimitri Desmidt

Physical fabrics may provide VXLAN, but modern private clouds demand far more than basic overlay connectivity. This video explores how VCF Networking (NSX) decouples networking from the physical fabric, enabling automated, policy-driven network services that integrate natively with vCenter and VCF Automation. We also examine Virtual Private Clouds (VPCs), which empower developers to instantly provision secure, multi-tenant environments without deep networking expertise. Discover why VCF Networking is not simply an overlay but the foundational layer that unlocks agility, operational simplicity, and true cloud operating models inside the modern data center. Dimitri Desmidt shows why network virtualization within VMware Cloud Foundation (VCF) is essential, even if the underlying physical network already supports VXLAN. He highlights that while physical networks provide basic overlay connectivity, they fall short in delivering the comprehensive network services – such as switching, routing, load balancing, and firewalling – that modern applications require. Managing these services manually on physical infrastructure for each new application often entails a cumbersome, ticket-driven process spanning multiple teams and interfaces, delaying application deployment by weeks or even months.

VCF Networking, powered by NSX, addresses this by bringing these crucial network services directly into the cloud platform, enabling a self-service, automated consumption model. This shift eliminates the need for manual configuration and inter-team coordination, drastically reducing network provisioning time from weeks to mere seconds. A key innovation in VCF 9.0 is the introduction of Virtual Private Clouds (VPCs), which adopt the familiar industry-standard concept. A VPC is a self-contained “network bubble” that developers or vCenter administrators can instantly provision with subnets and automated IP address management. VCF is pre-configured with an IP block designated for future application networks, ensuring that newly provisioned subnets do not conflict with or overlap existing physical network infrastructure, thereby preventing IP conflicts and maintaining network stability.

VPCs offer granular control over network access, allowing for “public” subnets exposed to the external world, “private transit gateway” subnets for communication within a tenant, and “private VPC” subnets for isolation within a single VPC bubble. While VCF Networking handles basic access control and Network Address Translation (NAT), more advanced security needs, such as protocol-level firewalling, IDS/IPS, and malware inspection, are addressed by vDefense. The VPC gateway is fully distributed, running as a process within each ESX host, making the creation of new subnets completely transparent to the underlying physical fabric. This design means the physical network only sees encapsulated traffic between ESX host IPs, so no changes are required to the physical switches. This approach not only provides exceptional flexibility for dynamically connecting virtual machines but also allows for overlapping private IP address spaces across different VPCs, as all outbound traffic is automatically NAT’d, preventing conflicts. Additionally, VCF enables administrators to set quotas for network resources, ensuring fair usage and resource governance across various tenants or business units.

Event: Cloud Field Day 25

Appearance: The Rise and Fall of the Cloud – Again

Company: Tech Field Day

Video Links:

• Vimeo: The Rise and Fall of the Cloud – Again with Tom Lyon
• YouTube: The Rise and Fall of the Cloud – Again with Tom Lyon

Personnel: Tom Lyon

Tom Lyon begins by suggesting that if cloud computing is defined as outsourcing data processing to a company that owns the equipment, then the concept is nearly a hundred years old. He traces its origins to the 1930s, when IBM established service bureaus where clients could bring data to be processed using punch cards and tabulating machines, an expensive service akin to modern cloud offerings. This early period, marked by the Great Depression, saw basic arithmetic being outsourced, with computing often done by “human computers” before the widespread adoption of machines. The post-World War II era saw advanced punch card computations and a 1956 IBM consent decree that necessitated the creation of the Service Bureau Corporation, highlighting the significance of outsourced data processing even then.

The evolution continued into the 1960s with the proliferation of service bureaus, the birth of timesharing, and the emergence of software as a distinct business. The late 60s witnessed “go-go years” with the concept of a “computer utility” – a direct precursor to modern cloud computing – fueled by remote access, modems, and hard drives, leading to “irrational exuberance” and a subsequent “major depression” in the early 70s. This bust was exacerbated by a shift from services to software and the rise of the mini-computer. The late 70s and 80s brought networking innovations and the desktop era, with the “network is the computer” philosophy solidifying the idea of distributed computing, though general computing wasn’t yet fully within the network “cloud”. The late 90s dot-com boom saw the rise of ISPs and early Infrastructure as a Service (IaaS) providers like Loudcloud and TerraSpring, again characterized by “irrational exuberance” and ambitious data center plans.

However, this boom also led to a significant bust in the early 2000s, which Lyon attributes more to “telecom fraud” than just dot-com speculation. AWS launched in 2006, offering basic cloud services, just before the real estate crash. The 2010s saw AI “get real” with breakthroughs like Watson and AlexNet, propelled by GPU processing and big data. Today, in the 2020s, AI is experiencing “total irrational exuberance,” with an “insane” build-out of data centers, NVIDIA’s dominance, and concerns about creative accounting and fraud. Lyon warns of an impending “AI recession” driven by unsustainable growth expectations, massive infrastructure challenges (especially in energy and water), data sovereignty concerns, and copyright issues. While acknowledging the underlying value of AI, he suggests a period of “normalcy” is five to ten years away, similar to how previous busts eventually paved the way for future growth by leaving behind overbuilt but eventually useful infrastructure.

Event: Cloud Field Day 25

Appearance: VMware by Broadcom Presents at Cloud Field Day 25

Company: VMware by Broadcom

Video Links:

• Vimeo: Database as a Service (DBaaS) with VMware Data Services Manager from VMware by Broadcom
• YouTube: Database as a Service (DBaaS) with VMware Data Services Manager from VMware by Broadcom

Personnel: Eric Gray

Open-source databases like PostgreSQL and MySQL are in high demand, but provisioning them often creates bottlenecks for vSphere admins and DBA teams. Ticket queues grow, governance slips, and “shadow IT” introduces risk. In this video, we show how VMware Data Services Manager (DSM) enables on-demand Database-as-a-Service (DBaaS) on VMware Cloud Foundation. Learn how infrastructure policies and RBAC deliver secure, self-service database deployment while maintaining visibility and control. We also highlight how DSM automates HA deployments, read replicas, backups, and point-in-time recovery, eliminating database sprawl and simplifying Day 2 operations. This addresses the common challenges organizations face with database sprawl, lack of governance, configuration drift, and ticketing bottlenecks when developers arbitrarily spin up VMs with databases without proper oversight.

VMware Data Services Manager (DSM) integrates as an appliance and vCenter plugin within an existing VMware Cloud Foundation (VCF) environment, leveraging management and workload domains. As a vSphere administrator, you retain control over the infrastructure, defining compute resources (clusters, resource pools, supervisor namespaces), storage policies (vSAN, NFS), and networking (VLANs, VPC subnets). DSM handles IP address assignment and allows administrators to define VM classes (e.g., small, medium, large) to provide granular control over resource allocation. Supported databases currently include PostgreSQL, MySQL, and Microsoft SQL Server in tech preview, with the system designed using cloud-native Kubernetes technologies.

The administrative setup involves configuring S3-compatible backup targets (on-prem or cloud), enabling specific database versions, creating DSM namespaces to group resources, and linking directory groups (such as “developers”) to these namespaces with appropriate DSM user roles. Data service policies tie together specific database engines, namespaces, allowed versions, infrastructure policies, and backup locations, providing robust guardrails for self-service. For developers, this translates to a streamlined experience where they can easily provision single or clustered database instances, perform version upgrades, enable read replicas for scaling, and manage backups, all through a simplified UI or API, receiving a ready-to-use connection string for their applications. DSM also offers basic monitoring and integrates with VCF operations or Prometheus for more comprehensive metric collection, ensuring health and resource management while providing flexible point-in-time recovery options.

Event: Cloud Field Day 25

Appearance: VMware by Broadcom Presents at Cloud Field Day 25

Company: VMware by Broadcom

Video Links:

• Vimeo: The DRAM Barrier – Why VMware Advanced Memory Tiering is a Data Center Game Changer with VMware
• YouTube: The DRAM Barrier – Why VMware Advanced Memory Tiering is a Data Center Game Changer with VMware

Personnel: Dave Morera

Memory is often the most expensive and restrictive bottleneck in modern datacenters. VMware Memory Tiering (an industry exclusive) solves this by automating data placement across high-performance and cost-optimized memory tiers. This session explores how this unique hypervisor integration drives 40%+ TCO savings, improves VM density, and ensures smarter resource consumption. Learn why VMware is the sole leader in transforming memory from a hardware constraint into a strategic advantage. This innovative feature, “VMware Advanced Memory Tiering with NVMe,” addresses the rapidly escalating cost of DRAM, which now accounts for up to 96% of a server’s bill of materials. Presented as a core component of vSphere, and thus included in VMware Cloud Foundation (VCF) and VMware vSphere Foundation (VVF), this technology aims to overcome the “DRAM barrier” by intelligently managing memory resources.

The core of VMware Memory Tiering involves using less expensive NVMe devices as a secondary memory tier, with DRAM remaining the primary, high-performance tier (Tier 0). From a VM’s perspective, this combination appears as a single, logical memory space, making the underlying tiering transparent. VMware employs a proprietary algorithm that constantly monitors memory page activity, classifying pages as hot, warm, or cold based on recent access patterns. When DRAM utilization reaches a configurable threshold (e.g., 70-75% pressure), cold, inactive pages are proactively moved to the NVMe tier, freeing up DRAM for active workloads. This intelligent, proactive approach differs from reactive measures like swapping or ballooning, enabling customers to achieve over 40% reduction in total cost of ownership by purchasing less physical DRAM, and doubling VM density on existing hardware due to more efficient CPU and memory utilization. The NVMe devices must be directly connected, dedicated solely for this purpose, and meet specific endurance and performance requirements, with hardware RAID support for data mirroring and redundancy.

For operational flexibility, VMware Memory Tiering offers configurable ratios between DRAM and NVMe, starting with a default 1:1 ratio (providing 100% more memory capacity) and scalable up to 1:4 (a 4X increase), with a maximum partition size of 4TB. This allows administrators to adjust capacity based on workload needs without physical hardware changes. The feature seamlessly integrates with existing vSphere functionalities like HA, DRS, and vMotion, as well as various encryption methods (host, VM, vSAN), with vMotion being “tier-aware” to handle VM migrations between hosts with and without memory tiering. However, certain specialized VMs, such as latency-sensitive applications, monster VMs, and security-hardened VMs (e.g., those using TDX or SEV for memory encryption), are not supported as the hypervisor cannot classify their encrypted memory pages. VMware provides extensive documentation, including performance whitepapers, deployment guides, and Hands-on Labs, to aid in understanding and implementing this transformative technology.

Event: Cloud Field Day 25

Appearance: Hammerspace Presents at Cloud Field Day 25

Company: Hammerspace

Video Links:

• Vimeo: Accelerate cloud and AI workloads with the Hammerspace Data Platform
• YouTube: Accelerate cloud and AI workloads with the Hammerspace Data Platform

Personnel: Dan Reger

Hammerspace is a data platform for unstructured data that helps customers unify all their data storage and accelerate their workloads, including AI, to deliver results faster – both in the cloud and in their own data centers. This session will introduce Hammerspace and how it helps cloud customers maximize performance, avoid wholesale data migration, and reduce cloud storage costs. Dan Reger, Senior Product Marketing Director at Hammerspace, focused on accelerating cloud and AI workloads using the platform, particularly highlighting its benefits for cloud and hybrid environments. He noted that migrating workloads to the cloud is often complex, especially when data is distributed across multiple regions or subject to regulatory requirements, and that traditional cloud storage isn’t always optimized for modern high-performance demands.

Hammerspace tackles these challenges by providing a unified global file system namespace that spans across on-premises storage, various cloud storage services (block, file, object), and even different cloud regions. This agentless solution allows customers to simplify and speed cloud migrations, accessing data everywhere without wholesale data movement. The platform dynamically orchestrates data, moving only the necessary subsets to the fastest available storage tiers (e.g., local NVMe on bare-metal GPU servers) to maximize workload performance and compute utilization. This objective-based policy engine ensures data is always where it’s needed, preventing bottlenecks and eliminating unnecessary data transfers.

The platform is designed to accelerate AI, HPC, and workloads involving large volumes of unstructured data across diverse environments. Hammerspace’s capabilities, including parallel NFS and intelligent data orchestration, ensure optimal data performance and efficient use of cloud compute resources. This approach also addresses concerns such as rising cloud storage costs and data sovereignty, with Hammerspace approved for deployment in OCI’s dedicated regions. Real-world examples, such as Meta and other unnamed “household name” customers, illustrate successful large-scale deployments involving thousands of servers, tens of thousands of GPUs, and petabytes of data, demonstrating Hammerspace’s ability to seamlessly integrate and enhance existing IT processes without requiring significant changes.

Event: AI Infrastructure Field Day 4

Appearance: Delegate Roundtable at AI Infrastructure Field Day

Company: Tech Field Day

Video Links:

• Vimeo: AI is Driving all Infrastructure Change – Delegate Roundtable at AI Infrastructure Field Day 4
• YouTube: AI is Driving all Infrastructure Change – Delegate Roundtable at AI Infrastructure Field Day 4

Personnel: Alastair Cooke

This roundtable discussion explores how the reality of artificial intelligence is driving profound shifts in infrastructure, moving beyond mere marketing labels to necessitate new, distinct approaches. Participants noted this transformative power in vendor presentations, citing Exite Labs’ massively scalable ARM architectures embedded within network interfaces and Vast’s innovative use of Bluefield DPUs, both driven by the evolving demands of AI. The conversation highlighted AI’s role as a significant driver of innovation in networking, pushing traditional Ethernet to the forefront over InfiniBand for HPC, and accelerating the development of smarter NICs and HBAs to support AI workflows.

A significant shift observed was the increasing emphasis on AI inferencing over training. This pivot indicates the practical application of AI in real-world scenarios, with enterprises actively deploying AI solutions. However, delegates recognized that building inference is not the final stage; it requires sophisticated application delivery and load balancing that, while familiar in concept, now demands context switching based on specific AI prompts or models. Parallels were drawn to historical architectural migrations, suggesting that AI is reaching a maturity where it’s integrated into applications for mainstream business value, moving away from being a “solution in search of a problem.” This evolution also sees a mix of large language models for general tasks and specialized, smaller language models (SLMs) for specific business applications, as exemplified by Forward Networks’ approach to distribute intelligence.

The discussion also touched on the critical role of human oversight and trust in AI systems, particularly in regulated environments, likening it to the gradual adoption of automation seen in systems such as VMware vSphere’s Dynamic Resource Scheduler. While AI is undeniably accelerating the scale and speed of innovation in networking and storage, some elements resonate with “everything old is new again,” as past concepts like offload engines and advanced storage architectures are being repurposed at an unprecedented scale. There was a debate on whether AI *drives* innovation or simply provides a compelling use case for existing “cool tech” that previously lacked widespread application. Looking ahead, AI is poised to become the “killer app for the edge,” driven by the high cost and time required to move large datasets, pushing processing closer to data generation. This necessitates new infrastructure designs for smaller, distributed AI clusters, creating opportunities for greenfield builds and challenging architects to bridge the gap between massive data center deployments and efficient, localized AI.

Event: AI Infrastructure Field Day 4

Appearance: Forward Networks Presents at AI Infrastructure Field Day

Company: Forward Networks

Video Links:

• Vimeo: Forward AI – Security Vulnerability Management with Forward Networks
• YouTube: Forward AI – Security Vulnerability Management with Forward Networks

Personnel: Nikhil Handigol

The presentation highlighted a security vulnerability management use case that demonstrated a unique way to access Forward AI via Slack. In a common scenario, a CISO asked via Slack which devices were affected by a specific CVE. Forward AI, acting as an agent within the Slack channel, was prompted to investigate. It gathered vulnerability details and responded directly in Slack, identifying affected devices and providing a link to further evidence and details within the Forward Networks platform. The speaker addressed security concerns about Slack integration, emphasizing that specific integrations and channel restrictions are in place to ensure secure communication.

Beyond this demonstration, Forward AI aims to lower the barrier to network understanding by enabling users to ask questions in plain English rather than requiring them to learn complex, network-specific languages. It supercharges efficiency through an agentic architecture that can plan and execute dynamic, multi-step workflows, coordinating actions across multiple systems like ServiceNow and Slack. This capability instantly up-levels teams, enabling non-experts to solve complex network problems using state-of-the-art AI. The foundation of Forward AI’s effectiveness lies in combining the broad general capabilities of modern large language models with the deep, specific knowledge derived from Forward Networks’ mathematically accurate digital twin, which overcomes the challenge of applying AI directly to overwhelmingly complex raw network data.

Looking ahead, Forward AI, built on this robust digital twin, is designed to evolve into an agency system that can interact with other external systems via a general mechanism called MCP, fostering a thriving ecosystem of interacting agents. The core philosophy underpinning these agentic operations is trust, especially given the critical nature of network infrastructure. While striving for speed and efficiency, the current approach for Forward AI is to guide operators and provide deep insights, avoiding direct network changes to ensure safety and prevent unintended disruptions. The digital twin remains the essential foundation for enabling these trusted agentic operations, delivering measurable ROI.

Event: AI Infrastructure Field Day 4

Appearance: Forward Networks Presents at AI Infrastructure Field Day

Company: Forward Networks

Video Links:

• Vimeo: Forward AI – Config Audit and Compliance with Forward Networks
• YouTube: Forward AI – Config Audit and Compliance with Forward Networks

Personnel: Nikhil Handigol

Forward AI aims to revolutionize network configuration audit and compliance, particularly for organizations in regulated industries that dread annual audits. These audits are typically manual, time-consuming, and error-prone, and carry a significant risk of penalties. The traditional approach involves painstakingly listing all devices, understanding vendor-specific configuration syntaxes for each operating system, extracting data, correlating it with standards, and generating reports – a task that can span days and requires specialized expertise across multiple vendors. This complexity underscores the critical need for automation and simplification to achieve and demonstrate compliance.

Forward Networks addresses this challenge with Forward AI, allowing users to express their audit goals in natural language, such as validating consistent NTP server configurations across all devices. An agentic system then kicks in, generating a precise query to extract relevant configuration data from Forward Networks’ normalized data model, which contains configurations from all network devices. Crucially, Forward AI understands the nuances of multi-vendor environments and automatically generates platform-specific configuration templates for devices from Cisco, Arista, Juniper, Fortinet, and Palo Alto, enabling accurate interpretation and analysis of NTP settings.

This automated process swiftly assesses hundreds of devices, providing a comprehensive report detailing device names, types, configured NTP servers, and compliance status against the specified standard. For instance, the demo showed an audit of 124 devices completed rapidly, identifying discrepancies and highlighting specific device classes where the target NTP server was absent. This not only streamlines the audit process but also provides solid, verifiable evidence for compliance resolution, dramatically reducing manual effort, improving accuracy, and ensuring organizations can efficiently meet their regulatory obligations.

Event: AI Infrastructure Field Day 4

Appearance: Forward Networks Presents at AI Infrastructure Field Day

Company: Forward Networks

Video Links:

• Vimeo: Forward AI Demo – Risk Mitigation and Security with Forward Networks
• YouTube: Forward AI Demo – Risk Mitigation and Security with Forward Networks

Personnel: Nikhil Handigol

The presentation by Forward Networks demonstrated how their Forward AI platform addresses the critical security challenge of mitigating risks posed by vulnerable hosts, specifically a host named `batch 01` with unpatchable critical vulnerabilities. Traditionally, blocking internet access for such a host involves a laborious, hop-by-hop network analysis to identify firewalls and their configurations, a process that is time-consuming, prone to errors, and difficult to scale across multiple vulnerable devices. Failure to implement these blocks correctly could leave the network exposed, underscoring the need for an automated, reliable solution.

Forward AI streamlines this process significantly. Upon receiving a natural-language query such as “What firewalls do I have to block in order to remove access to the internet for host batch 01?”, the system first gathers context about the host’s vulnerabilities. It then performs a comprehensive path trace from the vulnerable host’s IP address to the entire internet (`0.0.0.0/0`), identifying all egress paths. The AI pinpoints the specific firewall (e.g., `SJC building one FW01`) and the exact access control rule currently permitting the traffic. It then provides verifiable evidence of these findings, such as showing multiple potential paths and the specific rule, and subsequently suggests precise CLI commands to implement a block, typically by modifying or adding a rule to deny traffic from the vulnerable host, thus offering a critical head start in rapid risk mitigation.

The underlying AI architecture uses state-of-the-art, off-the-shelf Large Language Models (LLMs) from providers such as Anthropic (Sonnet and Haiku models via AWS Bedrock) for natural language understanding and task planning. Crucially, these LLMs are not custom-trained or fine-tuned with proprietary networking data. Instead, deep network analysis, the network’s digital twin, and the “guardrails” that ensure the AI’s suggestions are relevant, accurate, and actionable within the network context reside within the Forward Networks platform’s agent. This modular design allows customers to plug in their own hosted LLMs while relying on Forward Networks for authoritative network intelligence and protective logic.

Event: AI Infrastructure Field Day 4

Appearance: Forward Networks Presents at AI Infrastructure Field Day

Company: Forward Networks

Video Links:

• Vimeo: Forward AI Demo Troubleshooting Network Operations with Forward Networks
• YouTube: Forward AI Demo Troubleshooting Network Operations with Forward Networks

Personnel: Nikhil Handigol

Nikhil Handigol’s presentation showcases how Forward AI revolutionizes network operations troubleshooting. Before diving into the AI capabilities, Handigol provided a concise tour of the foundational Forward Enterprise platform. This robust, deterministic software connects to all network devices across hybrid multi-cloud and multi-vendor environments to create detailed, point-in-time snapshots of network configuration and behavior. The platform offers various analytical views, including graphical topology, inventory dashboards, vulnerability assessments, blast radius analysis, and precise path tracing. A key component is the Network Query Engine (NQE), which transforms raw configuration data into a normalized, hierarchical data model and supports queries via a SQL-like language, enabling users to extract specific network insights and verify compliance against predefined checks, triggering alerts when discrepancies arise.

The core demonstration focused on how Forward AI, as a conversational interface, streamlines resolving common network connectivity issues. By ingesting a service ticket describing a host’s inability to reach a database server over SSH, the AI agent dynamically constructs and executes a diagnostic plan. This plan involves gathering context about the involved hosts and performing a precise path trace through the network’s digital twin. In the scenario presented, Forward AI swiftly identified the issue: SSH traffic was blocked by a specific firewall due to an explicit Access Control List (ACL) deny rule. Crucially, the system provides a clear, “bottom line up front” diagnosis, supported by detailed explanations of the blocking device, the rule, and the full traffic path, all substantiated with direct links to the relevant “evidence” views within the Forward application, enhancing transparency and user trust.

Extending its utility, Forward AI can also generate proposed Command Line Interface (CLI) commands as a starting point for resolving identified issues, such as creating a new firewall security policy. Nikhil strongly emphasized that these generated fixes are for planning purposes only and require human validation and adherence to established operational change procedures, underscoring that the system does not autonomously execute changes. Discussions highlighted essential guardrails, including the AI’s ability to reject unanswerable requests and the enforcement of Role-Based Access Control (RBAC) to restrict data access and command generation based on user permissions. While a feedback mechanism (thumbs up/down) is in place to gather user input for continuous improvement, future iterations may incorporate business policies into AI recommendations and develop simulation capabilities within the digital twin before deploying changes to production, further building trust and enhancing automation.

Event: AI Infrastructure Field Day 4

Appearance: Forward Networks Presents at AI Infrastructure Field Day

Company: Forward Networks

Video Links:

• Vimeo: Introducing Forward AI, chat with your network, with Forward Networks
• YouTube: Introducing Forward AI, chat with your network, with Forward Networks

Personnel: Nikhil Handigol

Forward Networks’ foundational technology is a “digital twin of the network,” which serves as a behavioral source of truth. This software platform connects to all network devices, collecting configuration and state data to build a behaviorally accurate model. It transforms raw, vendor-specific data into a queryable, vendor-independent model, analyzes all possible network behaviors, and proactively traces every conceivable packet path to determine delivery, drops, and the underlying causes. This capability goes beyond mere monitoring by enabling the network’s properties to be provably validated, including connectivity readiness and security isolation between regions. The platform collects extensive multi-vendor, multi-protocol data at scale, including tens of thousands of devices, and organizes it into a hierarchical stack of raw, normalized, behavioral, and contextual data to enable deep insights.

The company identified an “operational gap” in which network and security teams struggle to translate their goals into actionable information from disparate sources and to manage complex, multi-step workflows. Envisioning “agentic operations” where AI assists with routine tasks, Forward Networks emphasizes the critical need for robust data and trustworthy AI outputs. To address this, they introduce Forward AI, a conversational agentic system powered by the network digital twin. Forward AI provides a plain English interface, allowing operators to ask questions about devices, hosts, subnets, packet paths, and vulnerabilities, effectively bridging the gap between human intent and the complex underlying network data. While designed with agentic capabilities, the initial focus is on enabling users to gain trusted insights necessary for informed actions.

Event: AI Infrastructure Field Day 4

Appearance: Forward Networks Presents at AI Infrastructure Field Day

Company: Forward Networks

Video Links:

• Vimeo: AI is reshaping network operations with Forward Networks
• YouTube: AI is reshaping network operations with Forward Networks

Personnel: David Erickson

Networks are more critical than ever, yet their operational models have remained largely unchanged for decades, relying on manual CLIs, spreadsheets, and often outdated diagrams. This traditional approach struggles to keep pace with the rapid evolution of applications, end users, and adversaries, resulting in extremely high operational workloads due to the sheer complexity of modern networking. Enterprise networks are being fundamentally reshaped by AI, cloud, and data-intensive workloads, requiring reliable, high-performance, and secure infrastructure. While companies have increased visibility into network packets and application performance, these methods cannot answer foundational questions about network inventory, connectivity, security posture, compliance, or whether network behavior aligns with its intended design, akin to reactively treating symptoms in medicine without full diagnostic imaging.

Forward Networks addresses this challenge by pioneering a shift from reactive symptom measurement to a proactive, comprehensive understanding of the network, analogous to full-body imaging scans in medicine. Twelve years ago, the company developed a mathematically accurate digital twin of the network, building on PhD research that broke down complex network behaviors into mathematical primitives. This mathematical underpinning enables the creation of provable assurances for critical aspects such as compliance, security, reliability, and availability. The digital twin is built by exhaustively collecting configuration and protocol state data from every packet-moving device across on-premise infrastructure (switches, routers, firewalls, load balancers, Wi-Fi, SD-WAN) and cloud environments (AWS, Google, Microsoft, IBM), along with security vulnerability data, performance metrics, and contextual business data. This rigorous modeling even accounts for potential device behaviors under varying conditions and firmware changes through extensive testing.

This centralized, mathematically sound digital twin provides instant, accurate answers to a wide range of questions, from inventory and connectivity to security properties, compliance, and the impact of changes. It facilitates a major operational shift by eliminating the “toil” of manual data extraction and cross-referencing, enabling network, security, and compliance teams to collaborate around a single source of truth. Forward Networks clients reportedly experience over $14 million in annual ROI and significantly improved operational confidence. Building on this robust foundation, the company has now introduced Forward AI, a conversational interface that enables users to ask complex questions in plain English and receive trusted answers, making network knowledge effortless. This innovation leverages the digital twin’s “ground truth” to support safe, trusted, and agentic operations, human-supervised, fundamentally transforming how organizations interact with and manage their critical networks.

Event: AI Infrastructure Field Day 4

Appearance: Solidigm Presents at AI Infrastructure Field Day

Company: Solidigm

Video Links:

• Vimeo: VAST Data look at future innovations for AI and the AI OS with Solidigm
• YouTube: VAST Data look at future innovations for AI and the AI OS with Solidigm

Personnel: Phil Manez, Scott Shadley

An update from Vast Data on plans for 2026, efforts to help customers evolve their existing systems, and an early overview of the Vast AI OS platform. This session covers existing, soon-to-be-released, and future topics that will leave listeners wanting more, with more to come at an upcoming Vast event. Vast Data, founded in 2016, launched its initial storage product in 2019, based on a new shared-everything architecture designed to address the scale and efficiency challenges of migrating legacy systems into the AI era. Since then, the company has expanded to include a database product for structured and unstructured data, and is now integrating compute capabilities to enable customers to execute models and build agents. A significant focus is on addressing the pervasive “shared nothing” architecture, which, beyond storage, creates substantial problems in eventing infrastructure, leading to scaling difficulties, high write amplification from replication, and weak analytics capabilities, often causing significant delays in gaining real-time insights.

Vast Data’s shared-everything architecture aims to address these issues by providing a parallel compute layer that is ACID-compliant, ensuring event order across partitions. By treating eventing topics as tables in the Vast database, with each event as a row, they leverage storage-class memory for rapid data capture in row format, then migrate it to QLC in columnar format for robust analytics. This approach dramatically simplifies eventing infrastructure, boosts scalability, and delivers superior performance, achieving 1.5 million transactions per server and significantly reducing server count compared to legacy systems. The same “shared nothing” paradigm also plagues vector databases, leading to memory-bound systems that require extensive sharding, suffer from slow inserts and updates, and struggle to scale for rich media such as video, where vector counts can reach trillions.

Vast Data’s vector database, built on its unified architecture, addresses these challenges by supporting trillions of vectors within a single, consolidated database, eliminating the need for complex sharding. This enables seamless scalability for vector search and rapid inserts, a critical capability for real-time applications such as analyzing live video feeds, where traditional in-memory vector databases often fail. Furthermore, a key innovation is the unified security model, which applies a consistent permission structure from the original data (documents, images, videos) to their derived vectors. This ensures that large language models only access information authorized for the user, preventing unintended data exposure and maintaining robust data governance. The platform also supports data-driven workflows, automatically triggering processes such as video embedding and vector storage when new data arrives.

Event: AI Infrastructure Field Day 4

Appearance: Solidigm Presents at AI Infrastructure Field Day

Company: Solidigm

Video Links:

• Vimeo: Driving Storage Efficiency and the Impacts of AI in 2026 with Solidigm
• YouTube: Driving Storage Efficiency and the Impacts of AI in 2026 with Solidigm

Personnel: Phil Manez, Scott Shadley

A discussion between Solidigm and Vast on the efforts in the last year, from the all-flash TCO Colloborate to the way our technologies have synced to solve AI Market demands, discusses some recent Context-related impacts to the year of Inference for 2026. With the evolution of DPU-enabled inference platforms, the value and capabilities of Solidigm storage and Vast Data solutions drive even greater customer success. Solidigm’s Scott Shadley initiated the presentation by highlighting the immense power and storage demands of future AI infrastructure, using the “1.21 gigawatts” analogy. He projected that one gigawatt of power could support 550,000 NVIDIA Grace Blackwell GB300 GPUs and 25 exabytes of storage in 2025. This scale requires extremely efficient, high-capacity solid-state drives (SSDs) to stay within power envelopes, making Solidigm’s 122 terabyte drives a key enabler.

In 2026, the presentation introduced NVIDIA’s Vera Rubin platform with Bluefield 4 DPUs, which fundamentally alters AI storage architecture. This new design introduces an “inference context memory storage platform” (ICMSP) layer. This layer, positioned between direct-attached storage and object/data lake storage, is critical for rapid access to KV cache data in AI inference workloads. The new hierarchy distributes the 25 exabytes across high-capacity network-attached storage, the new 6.4 exabytes of context memory storage, and 6.1 exabytes of direct-attach storage. This evolution, while reducing the number of supportable GPUs within the 1-gigawatt limit, requires faster NVMe storage to improve performance and is projected to drive a 5x or greater compound annual growth rate (CAGR) in high-capacity storage demand.

Phil Manez from Vast Data then detailed their role in driving storage efficiency for AI. Vast’s disaggregated shared-everything (DASE) architecture separates compute from storage, utilizing Solidigm SSDs for dense capacity. This design enables global data reduction through a combination of compression, deduplication, and similarity-based reduction, achieving significantly higher data efficiency (often 3-4x more effective capacity) compared to traditional shared-nothing architectures, which is crucial amidst SSD supply constraints. Critically, Vast can deploy its C-node (storage logic) directly on the powerful Bluefield 4 DPUs, creating a highly optimized ICMSP. This approach accelerates time to first token, boosts GPU efficiency by offloading context computation, and dramatically reduces power consumption by eliminating intermediate compute layers, enabling AI inference workloads to operate at unprecedented speed and scale with shared, globally accessible context.

Event: AI Infrastructure Field Day 4

Appearance: Solidigm Presents at AI Infrastructure Field Day

Company: Solidigm

Video Links:

• Vimeo: Redefining Scale and Efficiency for the Al Era with Solidigm
• YouTube: Redefining Scale and Efficiency for the Al Era with Solidigm

Personnel: Phil Manez, Scott Shadley

Solidigm presents on the current state of the Storage market. A view of what technologies are driving change, the solutions provided to overcome some of the challenges, and a look at how the latest innovations for early 2026 impact the view of storage in the AI pipeline and deployment efforts. Including the scope of high-capacity storage, what is coming, and how the impact of storage is more paramount than ever for AI deployments.

Scott Shadley, representing Solidigm, emphasized the company’s role as a hardware storage provider and highlighted its crucial partnership with software solutions such as VAST Data, represented by Phil Manez. Using a vivid donut analogy, Shadley explained the evolution of storage from traditional hard drives, akin to a “glazed donut with a hole,” to modern SSDs, symbolized by the “maple bar” form factor. The analogy extended to VAST Data as the “jelly” filling the donut, providing essential software solutions for data management and utility. He then delved into the cyclical nature of the semiconductor market, detailing past shifts such as the transition from 2D to 3D NAND and the impact of pandemic-induced hoarding, culminating in the current AI “bubble.” This unprecedented demand, coupled with historical underinvestment in NAND relative to memory, has created significant challenges for storage supply, necessitating long-term agreements and driving the need for Solidigm to innovate beyond building drives.

Solidigm’s strategy for the AI era focuses on delivering both high-performance and high-capacity storage, with products such as the PS1010 for performance and the P5336 (122TB drives) leading in high-capacity shipments. Beyond products, the company is deeply involved in enabling new cooling architectures essential for AI infrastructure. This includes pioneering liquid cold plate designs, contributing to industry standards (SNIA) to ensure vendor compatibility, and validating off-the-shelf products for full-immersion cooling, while addressing practical challenges such as adhesion of stickers in immersion fluids. To further support customers, Solidigm established the AI Central Lab, an independent facility offering remote access to diverse AI architectures, including Hopper, Blackwell, and future Vera Rubin platforms. This lab enables partners and customers to test and optimize solutions, overcoming barriers related to infrastructure availability and cost, and has already demonstrated significant improvements, such as a 27x faster “time to first token” by offloading the KV cache to SSDs, showcasing Solidigm’s deeper involvement in overall AI system functionality.

Event: AI Infrastructure Field Day 2

Appearance: Signal 65 Presents at AI Infrastructure Field Day

Company: Signal65

Video Links:

• Vimeo: Compute-ready data for AI with Futurum Signal65
• YouTube: Compute-ready data for AI with Futurum Signal65

Personnel: Brian Martin

Enterprises are discovering that PDFs are for people; machines need something else. As AI systems move from experimentation to production, raw documents limit accuracy, performance, and trust. This session covers a proof-of-concept engagement exploring why unstructured content must be transformed into secure, compute-ready assets to deliver faster insights, predictable performance, and true data sovereignty. This solution enables processing data once to power AI everywhere in your organization. Brian Martin, VP of AI and Data Center Performance at Signal65, presented the work of their AI lab, which is sponsored by Dell Technologies and focuses on real-world impact tests of AI workloads using extensive AI infrastructure, including various Dell XE servers and NVIDIA GPUs. The lab also developed a digital twin to optimize its physical layout, particularly for complex designs such as slab-on-grade data centers with overhead utilities, demonstrating an immediate positive ROI by identifying costly design changes early.

The presentation then transitioned to the critical challenge of preparing data for AI models. Echoing the sentiment that “garbage in, garbage out” becomes “expensive garbage out” with AI infrastructure, Signal65 highlighted how raw, unstructured data, particularly PDFs, hinders AI accuracy, performance, and trust. PDFs are designed for human consumption, not machine processing. Gadget Software addresses this by offering “compute-ready data,” which transforms unstructured content into AI-digestible formats. This process involves semantic decomposition to maintain topic continuity, LLM enrichment to generate useful metadata such as summaries, keywords, sentiment, and sample Q&A pairs, and robust governance and security through unique IDs and lineage tracking. This approach overcomes the limitations of traditional chunking and pure vectorization, which often lose context and attribution, making it difficult to cite sources or enforce security policies.

In a proof of concept using the vast United States Federal Register, Signal65 demonstrated the tangible benefits of this compute-ready data pipeline. The process ensured that all AI responses could be traced back to the original documents, which is crucial for governance and security. Performance testing revealed a significant advantage for local GPU processing (using L40S and RTX Pros) compared to accessing cloud LLM APIs. Local processing delivered remarkably consistent, flat latency during data ingestion and enrichment, in contrast to the spiky, unpredictable latency observed with cloud APIs, regardless of document size. This “write once, read many” approach ensures that once data is processed and enriched, it can be reliably accessed by various AI applications, such as chatbots or BI tools, delivering consistent, attributable results. Furthermore, the prepared data facilitates user interaction by enabling intuitive dashboards that showcase data content and suggest relevant queries, addressing the common user challenge of “what can I ask?”

Event: AI Infrastructure Field Day 4

Appearance: Xsight Labs Presents at AI Infrastructure Field Day

Company: Xsight Labs

Video Links:

• Vimeo: Interface Masters Smart Switch Delivers 11.2tbps Switching and Routing With Xsight Labs DPUs
• YouTube: Interface Masters Smart Switch Delivers 11.2tbps Switching and Routing With Xsight Labs DPUs

Personnel: John C Carney, Ted Weatherford

Xsight Labs, represented by VP of BizDev Ted Weatherford and distinguished engineer John Carney, introduced a smart switch developed with Interface Masters, designed for government security applications. This 1RU device offers 28x 400G connectivity using modern QSFP112 100G SerDes, powered by Xsight Labs’ X2 12.8terabit Ethernet switch chip. A key feature is 1.6 terabits of line-rate stateful processing (Layer 4-7) provided by two E1 DPUs, which together offer 128 ARM cores and up to 1 terabyte of DRAM (0.5 TB per DPU). This solution boasts significantly higher compute and networking density per rack unit compared to incumbent offerings, delivering a “line rate smart switch” that is available bare metal.

The presentation also showcased a larger 6.4 terabit top-of-rack (ToR) solution, integrating eight E1 DPU add-in cards into a PCIe motherboard, providing 500 Neoverse 2 ARM cores and up to 2 terabytes of memory. This proof of concept, developed for a major US Cloud Service Provider, demonstrated substantial power and cost savings, up to 25% less power and less than half the cost, compared to traditional deployments. Crucially, both the E1 and X2 chips incorporate comprehensive packet timing features, including a stratum three clock, real-time clock logic for timestamping interfaces, PTP synchronization, and physical PPS in/out connectors, making them suitable for timing-sensitive applications.

Xsight Labs positions its X-series Ethernet switch as a superior ToR upgrade strategy for AI and general compute fabrics. Instead of relying on expensive, high-power, and often oversized 51.2 terabit switches, their solution offers a programmable, high-performance alternative at a fraction of the cost ($1,500 vs. $14,000), consuming significantly less power (300-400 watts) and occupying just one RU. This not only frees up rack space for additional GPUs but also provides lower latency and flexible congestion management. By optimizing for typical ToR needs, where downlink speeds rarely exceed 400 gig per NIC, Xsight Labs aims to reduce overall infrastructure costs and power consumption in data centers and infrastructure-as-a-service environments.

Event: AI Infrastructure Field Day 4

Appearance: Xsight Labs Presents at AI Infrastructure Field Day

Company: Xsight Labs

Video Links:

• Vimeo: Warm Flash for AI Context Storage using the Open Flash Platform with Xsight Labs and Hammerspace
• YouTube: Warm Flash for AI Context Storage using the Open Flash Platform with Xsight Labs and Hammerspace

Personnel: Kurt Kuckein, Ted Weatherford

The Open Flash Platform, co-founded by Xsight Labs and Hammerspace, introduces a new approach to “warm flash storage” for AI context, promising enhanced efficiency and performance. This collaborative effort leverages Hammerspace’s software, built on a Linux-based NFS file system that supports distributed management. By separating metadata from the data path and decentralizing storage, the platform eliminates traditional x86 servers, drastically reducing total cost of ownership, power consumption, and system complexity. This streamlined architecture also minimizes data hops, improving performance for large AI clusters by enabling direct data access to storage targets while managing metadata out of band.

Xsight Labs contributes its E1 DPU, forming the core of a unique cartridge design developed with Lumineer. Each cartridge is a self-contained “server on a cartridge,” featuring two 400 GbE ports for NVMe-style fabric and eight flash drives. These cartridges offer exceptional density, allowing for exabyte-scale memory within a standard seven-foot rack. The E1 chip’s ability to run a full Linux operating system makes it more powerful than a simple network interface card, supporting additional use cases beyond mere data transport. Data protection is managed through erasure coding across multiple blades rather than within individual SSDs, embracing a model where the blade is the consumable unit, further simplifying infrastructure and reducing failure points.

The concept of “warm flash” is vital for AI, as these applications require data that is always ready and accessible, rather than relying on traditional cold storage. This aligns with a growing customer demand to move away from disk drives towards all-flash environments, even for archival purposes, as flash longevity has significantly improved. The Xsight Labs E1 chip is precisely balanced for this, delivering optimal throughput without overprovisioning and ensuring quick data extraction even from very large flash capacities per blade. With significant market interest, the product is moving towards production, with initial releases expected soon and full production anticipated by year-end, underscoring a successful partnership focused on software-defined efficiency.

Event: AI Infrastructure Field Day 4

Appearance: Xsight Labs Presents at AI Infrastructure Field Day

Company: Xsight Labs

Video Links:

• Vimeo: Real World Deployments for AI at the Edge with Xsight Labs
• YouTube: Real World Deployments for AI at the Edge with Xsight Labs

Personnel: John C Carney, Ted Weatherford

This final technical section transitions from theoretical architecture to practical use cases, spanning Warm Flash Storage to “Extreme Edge” networking satellites. It showcases industry-first milestones, such as the 800G DPU for virtualized hosting and SmartSwitch technology for NIC pooling. Each example demonstrates how the X and E series products solve specific bottlenecks in modern cloud compute and AI storage networks. Xsight Labs, a nine-year-old fabless semiconductor company, focuses on real-world deployments for AI at the edge using its X series Ethernet switch and E series DPU. Their core philosophy centers on being software-defined, appealing to software engineers by offering performance comparable to fixed-function products while providing greater flexibility through an open instruction set architecture and Linux-based programming with tools such as DPTK or Open Virtual Switch. They target the edge market, believing it holds the highest volume, and have designed their single-die products for extreme power efficiency and high performance.

The company’s chips are deployed in diverse settings, from the “extreme edge” to terrestrial wireless infrastructure. A significant win is their integration into Starlink Gen 3 satellites, where multiple Ethernet switches per satellite are being launched at scale. This required Xsight Labs to deliver unparalleled programmability, power efficiency, and resilience against vibration, radiation, and extreme temperatures, crucial for a system that cannot be physically serviced. Similarly, their programmable Ethernet switches and DPUs are ideal for 5.5G or 6G terrestrial wireless infrastructure, addressing the complex, stateful packet-processing needs of antennas and associated processing units. These low-power, single-die solutions offer advantages in temperature range, cost, and operating expenses, including reduced carbon footprint.

Xsight Labs is also targeting the expanding AI market, particularly for inference, which is pushing computing out into half-rack, full-rack, and multi-row deployments. Their DPUs serve as front-end and scale-out back-end solutions for these systems, enabling very high-density general compute. Additionally, their Ethernet switches are used to cluster these AI systems, marking a departure from traditional “clos” architectures by supporting local clustering topologies such as Dragonfly. For example, in AI training systems similar to Amazon’s ultra-servers, Xsight Labs’ products with 100G serdes and 6.4T/12.8T switches can replicate or enhance existing topologies. The Starlink win underscores their capability to provide future-proof, high-performance, and power-efficient solutions essential for the most demanding and inaccessible environments.

Event: AI Infrastructure Field Day 4

Appearance: Xsight Labs Presents at AI Infrastructure Field Day

Company: Xsight Labs

Video Links:

• Vimeo: The E-Series Delivering Cloud-on-a-Chip from Xsight Labs
• YouTube: The E-Series Delivering Cloud-on-a-Chip from Xsight Labs

Personnel: John C Carney, Ted Weatherford

The E-Series session explores the convergence of storage, networking, compute, and security into a single, cohesive silicon platform. This “Cloud-on-a-chip” approach is dissected through its architecture and programming model to show how it simplifies complex data center environments. We will highlight our partnership with the Hammerspace solution, demonstrating how E-Series silicon powers a global data environment. Xsight Labs presents its E-Series, a System-on-a-Chip (SOC) designed to deliver “Cloud-on-a-chip” capabilities by integrating essential cloud elements. This includes Ethernet connectivity, robust security features, virtualized storage, and powerful processing via 64 ARM Neoverse N2 cores. The E-Series chip, which has been generally available for about four months, is offered in various form factors, including a server, an add-in card, and a COMEX module, targeting applications ranging from embedded systems to full servers.

Xsight Labs differentiates its E-Series architecture from traditional DPUs, which typically evolve from a NIC with a constrained CPU cluster. The E-Series began with a server-class compute system featuring 64 ARM Neoverse N2 cores, specifically optimized and sized for data-plane applications. This allows all packets and PCIe transactions to be terminated and processed in software using standard programming models like Linux, DPDK, or SPDK, eliminating proprietary code. The chip integrates an E-unit for Ethernet connectivity, offering inline encryption and stateless offloads, and a P-unit for PCIe Gen 5, providing up to 40 lanes and 800Gb bandwidth. This PCIe unit can software-emulate various devices (storage, networking, RDMA), offering immense flexibility. With a typical power consumption of 50-75W (up to 120W TDP) and a SpecInt rating of 170, the E-Series offers significant compute power efficiently. Beyond network and memory encryption, the roadmap for the follow-on E2 product includes CXL support, targeting 1.6T bandwidth.

The E-Series supports a broad range of use cases, from front-end DPUs in public cloud and AI clusters (offloading the host, providing virtualization and isolation) to back-end DPUs in AI inference clusters for KV cache offload. It also extends to local storage, bump-in-the-wire network appliances for security and load balancing, smart switches for stateful processing, edge servers, and storage target appliances. Xsight Labs provides a comprehensive software development kit, ensuring compatibility with standard ARM server operating systems such as Ubuntu, as well as Linux and DPDK drivers. A key demonstration of the E-Series’ capability is its performance on the Sonic Dash “Hero Benchmark,” a highly intensive SDN workload. This test requires processing millions of routes, prefixes, and mappings, which largely depends on off-chip DRAM due to poor cache locality. The E1 exceeded the benchmark requirement of 12 million new connections per second with 120 million background connections, without packet drops, by almost 20%, while still retaining CPU capacity for control-plane operations, making it the only DPU to pass this test at 800Gb with a single device.