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Introducing Forward AI, chat with your network, with Forward Networks

Event: AI Infrastructure Field Day 4

Appearance: Forward Networks Presents at AI Infrastructure Field Day

Company: Forward Networks

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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.


AI is reshaping network operations with Forward Networks

Event: AI Infrastructure Field Day 4

Appearance: Forward Networks Presents at AI Infrastructure Field Day

Company: Forward Networks

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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.


VAST Data look at future innovations for AI and the AI OS with Solidigm

Event: AI Infrastructure Field Day 4

Appearance: Solidigm Presents at AI Infrastructure Field Day

Company: Solidigm

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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.


Driving Storage Efficiency and the Impacts of AI in 2026 with Solidigm

Event: AI Infrastructure Field Day 4

Appearance: Solidigm Presents at AI Infrastructure Field Day

Company: Solidigm

Video Links:

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.


Redefining Scale and Efficiency for the Al Era with Solidigm

Event: AI Infrastructure Field Day 4

Appearance: Solidigm Presents at AI Infrastructure Field Day

Company: Solidigm

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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.


Compute-ready data for AI with Futurum Signal65

Event: AI Infrastructure Field Day 2

Appearance: Signal 65 Presents at AI Infrastructure Field Day

Company: Signal65

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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?”


Interface Masters Smart Switch Delivers 11.2tbps Switching and Routing With Xsight Labs DPUs

Event: AI Infrastructure Field Day 4

Appearance: Xsight Labs Presents at AI Infrastructure Field Day

Company: Xsight Labs

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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.


Warm Flash for AI Context Storage using the Open Flash Platform with Xsight Labs and Hammerspace

Event: AI Infrastructure Field Day 4

Appearance: Xsight Labs Presents at AI Infrastructure Field Day

Company: Xsight Labs

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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.


Real World Deployments for AI at the Edge with Xsight Labs

Event: AI Infrastructure Field Day 4

Appearance: Xsight Labs Presents at AI Infrastructure Field Day

Company: Xsight Labs

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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.


The E-Series Delivering Cloud-on-a-Chip from Xsight Labs

Event: AI Infrastructure Field Day 4

Appearance: Xsight Labs Presents at AI Infrastructure Field Day

Company: Xsight Labs

Video Links:

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.


The X Series Architecting for High Performance Scale with Xsight Labs

Event: AI Infrastructure Field Day 4

Appearance: Xsight Labs Presents at AI Infrastructure Field Day

Company: Xsight Labs

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Personnel: John C Carney, Ted Weatherford

This section provides a high-level overview of the product roadmap, specifically introducing the X-Series and E-Series lineups. It identifies the six critical chips required to build modern AI Factories and explains the concept of a “Truly Software Defined” stack that operates at full line-rate across layers L1-7. This serves as the technical foundation for the subsequent specialized deep dives.

The X-Series, an Ethernet switch, distinguishes itself through a “truly software-defined” programmable architecture, utilizing 3072 Harvard architecture cores operating on a “run-to-complete” model, unlike competitors’ fixed pipelines. This provides unparalleled flexibility, enabling parallel packet operations, recursion, and extensive header processing, including 11 layers of MPLS and various encapsulations. This design is particularly well-suited for emerging AI-centric protocols such as Ultra Ethernet (UEC) and ESON, enabling customizable congestion management and efficient in-flight packet handling. The X-Series boasts significantly lower latency, achieving 450 nanoseconds compared to the typical 800 nanoseconds, and demonstrates exceptional buffer utilization, consistently above 86% even under heavy load.

The X-Series also stands out with its low power consumption, operating at under 200 watts for a 12.8T switch, which is described as disruptive. Its software-defined physical layer supports diverse SERDES speeds (10G to 200G) and modulation schemes, enabling mixed-and-matched configurations that facilitate connections between new and legacy interfaces. The programming model, though initially assembler-based with Python wrappers and libraries, has seen customers such as Oxide develop P4 compilers, with Xsight Labs planning to develop their own. This powerful, flexible, and low-power solution is specifically designed for edge deployments, including half-rack to two-rack configurations, satellites, and base stations, delivering significant reductions in power, rack space, and cost. The X-Series product was generally available in November 2022 and has been in mass production since the summer of 2023.


Redefining Infrastructure Philosophy – the Xsight Labs Vision

Event: AI Infrastructure Field Day 4

Appearance: Xsight Labs Presents at AI Infrastructure Field Day

Company: Xsight Labs

Video Links:

Personnel: Ted Weatherford

This introductory session establishes the company’s core identity and its unique approach to the semiconductor market. It explores a product philosophy built on the pillars of extreme scalability, open architecture, and vertical integration to reduce Total Cost of Ownership (TCO). By the end of this section, the audience will understand how the company’s commitment to agility and simplicity drives its engineering decisions. Xsight Labs, a fabless semiconductor company founded in 2017, designs and sells chips manufactured through TSMC. Led by serial entrepreneurs and backed by $440 million in top-tier VC funding, the company employs over 200 engineers globally. Their unique approach aims to democratize the semiconductor space by providing open, programmable, and vertically integrated solutions for the rapidly evolving AI and data center infrastructure markets.

Xsight Labs focuses on two critical components of the “AI factory” or “token machine”: an Ethernet switch chip (X-series) and a Data Processing Unit (DPU) or infrastructure processor (E-series). These products are developed on 5nm technology, are generally available, and the X-series is already in mass production. The company emphasizes a “software-defined infrastructure” philosophy, claiming to be the first chip company to offer wire-speed, energy-efficient, and truly programmable products without compromising performance, price, or power. This agility is crucial given the unpredictable nature of future AI applications, and their open instruction sets and collateral allow for community contributions and custom compilers, accelerating innovation.

The E-series DPU, specifically the E1 800 gig product, is designed from an ARM server perspective rather than a traditional network interface card, offering 64-core ARM chips with derivatives to optimize for various power and performance needs. The upcoming E1L will be a low-power version targeting control plane markets and programmable SmartNICs. The X-series Ethernet switch, with its X2 12.8 terabit monolithic die, stands out for its exceptionally low power consumption (180W compared to competitors’ 300-600W) while maintaining high performance, low latency, and full programmability from Layer 1 to Layer 4 with embedded memory switches. The future X3 will further expand bandwidth and radix points through clever die combining, reinforcing Xsight Labs’ commitment to innovative, power-efficient, and highly flexible infrastructure solutions.


A Leap Forward in Storage Efficiency with the OFP Initiative and Hammerspace

Event: AI Infrastructure Field Day 4

Appearance: Hammerspace Presents at AI Infrastructure Field Day

Company: Hammerspace

Video Links:

Personnel: Kurt Kuckein, Ted Weatherford

Hammerspace is driving the Open Flash Platform (OFP) Initiative, an effort to significantly reduce the complexity and cost associated with large-scale flash storage for AI and other demanding workloads. This presentation introduced a reference design for a high-density, low-power flash storage solution that achieves unprecedented capacity and efficiency within data centers. The goal is to deliver one exabyte of storage in a single rack, enabling a new paradigm of “disappearing storage” in which compact 1U systems are distributed throughout a data center, leveraging otherwise unused rack space and minimal power consumption.

The development process involved several design iterations, shifting from a challenging 2U form factor to a more efficient 1U design. This shift addressed issues such as chassis deformation, power/cooling inefficiencies, and wasted space, requiring extensive thermal and pressure analyses to ensure reliable operation in a tightly packed environment. A significant breakthrough was selecting the Xsight DPU, which delivers robust compute capabilities comparable to an x86 server from a few years ago, in a highly power-efficient package that supports Linux and storage services within this compact design. Ted Weatherford highlighted the Xsight E1 chip as the world’s first 800-Gig DPU, featuring 64 Neoverse cores, a programmable NIC, and an “all fast path design” that eliminates data bottlenecks, achieving 800-Gig line rates, as independently verified by KeySite.

Looking ahead, Hammerspace and its partners are actively exploring new flash form factors to overcome current E2 limitations and achieve the one exabyte-per-rack goal. The OFP Initiative aims to standardize within the Open Compute Project (OCP) to ensure broad industry adoption and benefits. The versatility of the Xsight chip enables applications beyond shared file storage, including block storage and a homogeneous boot device for hyperscalers, streamlining qualification and management across diverse server infrastructures. The project is currently in prototyping and validation, with early-access customers receiving units this quarter and general availability targeted for the second half of the year, while continually recruiting more industry participants to drive this standard forward.


Unifying AI Enterprise Data into a Single Instantly Accessible Global Namespace with Hammerspace

Event: AI Infrastructure Field Day 4

Appearance: Hammerspace Presents at AI Infrastructure Field Day

Company: Hammerspace

Video Links:

Personnel: Kurt Kuckein, Sam Newnam

Hammerspace introduced its AI Data Platform solution to address the pervasive challenge of data fragmentation, a significant inhibitor to AI readiness. The presentation highlighted the complexity of AI tooling and the substantial capital outlay required, leading to enterprise fears of missing out (FOMO) and messing up (FOMU) on AI initiatives. Their solution aims to simplify these challenges by integrating seamlessly with NVIDIA’s reference designs to deliver a comprehensive, outcome-driven platform rather than a complex toolkit of disparate components.

Hammerspace’s AI Data Platform combines its unique global namespace and Tier Zero capabilities with NVIDIA software, including RAG Blueprints and RTX 6000 Pro, and is often deployed on standard servers such as Cisco C210s. This platform allows enterprises to connect to existing hybrid data through assimilation, whether full or read-only, making vast amounts of legacy data instantly accessible without costly and time-consuming migrations. The core mechanism involves discovering new files and automatically moving them to Tier Zero, a high-performance NVMe flash layer within the servers, for intensive processing such as extraction, embedding, and indexing. This heavy lifting is performed without burdening existing storage systems, with Hammerspace managing the entire process from data ingestion and validation to cleanup, ensuring AI-ready data is available in minutes. The software-defined nature enables flexibility across various hardware platforms and cloud environments, while leveraging protocols such as PNFS and NFS-direct to optimize GPU utilization.

The ultimate goal of Hammerspace’s AI Data Platform is to accelerate time-to-value by eliminating data gravity and GPU gravity. By shifting to a data-first strategy, the platform integrates data categorization and tagging, embedding security and performance characteristics directly into the data’s metadata. This enables automated, intelligent decisions about data placement and processing, replacing manual, script-driven workflows with an intuitive agentic system. This approach allows organizations to leverage their existing capital investments, transforming fragmented enterprise data into a unified, instantly accessible global namespace for AI applications within weeks, effectively creating an AI factory that starts where they are.


Taming Data Estate Chaos for AI with Hammerspace

Event: AI Infrastructure Field Day 4

Appearance: Hammerspace Presents at AI Infrastructure Field Day

Company: Hammerspace

Video Links:

Personnel: Kurt Kuckein, Sam Newnam

Hammerspace introduces itself as a “data company,” distinguishing itself from traditional storage vendors by offering a solution that addresses the complex data demands of modern infrastructure, particularly for AI workloads. The core concept behind Hammerspace is an instantly accessible, infinite virtual space that disaggregates data from its underlying infrastructure, enabling it to reside in any location, across any cloud, and on any storage backend, thereby eliminating data silos. This is achieved by assimilating metadata from existing storage systems into a single, global namespace, managed by metadata servers outside the data path. This approach not only accelerates data pipelines but also enhances existing infrastructure and enables rapid, easy integration of new technologies, providing users with visibility and access to all their data within minutes, rather than requiring lengthy, costly data migrations.

Hammerspace extends its capabilities to address critical challenges in AI infrastructure, including the current tight market and rising flash memory costs. The solution leverages underutilized flash storage within existing environments by aggregating systems and intelligently orchestrating data placement across tiers. It introduces “Tier Zero,” which consumes and aggregates local flash within compute (CPU and GPU) clusters into the global namespace, providing extremely high-performance storage by eliminating network latency. Hammerspace also treats cloud storage as a direct extension of on-premises infrastructure, not just a destination for data, thereby maximizing the use of available flash resources. The software-defined platform ensures data portability and access through a parallel file system (PNFS v4.2) and multi-protocol access (S3, NFS, SMB). Importantly, its policy-driven orchestration automates data movement and ensures data durability and availability through redundant metadata nodes and erasure coding across storage systems. It also centralizes privileged access and security policies, allowing permissions to follow data regardless of its physical location, critical for cross-border data compliance and auditability, and supports rich custom metadata beyond basic POSIX attributes.

Customer examples illustrate these benefits, such as a digital payments company that reduced storage costs by $5 million and simplified workflows for 3,000 data scientists by providing parallel file system access over object storage and enabling hybrid cloud agility. Another customer, facing a 3-4x increase in performance demand from new NVIDIA servers, leveraged Hammerspace to maintain existing NAS systems while deploying high-performance NVMe storage, avoiding significant new infrastructure investments. For inference workloads where latency is critical, Hammerspace can use policies to preload entire projects into local NVMe (Tier Zero) directly connected to GPUs, maintaining high performance and data consistency across globally distributed inference farms. Ultimately, through its integration with platforms like the NVIDIA AI Data Platform, Hammerspace goes beyond merely unifying data access; it truly unlocks the value within data by automating data preparation and orchestration, moving organizations from data chaos to a state of AI-ready data, often allowing interaction with the system via natural language for streamlined management.


Fabrix.ai Demo – Building Agentic AI at scale for Production

Event: AI Infrastructure Field Day 4

Appearance: Fabrix.ai Presents at AI Infrastructure Field Day

Company: Fabrix.ai

Video Links:

Personnel: Rached Blili

Fabrix.ai is building agentic AI at scale for production, moving beyond proofs of concept to deliver robust solutions. In the video from the Fabrix.AI channel, Rached Blili demonstrated the Fabrix.ai platform, highlighting its agent catalog, where users can access and manage a variety of agents, both developed by Fabrix.ai and custom-built. The platform offers an AI Storyboard dashboard that provides a comprehensive view of AI operations, enabling agents to be organized into projects with distinct permissions and toolsets. A significant emphasis is placed on observability, including detailed AI cost tracking at both global and project levels, and visibility into individual “conversations” or agentic sessions. Uniquely, Fabrix.ai provides performance evaluation for agents, treating them as digital workers by monitoring their performance over time, identifying top and underperforming agents, and suggesting specific fixes, such as modifying system prompts, to continuously improve their efficacy.

The demonstration showcases two types of agents: autonomous and interactive. Autonomous agents operate in the background, triggered by events, alerts, or schedules, as exemplified by a Network Root Cause Analysis agent. This agent automatically diagnoses network failures, such as router configuration errors, by analyzing logs, incident data, and router configurations. It generates comprehensive reports detailing the root cause, impact assessment, and multiple remediation plans, which a remediation agent can then use for automated implementation and verification. For interactive use, Fabrix.ai’s copilot, Fabio, enables users to converse directly with agents to manage complex tasks, such as verifying VPNs or configuring Netflow in a lab network, significantly reducing manual intervention and saving time.

Delving into the underlying architecture, the presentation revealed that complex problems are tackled using multi-agent complexes, where an orchestrator agent calls specialized sub-agents, each handling a specific part of the problem with a sequestered context. This approach enhances individual agents’ capabilities while enabling detailed cost management, tracking token usage, time, and expenses, and capturing individual agent contributions within a hierarchical structure. A detailed example illustrated an application root-cause analysis in which the orchestrator agent systematically investigated incident details, application dependency maps, and even interpreted plain-English change requests from a ticketing system. The platform’s advanced context and tooling engines are critical to operating at scale, enabling mass operations across numerous devices in parallel and efficiently processing vast tool outputs by storing them in a context cache for later retrieval and analysis, ensuring effective, secure, and reliable agent deployment.


Crossing the Production Gap to Agentic AI with Fabrix.ai

Event: AI Infrastructure Field Day 4

Appearance: Fabrix.ai Presents at AI Infrastructure Field Day

Company: Fabrix.ai

Video Links:

Personnel: Rached Blili

Fabrix.ai highlights the critical challenges in deploying agentic AI from prototype to production within large enterprises. The Rached Blili noted that while agents are quick to prototype, they frequently fail in real-world environments due to dynamic variables. These failures typically stem from issues in context management, such as handling large tool responses and maintaining “context purity,” as well as from operational challenges related to observability and infrastructure, including security and user rights. To overcome these hurdles, Fabrix.ai proposes three core principles: moving as much of the problem as possible to the tooling layer, rigorously curating the context fed to the Large Language Model (LLM), and implementing comprehensive operational controls that monitor for business outcomes rather than just technical errors.

Fabrix.ai’s solution is a middleware built on a “trifabric platform” comprising data, automation, and AI fabrics. This middleware features two primary functional components: the Context Engine and the Tooling and Connectivity Engine. The Context Engine focuses on delivering pure, relevant information to the LLM through intelligent caching of large datasets (making them addressable and providing profiles such as histograms) and sophisticated conversation compaction that tailors summaries to the current user goal, preserving critical information better than traditional summarization. The Tooling and Connectivity Engine serves as an abstraction layer that integrates various enterprise tools, including existing MCP servers and non-MCP tools. It allows tools to exchange data directly, bypassing the LLM and preventing token waste. This engine uses a low-code, YAML-based approach for tool definition and dynamic data discovery to automatically generate robust, specific tools for common enterprise workflows, thereby reducing the LLM’s burden and improving reliability.

Beyond these core components, Fabrix.ai emphasizes advanced operational capabilities. Their platform incorporates qualitative analysis of agentic sessions, generating reports, identifying themes, and suggesting optimizations to improve agent performance over time, effectively placing agents on a “performance improvement plan” (PIP). This outcome-based evaluation contrasts with traditional metrics like token count or latency. Case studies demonstrated Fabrix.ai’s ability to handle queries across vast numbers of large documents, outperforming human teams in efficiency and consistency, and to correlate information across numerous heterogeneous systems without requiring a data lake, thanks to dynamic data discovery. The platform also includes essential spend management and cost controls, recognizing the risk that agents may incur high operational costs if not properly managed.


Build Reliable, Secure, and Performant Agents using Fabrix.AI AgentOps Platform

Event: AI Infrastructure Field Day 4

Appearance: Fabrix.ai Presents at AI Infrastructure Field Day

Company: Fabrix.ai

Video Links:

Personnel: Shailesh Manjrekar

Fabrix.AI addresses the evolving AI operations landscape with an AgentOps platform that builds reliable, secure, and high-performance agents. The company, formerly Cloudfabrics.com, rebranded as Fabrix.AI in response to customer demand for agentic functionality, moving beyond traditional AIOps, which relies on manual remediation after correlation and root-cause analysis. This shift was motivated by real-world challenges, such as an 8-hour telco outage caused by inadvertent access control list changes, highlighting the need for autonomous or semi-autonomous remediation workflows powered by Large Language Models (LLMs). However, this transition introduces new complexities, including the non-deterministic nature of LLMs, context and data management at scale, and the challenge of connecting to diverse data sources, which can lead to issues such as hallucination and an “agentic value gap,” where experimental demos rarely translate to enterprise value.

Fabrix.AI’s solution centers on proprietary middleware that serves as a critical intermediary between AI agents/LLMs and various data sources. This middleware comprises two main components: the Context Engine and Universal Tooling. The Context Engine ensures “purity of context” by providing only curated, summarized data to the LLM, thereby preventing context corruption and reducing hallucination, while also maintaining state across interactions. The Universal Tooling dynamically connects to over 1,700 disparate data sources, including MCP-enabled endpoints, API-based systems, and raw or legacy data, by creating necessary wrappers and normalizing data schemas for LLM understanding, and can even dynamically generate tools by scraping public APIs. This approach allows the platform to integrate seamlessly with existing IT environments, offering a full-stack solution from data acquisition to automation.

The platform is purpose-built for real-time data environments, differentiating it from generic agentic frameworks that may not meet these requirements. It offers a “co-pilot” for conversational queries and an “Agent Studio” for building custom agents, supplementing its library of 50 out-of-the-box agents across AIOps, Observability, SecOps, and BizOps. Fabrix.AI emphasizes operationalizing agents through its AgentOps model, which incorporates trust via prompt templates and dynamic instructions, governance through FinOps models, security via a “least agency” principle, and comprehensive observability at the agentic layer with audit trails and real-time flow maps. By consolidating tools, reducing Mean Time to Resolution (MTTR) and alert noise, and enabling faster deployments, Fabrix.AI positions itself as a robust, enterprise-grade platform that complements and enhances existing observability and ITOM tools.


Resilient Wireless Networks for AI with Cisco Enterprise Networking

Event: AI Infrastructure Field Day 4

Appearance: Cisco Enterprise Networking Presents at AI Infrastructure Field Day

Company: Cisco

Video Links:

Personnel: Minse Kim

Minse Kim, Cisco’s wireless product manager, emphasized that the AI era is profoundly changing enterprise networking, extending beyond data centers to encompass “physical AI” applications in factories, medical facilities, and dynamic workspaces. He noted that surging demand for AI infrastructure components is also influencing customer buying cycles, with some customers proactively investing in Wi-Fi 7 now. A key insight is that while AI infrastructure is often perceived as data center-centric, the actual consumption and training of AI models, particularly for robotics and autonomous systems, relies heavily on high-performance, low-latency wireless connectivity, making Wi-Fi 6, 6E, and 7 crucial “last mile” technologies. Cisco’s Wi-Fi 7 access points are designed to meet these demands, offering multi-gigabit speeds and backhaul capabilities up to 20 Gbps per AP.

Addressing Wi-Fi’s traditional reliability-versus-speed trade-off, Cisco has developed Ultra-Reliable Wireless Backhaul (URWB) capabilities integrated into its Wi-Fi 7 APs. By dedicating a radio, URWB provides a stable, predictable, and low-latency “wired-like” connection, which is essential for critical applications like robotics that cannot tolerate the blips and jitters common in traditional Wi-Fi during client roaming. Beyond connectivity, Cisco Wi-Fi 7 APs also enhance spatial awareness and location services. Leveraging technologies such as 802.11mc (FTM) and Ultra-Wideband (UWB) with sensor fusion, these APs deliver sub-meter (e.g., one-foot) location accuracy and low latency, resolving long-standing problems in asset tracking and network operations, as demonstrated by real-time asset tracking in an office environment. This ability to accurately digitize the physical world is fundamental for AI analytics.

Furthermore, Cisco is integrating AI into network operations to simplify management and optimize performance. For instance, AI models leverage telemetry data from 35 million Cisco APs globally to intelligently manage firmware upgrades, learning from customer rollback decisions to improve future deployments. AI also enhances Radio Resource Management (RRM) by moving beyond simple rule-based engines to intelligently optimize RF configurations, leveraging historical interference patterns and dynamically adapting to environmental changes to maximize network efficiency and stability. Cisco is even introducing the concept of APs acting as “synthetic clients” to proactively collect network statistics and provide informed recommendations. This comprehensive AI-powered approach, delivering ultra-reliable, high-speed wireless, precise spatial awareness, and intelligent network automation, is not a future vision but a current reality, with thousands of customers already using Cisco’s AI-powered network solutions.


Smarter Switching for AI with Cisco Enterprise Networking

Event: AI Infrastructure Field Day 4

Appearance: Cisco Enterprise Networking Presents at AI Infrastructure Field Day

Company: Cisco

Video Links:

Personnel: Kenny Lei

The foundational goal of campus switching (providing connectivity to users and endpoints) remains unchanged, but the ecosystem it serves is undergoing rapid transformation driven by evolving applications and devices. Kenny Lei, a Technical Marketing Engineer at Cisco, highlighted the pervasive influence of AI tools like ChatGPT and GitHub Copilot, the surging adoption of Wi-Fi 7 for its increased bandwidth and user density, and the emerging security challenges posed by quantum computing. These trends necessitate a campus network capable of handling dramatically increased, often symmetric, data traffic, with higher performance, lower latency, and robust security.

To address these demands, Cisco has introduced its new “Smart Switch” series, featuring the Catalyst 9350 for access layers and the Catalyst 9610 for aggregation. The Catalyst 9350 offers high Power over Ethernet (90W) and 10Gbps copper ports, complemented by multiple 100Gbps uplinks, significantly reducing oversubscription and ensuring optimal performance for latency-sensitive AI applications. The modular Catalyst 9610, with up to 25 Terabits of performance and support for hundreds of 100Gbps ports (with future 400Gbps capabilities), serves as a high-capacity core. Both platforms are powered by Cisco Silicon One A6 ASICs, which use a virtual output queuing (VOQ) architecture to prevent head-of-line blocking and support up to seven queues for granular traffic prioritization. This intelligent design, coupled with a hybrid buffer memory system, ensures that latency-sensitive traffic is processed swiftly while bulk data transfers avoid packet drops even under congestion.

Cisco emphasizes that security is embedded in the network fabric, featuring Trust Anchor Modules (TAMs) for hardware and software integrity, IPsec/MACsec for secure transport, and a zero-trust model powered by Security Group Tags (SGTs) and the Identity Services Engine (ISE) for continuous authentication and policy enforcement. The new switches also enhance visibility and policy management through HCAM (a combination of TCAM and SRAM), enabling efficient NetFlows and ACLs while significantly reducing resource consumption. Furthermore, the enhanced CPU and memory on these smart switches allow for hosting AI workloads closer to the edge, fostering distributed intelligence and faster processing. Operational efficiency is boosted by innovations such as the eXpress Forwarding Software Upgrade (XFSU), which minimizes outage time during updates by separating the control and data planes and offloading critical processes. Cisco also integrates AI into network operations through an AI Assistant in the Meraki dashboard, streamlining day-zero, day-one, and day-N tasks from inventory management and troubleshooting to compliance checks, ensuring a high-performance, secure, and quantum-ready network infrastructure for the AI era.


Secure Routing for AI with Cisco Enterprise Networking

Event: AI Infrastructure Field Day 4

Appearance: Cisco Enterprise Networking Presents at AI Infrastructure Field Day

Company: Cisco

Video Links:

Personnel: Rahul Sagi

Secure Routing with Cisco Enterprise Networking tackles the increasing complexity, user experience demands, and security requirements of modern WAN networks, especially with the advent of AI branches. Rahul Sagi introduced Cisco Secure Routers, launching in 2025, designed to converge Cisco’s best-in-class networking with advanced security in a single product. This convergence is enabled by a new Secure Networking Processor (SNP) that delivers the high throughput and capacity essential for future AI applications. These routers offer comprehensive on-box security capabilities, including a full stack of hybrid mesh firewalls with IPS/IDS, URL filtering, and AMP Threat Grid, while also supporting cloud security options for direct Internet access (DIA) use cases.

The Secure Networking Processor, an ARM-based chip with Cisco IP, is central to these innovations, enabling inline cryptographic acceleration and a natively integrated Next-Generation Firewall (NGFW) stack for superior performance. Cisco highlights significant improvements, including up to three times the IPsec performance and high security efficacy, with threat protection throughput reaching up to 11 Gbps even with all security features enabled. Addressing the impending threat of quantum computing, the new portfolio integrates Post-Quantum Cryptography (PQC) algorithms, specifically ML-CEM for key exchanges in WAN transport (IPsec and MACsec) and quantum-resistant secure boot, ensuring networks are future-proofed against quantum attacks by 2030, a critical concern for sectors like public, healthcare, retail, and finance. The secure routers also boast improved power efficiency and increased WAN interface capacities, supporting up to 100 Gbps, to handle the escalating I/O demands of AI-driven environments. Furthermore, some platforms include a dedicated AI/ML engine for local inferencing to enhance network performance in future software releases, and native zero-trust principles are embedded throughout the system.

Beyond hardware, Cisco is leveraging AI to simplify WAN operations, offering “AI for networking” tools for administrators. This includes “Branch as Code” with Cisco Validated Designs and integration into CI/CD pipelines for automated, scalable deployments across hundreds of sites. The AI Assistant in management solutions such as Catalyst SD-WAN Manager and the Meraki dashboard streamlines configuration and troubleshooting. Specific AI-powered features include Predictive Path Recommendations, which analyze historical network behavior to suggest optimal transport paths for applications at specific times, and Bandwidth Forecasting, which helps predict and plan for circuit upgrades. Anomaly Detection continuously monitors network attributes such as round-trip time, jitter, and loss to proactively alert administrators to anomalous behavior, reducing troubleshooting time. These combined efforts aim to deliver AI-ready networking products, simplify WAN operations with intelligent tools, and reduce risk across all layers with robust, future-proof security controls.


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