That is half two of a multi-part weblog collection on AI. Half one, Why 2024 is the 12 months of AI for Networking, mentioned Cisco’s AI networking imaginative and prescient and technique. This weblog will deal with evolving knowledge middle community infrastructure for supporting AI/ML workloads, whereas the subsequent weblog will talk about the Cisco compute technique and improvements for mainstreaming AI.

As mentioned partially one of many weblog collection, Synthetic intelligence (AI) and machine studying (ML) have lately skilled a steep funding trajectory lately, catapulted by generative AI. This has opened up new alternatives to ship actionable insights and real-world problem-solving capabilities.

Generative AI requires a major quantity of processing energy and better networking efficiency to ship outcomes quickly. Hyperscalers have led the AI revolution with mass-scale infrastructure utilizing 1000’s of graphics processing items (GPUs) to course of petabytes of information for AI workloads, comparable to coaching fashions. Many organizations, together with enterprise, public sector, service suppliers, and Tier 2 web-scalers, are exploring or beginning to use generative AI with coaching and inference fashions.

To course of AI/ML workloads or jobs that contain massive knowledge units,  it’s essential to distribute them throughout a number of GPUs in an AI/ML cluster. This helps stability the load by parallel processing and ship high-quality outcomes rapidly. To attain this, it’s important to have a high-performance community that helps non-blocking, low-latency, lossless material. With out such a community, latency or packet drops could cause studying jobs to take for much longer to finish, or could not full in any respect. Equally, when operating AI inferencing in edge knowledge facilities, it is important to have a sturdy community to ship real-time insights to numerous end-users.

Why Ethernet?

The muse for many networks immediately is Ethernet, which has advanced from use in 10Mbps LANs to WANs with 400GbE ports. Ethernet’s adaptability has allowed it to scale and evolve to fulfill new calls for, together with these of AI. It has efficiently overcome challenges comparable to scaling previous DS1, DS3, and SONET speeds, whereas sustaining the standard of service for voice and video site visitors. This adaptability and resilience have allowed Ethernet to outlast options comparable to Token Ring, ATM, and body relay.

To assist enhance throughput and decrease compute and storage site visitors latency, the distant direct reminiscence entry (RDMA) over Converged Ethernet (RoCE) community protocol is used to help distant entry to reminiscence on a distant host with out CPU involvement.  Ethernet materials with RoCEv2 protocol help are optimized for AI/ML clusters with extensively adopted standards-based know-how, simpler migration for Ethernet-based knowledge facilities, confirmed scalability at decrease cost-per-bit, and designed with superior congestion administration to assist intelligently management latency and loss.

In line with the Dell’oro Group, AI networks will act as a catalyst to speed up the transition to increased speeds. Market demand from “Tier 2/3 and huge enterprises are forecast to be vital, approaching $10 B over the subsequent 5 years,” and they’re anticipated to want Ethernet.

Why Cisco AI infrastructure?

We’ve got made vital investments in our knowledge middle networking portfolio for AI infrastructure throughout platforms, software program, silicon, and optics. This embody Cisco Nexus 9000 Collection switches, Cisco 8000 Collection Routers, Cisco Silicon One, community working programs (NOSs), administration, and Cisco Optics (see Determine 1).

Determine 1. Cisco AI/ML knowledge middle infrastructure options

This portfolio is designed for knowledge middle Ethernet networks transporting AI/ML workloads, comparable to operating inference fashions on Cisco unified computing system (UCS) servers. Clients want decisions, which is why we’re offering flexibility with totally different choices.

Cisco Nexus 9000 Collection switches are built-in options that ship high-throughput and supply congestion administration to assist cut back latency and site visitors drops throughout AI/ML clusters. Cisco Nexus Dashboard helps view and analyze telemetry, and may help rapidly configure AI/ML networks with automation, together with congestion parameters, ports, and including leaf/backbone switches. This answer offers AI/ML prepared networks for purchasers to fulfill the important thing necessities, with a blueprint for community infrastructure and operations.

Cisco 8000 Collection Routers help disaggregation for knowledge middle use instances requiring high-capacity open platforms utilizing Ethernet—comparable to AI/ML clusters within the hyperscaler phase. For these use instances, the NOS on the Cisco 8000 Collection Routers may be third-party or Software program for Open Networking within the Cloud (SONiC), which is community-supported and designed for purchasers needing an open-source answer. Cisco 8000 Collection Routers additionally help IOS XR software program for different knowledge middle routing use instances, together with super-spine, knowledge middle interconnect, and WAN.

Our options portfolio leverages Cisco Silicon One, which is Cisco chip innovation primarily based on a unified structure that delivers high-performance with useful resource effectivity. Cisco Silicon One is optimized for latency management with AI/ML clusters utilizing Ethernet, telemetry-assisted Ethernet, or totally scheduled material. Cisco Optics allow excessive throughput on Cisco routers and switches, scaling as much as 800G per port to assist meet the calls for of AI infrastructure.

We’re additionally serving to clients with their budgetary and sustainability targets by {hardware} and software program innovation. For instance, system scalability and Cisco Silicon One energy effectivity assist cut back the quantity of sources required for AI/ML interconnects. Clients can entry community visibility into precise utilization of energy and carbon footprint comparable to KWh, value, and CO2 emissions through Cisco Nexus Dashboard Insights.

With this AI/ML infrastructure options portfolio, Cisco helps clients ship high-quality experiences for his or her end-users with quick insights, by sustainable, high-performance AI/ML Ethernet materials which might be clever and operationally environment friendly.

Is my knowledge middle able to help AI/ML functions?

Knowledge middle architectures should be designed correctly to help AI/ML workloads. To assist clients accomplish this purpose, we utilized our in depth knowledge middle networking expertise to create a knowledge middle networking blueprint for AI/ML functions (see Determine 2), which discusses the best way to:

• Construct automated, scalable, low-latency, Ethernet networks with help for lossless transport, utilizing congestion administration mechanisms comparable to specific congestion notification (ECN) and precedence stream management (PFC) to help RoCEv2 transport for GPU memory-to-memory switch of data.
• Design a non-blocking community to additional enhance efficiency and allow sooner completion charges of AI/ML jobs.
• Rapidly automate configuration of the AI/ML community material, together with congestion administration parameters for quality-of-service (QoS) management.
• Obtain totally different ranges of visibility into the community by telemetry to assist rapidly troubleshoot points and enhance transport efficiency, comparable to real-time congestion statistics that may assist establish methods to tune the community.
• Leverage the Cisco Validated Design for Knowledge Middle Community Blueprint for AI/ML, which incorporates configuration examples as greatest practices on constructing AI/ML infrastructure.

Determine 2. Cisco AI knowledge middle networking blueprint

How do I get began?

Evolving to a next-gen knowledge middle is probably not easy for all clients, which is why Cisco is collaborating with NVIDIA® to ship AI infrastructure options for the info middle which might be simple to deploy and handle by enterprises, public sector organizations, and repair suppliers (see Determine 3).

Determine 3. Cisco/NVIDIA partnership

By combining industry-leading applied sciences from Cisco and NVIDIA, built-in options embody:

• Cisco knowledge middle Ethernet infrastructure: Cisco Nexus 9000 Collection switches and Cisco 8000 Collection Routers, together with Cisco Optics and Cisco Silicon One, for high-performance AI/ML knowledge middle community materials that management latency and loss to allow higher experiences with well timed outcomes for AI/ML workloads
• Cisco Compute: M7 technology of UCS rack and blade servers allow optimum compute efficiency throughout a broad array of AI and data-intensive workloads within the knowledge middle and on the edge
• Infrastructure administration and operations: Cisco Networking Cloud with Cisco Nexus Dashboard and Cisco Intersight, digital expertise monitoring with Cisco ThousandEyes, and cross-domain telemetry analytics with the Cisco Observability Platform
• NVIDIA Tensor Core GPUs: Newest-generation processors optimized for AI/ML workloads, utilized in UCS rack and blade servers
• NVIDIA BlueField-3 SuperNICs: Function-built community accelerators for contemporary AI workloads, offering high-performance community connectivity between GPU servers
• NVIDIA BlueField-3 knowledge processing items (DPUs): Cloud infrastructure processors for offloading, accelerating, and isolating software-defined networking, storage, safety, and administration features, considerably enhancing knowledge middle efficiency, effectivity, and safety
• NVIDIA AI Enterprise: Software program frameworks, pretrained fashions, and improvement instruments, in addition to new NVIDIA NIM microservices, for safer, secure, and supported manufacturing AI
• Cisco Validated Designs: Validated reference architectures designed assist to simplify deployment and administration of AI clusters at any scale in a variety of use instances spanning virtualized and containerized environments, with each converged and hyperconverged choices
• Companions: Cisco’s world ecosystem of companions may help advise, help, and information clients in evolving their knowledge facilities to help AI/ML functions

Main the way in which

Cisco’s collaboration with NVIDIA goes past promoting present options by Cisco sellers/companions, as extra technological integrations are deliberate. By way of these improvements and dealing with NVIDIA, we’re serving to enterprise, public sector, service supplier and web-scale clients on the info middle journeys to completely enabled AI/ML infrastructures, together with for coaching and inference fashions.

We’ll be at NVIDIA GTC, a world AI convention operating March 18–21, so go to us at Sales space #1535 to study extra.

Within the subsequent weblog of this collection, Jeremy Foster, SVP/GM, Cisco Compute, will focus on the Cisco Compute technique and improvements for mainstreaming AI.

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