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 give attention to evolving knowledge heart community infrastructure for supporting AI/ML workloads, whereas the following weblog will focus on the Cisco compute technique and improvements for mainstreaming AI.
As mentioned partly one of many weblog collection, Synthetic intelligence (AI) and machine studying (ML) have not too long ago skilled a steep funding trajectory not too long ago, catapulted by generative AI. This has opened up new alternatives to ship actionable insights and real-world problem-solving capabilities.
Generative AI requires a big quantity of processing energy and better networking efficiency to ship outcomes quickly. Hyperscalers have led the AI revolution with mass-scale infrastructure utilizing hundreds of graphics processing models (GPUs) to course of petabytes of information for AI workloads, similar 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 giant knowledge units, it’s essential to distribute them throughout a number of GPUs in an AI/ML cluster. This helps stability the load via parallel processing and ship high-quality outcomes shortly. 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’s critical to have a strong community to ship real-time insights to a lot of end-users.
Why Ethernet?
The inspiration for many networks in the present day is Ethernet, which has developed from use in 10Mbps LANs to WANs with 400GbE ports. Ethernet’s adaptability has allowed it to scale and evolve to satisfy new calls for, together with these of AI. It has efficiently overcome challenges similar to scaling previous DS1, DS3, and SONET speeds, whereas sustaining the standard of service for voice and video visitors. This adaptability and resilience have allowed Ethernet to outlast options similar to Token Ring, ATM, and body relay.
To assist enhance throughput and decrease compute and storage visitors latency, the distant direct reminiscence entry (RDMA) over Converged Ethernet (RoCE) community protocol is used to assist distant entry to reminiscence on a distant host with out CPU involvement. Ethernet materials with RoCEv2 protocol assist are optimized for AI/ML clusters with broadly adopted standards-based expertise, 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.
Based on the Dell’oro Group, AI networks will act as a catalyst to speed up the transition to larger speeds. Market demand from “Tier 2/3 and huge enterprises are forecast to be vital, approaching $10 B over the following 5 years,” and they’re anticipated to desire Ethernet.
Why Cisco AI infrastructure?
Now we have made vital investments in our knowledge heart networking portfolio for AI infrastructure throughout platforms, software program, silicon, and optics. This embrace Cisco Nexus 9000 Sequence switches, Cisco 8000 Sequence Routers, Cisco Silicon One, community working programs (NOSs), administration, and Cisco Optics (see Determine 1).
Determine 1. Cisco AI/ML knowledge heart infrastructure options
This portfolio is designed for knowledge heart Ethernet networks transporting AI/ML workloads, similar to operating inference fashions on Cisco unified computing system (UCS) servers. Prospects want decisions, which is why we’re offering flexibility with completely different choices.
Cisco Nexus 9000 Sequence switches are built-in options that ship high-throughput and supply congestion administration to assist scale back latency and visitors drops throughout AI/ML clusters. Cisco Nexus Dashboard helps view and analyze telemetry, and may also help shortly 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 satisfy the important thing necessities, with a blueprint for community infrastructure and operations.
Cisco 8000 Sequence Routers assist disaggregation for knowledge heart use instances requiring high-capacity open platforms utilizing Ethernet—similar to AI/ML clusters within the hyperscaler section. For these use instances, the NOS on the Cisco 8000 Sequence Routers could 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 Sequence Routers additionally assist IOS XR software program for different knowledge heart routing use instances, together with super-spine, knowledge heart 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 absolutely 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 prospects with their budgetary and sustainability targets via {hardware} and software program innovation. For instance, system scalability and Cisco Silicon One energy effectivity assist scale back the quantity of assets required for AI/ML interconnects. Prospects can entry community visibility into precise utilization of energy and carbon footprint similar to KWh, value, and CO2 emissions by way of Cisco Nexus Dashboard Insights.
With this AI/ML infrastructure options portfolio, Cisco helps prospects ship high-quality experiences for his or her end-users with quick insights, via sustainable, high-performance AI/ML Ethernet materials which might be clever and operationally environment friendly.
Is my knowledge heart able to assist AI/ML functions?
Information heart architectures must be designed correctly to assist AI/ML workloads. To assist prospects accomplish this objective, we utilized our in depth knowledge heart networking expertise to create a knowledge heart networking blueprint for AI/ML functions (see Determine 2), which discusses the best way to:
- Construct automated, scalable, low-latency, Ethernet networks with assist for lossless transport, utilizing congestion administration mechanisms similar to express congestion notification (ECN) and precedence stream management (PFC) to assist RoCEv2 transport for GPU memory-to-memory switch of knowledge.
- Design a non-blocking community to additional enhance efficiency and allow sooner completion charges of AI/ML jobs.
- Shortly automate configuration of the AI/ML community material, together with congestion administration parameters for quality-of-service (QoS) management.
- Obtain completely different ranges of visibility into the community via telemetry to assist shortly troubleshoot points and enhance transport efficiency, similar to real-time congestion statistics that may assist determine methods to tune the community.
- Leverage the Cisco Validated Design for Information Middle Community Blueprint for AI/ML, which incorporates configuration examples as greatest practices on constructing AI/ML infrastructure.
Determine 2. Cisco AI knowledge heart networking blueprint
How do I get began?
Evolving to a next-gen knowledge heart might not be simple for all prospects, which is why Cisco is collaborating with NVIDIA® to ship AI infrastructure options for the info heart 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 embrace:
- Cisco knowledge heart Ethernet infrastructure: Cisco Nexus 9000 Sequence switches and Cisco 8000 Sequence Routers, together with Cisco Optics and Cisco Silicon One, for high-performance AI/ML knowledge heart community materials that management latency and loss to allow higher experiences with well timed outcomes for AI/ML workloads
- Cisco Compute: M7 era of UCS rack and blade servers allow optimum compute efficiency throughout a broad array of AI and data-intensive workloads within the knowledge heart 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: Objective-built community accelerators for contemporary AI workloads, offering high-performance community connectivity between GPU servers
- NVIDIA BlueField-3 knowledge processing models (DPUs): Cloud infrastructure processors for offloading, accelerating, and isolating software-defined networking, storage, safety, and administration capabilities, considerably enhancing knowledge heart efficiency, effectivity, and safety
- NVIDIA AI Enterprise: Software program frameworks, pretrained fashions, and growth instruments, in addition to new NVIDIA NIM microservices, for safer, steady, 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 international ecosystem of companions may also help advise, assist, and information prospects in evolving their knowledge facilities to assist AI/ML functions
Main the best way
Cisco’s collaboration with NVIDIA goes past promoting present options via Cisco sellers/companions, as extra technological integrations are deliberate. By these improvements and dealing with NVIDIA, we’re serving to enterprise, public sector, service supplier and web-scale prospects on the info heart journeys to totally enabled AI/ML infrastructures, together with for coaching and inference fashions.
We’ll be at NVIDIA GTC, a worldwide AI convention operating March 18–21, so go to us at Sales space #1535 to be taught 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.
Discover out extra from the press launch:
Share:
