Cisco IT designed AI-ready infrastructure with Cisco compute, best-in-class NVIDIA GPUs, and Cisco networking that helps AI mannequin coaching and inferencing throughout dozens of use instances for Cisco product and engineering groups.
It’s no secret that the strain to implement AI throughout the enterprise presents challenges for IT groups. It challenges us to deploy new know-how quicker than ever earlier than and rethink how knowledge facilities are constructed to satisfy growing calls for throughout compute, networking, and storage. Whereas the tempo of innovation and enterprise development is exhilarating, it might additionally really feel daunting.
How do you shortly construct the info middle infrastructure wanted to energy AI workloads and sustain with crucial enterprise wants? That is precisely what our group, Cisco IT, was dealing with.
The ask from the enterprise
We had been approached by a product group that wanted a approach to run AI workloads which could be used to develop and check new AI capabilities for Cisco merchandise. It would ultimately help mannequin coaching and inferencing for a number of groups and dozens of use instances throughout the enterprise. And they wanted it completed shortly. want for the product groups to get improvements to our clients as shortly as doable, we needed to ship the new atmosphere in simply three months.
The know-how necessities
We started by mapping out the necessities for the brand new AI infrastructure. A non-blocking, lossless community was important with the AI compute cloth to make sure dependable, predictable, and high-performance knowledge transmission throughout the AI cluster. Ethernet was the first-class selection. Different necessities included:
- Clever buffering, low latency: Like every good knowledge middle, these are important for sustaining easy knowledge circulation and minimizing delays, in addition to enhancing the responsiveness of the AI cloth.
- Dynamic congestion avoidance for numerous workloads: AI workloads can differ considerably of their calls for on community and compute sources. Dynamic congestion avoidance would make sure that sources had been allotted effectively, stop efficiency degradation throughout peak utilization, keep constant service ranges, and forestall bottlenecks that might disrupt operations.
- Devoted front-end and back-end networks, non-blocking cloth: With a objective to construct scalable infrastructure, a non-blocking cloth would guarantee enough bandwidth for knowledge to circulation freely, in addition to allow a high-speed knowledge switch — which is essential for dealing with massive knowledge volumes typical with AI functions. By segregating our front-end and back-end networks, we might improve safety, efficiency, and reliability.
- Automation for Day 0 to Day 2 operations: From the day we deployed, configured, and tackled ongoing administration, we needed to scale back any handbook intervention to maintain processes fast and decrease human error.
- Telemetry and visibility: Collectively, these capabilities would supply insights into system efficiency and well being, which might permit for proactive administration and troubleshooting.
The plan – with just a few challenges to beat
With the necessities in place, we started determining the place the cluster may very well be constructed. The prevailing knowledge middle services weren’t designed to help AI workloads. We knew that constructing from scratch with a full knowledge middle refresh would take 18-24 months – which was not an possibility. We would have liked to ship an operational AI infrastructure in a matter of weeks, so we leveraged an current facility with minor adjustments to cabling and system distribution to accommodate.
Our subsequent issues had been across the knowledge getting used to coach fashions. Since a few of that knowledge wouldn’t be saved regionally in the identical facility as our AI infrastructure, we determined to copy knowledge from different knowledge facilities into our AI infrastructure storage methods to keep away from efficiency points associated to community latency. Our community group had to make sure enough community capability to deal with this knowledge replication into the AI infrastructure.
Now, attending to the precise infrastructure. We designed the guts of the AI infrastructure with Cisco compute, best-in-class GPUs from NVIDIA, and Cisco networking. On the networking aspect, we constructed a front-end ethernet community and back-end lossless ethernet community. With this mannequin, we had been assured that we might shortly deploy superior AI capabilities in any atmosphere and proceed so as to add them as we introduced extra services on-line.
Merchandise:
Supporting a rising atmosphere
After making the preliminary infrastructure accessible, the enterprise added extra use instances every week and we added extra AI clusters to help them. We would have liked a approach to make all of it simpler to handle, together with managing the swap configurations and monitoring for packet loss. We used Cisco Nexus Dashboard, which dramatically streamlined operations and ensured we might develop and scale for the long run. We had been already utilizing it in different elements of our knowledge middle operations, so it was straightforward to increase it to our AI infrastructure and didn’t require the group to study an extra software.
The outcomes
Our group was capable of transfer quick and overcome a number of hurdles in designing the answer. We had been capable of design and deploy the backend of the AI cloth in beneath three hours and deploy your entire AI cluster and materials in 3 months, which was 80% quicker than the choice rebuild.
Immediately, the atmosphere helps greater than 25 use instances throughout the enterprise, with extra added every week. This consists of:
- Webex Audio: Bettering codec growth for noise cancellation and decrease bandwidth knowledge prediction
- Webex Video: Mannequin coaching for background alternative, gesture recognition, and face landmarks
- Customized LLM coaching for cybersecurity merchandise and capabilities
Not solely had been we capable of help the wants of the enterprise immediately, however we’re designing how our knowledge facilities must evolve for the long run. We’re actively constructing out extra clusters and can share extra particulars on our journey in future blogs. The modularity and adaptability of Cisco’s networking, compute, and safety offers us confidence that we will hold scaling with the enterprise.
Further sources:
Share:
