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 circumstances 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 expertise sooner than ever earlier than and rethink how knowledge facilities are constructed to fulfill rising 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 heart infrastructure wanted to energy AI workloads and sustain with crucial enterprise wants? That is precisely what our group, Cisco IT, was going through.
The ask from the enterprise
We have been approached by a product group that wanted a strategy to run AI workloads which can 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 circumstances throughout the enterprise. And they wanted it achieved shortly. want for the product groups to get improvements to our prospects as shortly as doable, we needed to ship the new setting in simply three months.
The expertise 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 inside the AI cluster. Ethernet was the first-class alternative. Different necessities included:
- Clever buffering, low latency: Like every good knowledge heart, these are important for sustaining easy knowledge movement and minimizing delays, in addition to enhancing the responsiveness of the AI cloth.
- Dynamic congestion avoidance for varied workloads: AI workloads can differ considerably of their calls for on community and compute assets. Dynamic congestion avoidance would be sure that assets have been allotted effectively, forestall efficiency degradation throughout peak utilization, keep constant service ranges, and stop bottlenecks that would 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 movement freely, in addition to allow a high-speed knowledge switch — which is essential for dealing with giant 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 guide intervention to maintain processes fast and decrease human error.
- Telemetry and visibility: Collectively, these capabilities would offer insights into system efficiency and well being, which might permit for proactive administration and troubleshooting.
The plan – with a number of challenges to beat
With the necessities in place, we started determining the place the cluster may very well be constructed. The prevailing knowledge heart amenities weren’t designed to help AI workloads. We knew that constructing from scratch with a full knowledge heart refresh would take 18-24 months – which was not an possibility. We wanted to ship an operational AI infrastructure in a matter of weeks, so we leveraged an present facility with minor adjustments to cabling and system distribution to accommodate.
Our subsequent considerations have 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 programs 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 facet, we constructed a front-end ethernet community and back-end lossless ethernet community. With this mannequin, we have been assured that we might shortly deploy superior AI capabilities in any setting and proceed so as to add them as we introduced extra amenities on-line.
Merchandise:
Supporting a rising setting
After making the preliminary infrastructure obtainable, the enterprise added extra use circumstances every week and we added further AI clusters to help them. We wanted a strategy to make all of it simpler to handle, together with managing the change 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 have been already utilizing it in different components of our knowledge heart operations, so it was straightforward to increase it to our AI infrastructure and didn’t require the group to be taught a further instrument.
The outcomes
Our group was capable of transfer quick and overcome a number of hurdles in designing the answer. We have been capable of design and deploy the backend of the AI cloth in below three hours and deploy your complete AI cluster and materials in 3 months, which was 80% sooner than the choice rebuild.
As we speak, the setting helps greater than 25 use circumstances 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 have been we capable of help the wants of the enterprise at this time, however we’re designing how our knowledge facilities have to evolve for the long run. We’re actively constructing out extra clusters and can share further particulars on our journey in future blogs. The modularity and suppleness of Cisco’s networking, compute, and safety provides us confidence that we will maintain scaling with the enterprise.
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