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 stress to implement AI throughout the enterprise presents challenges for IT groups. It challenges us to deploy new expertise quicker than ever earlier than and rethink how information facilities are constructed to fulfill growing calls for throughout compute, networking, and storage. Whereas the tempo of innovation and enterprise development is exhilarating, it may additionally really feel daunting.
How do you shortly construct the information middle 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 technique to run AI workloads which can be used to develop and take a look at new AI capabilities for Cisco merchandise. It would ultimately assist mannequin coaching and inferencing for a number of groups and dozens of use instances throughout the enterprise. And they wanted it performed shortly. want for the product groups to get improvements to our clients as shortly as attainable, we needed to ship the new surroundings 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 information transmission inside the AI cluster. Ethernet was the first-class selection. Different necessities included:
- Clever buffering, low latency: Like all good information middle, these are important for sustaining easy information 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 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 aim to construct scalable infrastructure, a non-blocking cloth would guarantee enough bandwidth for information to circulation freely, in addition to allow a high-speed information switch — which is essential for dealing with massive information volumes typical with AI purposes. By segregating our front-end and back-end networks, we may 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 cut back any guide intervention to maintain processes fast and reduce 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 just a few challenges to beat
With the necessities in place, we started determining the place the cluster could possibly be constructed. The prevailing information middle amenities weren’t designed to assist AI workloads. We knew that constructing from scratch with a full information middle refresh would take 18-24 months – which was not an choice. We wanted to ship an operational AI infrastructure in a matter of weeks, so we leveraged an current facility with minor modifications to cabling and system distribution to accommodate.
Our subsequent issues have been across the information getting used to coach fashions. Since a few of that information wouldn’t be saved regionally in the identical facility as our AI infrastructure, we determined to copy information from different information facilities into our AI infrastructure storage techniques to keep away from efficiency points associated to community latency. Our community group had to make sure enough community capability to deal with this information replication into the AI infrastructure.
Now, attending to the precise infrastructure. We designed the center 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 may shortly deploy superior AI capabilities in any surroundings and proceed so as to add them as we introduced extra amenities on-line.
Merchandise:
Supporting a rising surroundings
After making the preliminary infrastructure accessible, the enterprise added extra use instances every week and we added further AI clusters to assist them. We wanted a technique 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 may develop and scale for the longer term. We have been already utilizing it in different elements of our information middle operations, so it was straightforward to increase it to our AI infrastructure and didn’t require the group to be taught an extra software.
The outcomes
Our group was in a position to transfer quick and overcome a number of hurdles in designing the answer. We have been in a position to design and deploy the backend of the AI cloth in beneath three hours and deploy the complete AI cluster and materials in 3 months, which was 80% quicker than the choice rebuild.
At the moment, the surroundings helps greater than 25 use instances throughout the enterprise, with extra added every week. This consists of:
- Webex Audio: Enhancing codec growth for noise cancellation and decrease bandwidth information 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 in a position to assist the wants of the enterprise as we speak, however we’re designing how our information facilities must evolve for the longer term. 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 offers us confidence that we are able to maintain scaling with the enterprise.
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