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 information facilities are constructed to satisfy rising calls for throughout compute, networking, and storage. Whereas the tempo of innovation and enterprise development is exhilarating, it may possibly 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 workforce, Cisco IT, was going through.
The ask from the enterprise
We had been approached by a product workforce 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 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 setting 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 material 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 every good information heart, these are important for sustaining easy information movement and minimizing delays, in addition to enhancing the responsiveness of the AI material.
- Dynamic congestion avoidance for numerous workloads: AI workloads can range considerably of their calls for on community and compute assets. Dynamic congestion avoidance would be certain that assets had been allotted effectively, forestall 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 material: With a aim to construct scalable infrastructure, a non-blocking material would guarantee adequate bandwidth for information to movement 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 scale back any handbook intervention to maintain processes fast and reduce 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 a number of challenges to beat
With the necessities in place, we started determining the place the cluster could possibly be constructed. The prevailing information heart services weren’t designed to help AI workloads. We knew that constructing from scratch with a full information heart refresh would take 18-24 months – which was not an choice. We would have liked to ship an operational AI infrastructure in a matter of weeks, so we leveraged an present facility with minor modifications to cabling and gadget distribution to accommodate.
Our subsequent issues had been across the information getting used to coach fashions. Since a few of that information wouldn’t be saved domestically in the identical facility as our AI infrastructure, we determined to copy information from different information facilities into our AI infrastructure storage programs to keep away from efficiency points associated to community latency. Our community workforce had to make sure adequate 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 had been assured that we may shortly deploy superior AI capabilities in any setting and proceed so as to add them as we introduced extra services on-line.
Merchandise:
Supporting a rising setting
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 strategy 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 may develop and scale for the long run. We had been already utilizing it in different components of our information heart operations, so it was straightforward to increase it to our AI infrastructure and didn’t require the workforce to be taught a further device.
The outcomes
Our workforce was in a position to transfer quick and overcome a number of hurdles in designing the answer. We had been in a position to design and deploy the backend of the AI material in underneath three hours and deploy your complete AI cluster and materials in 3 months, which was 80% quicker than the choice rebuild.
Right now, the setting helps greater than 25 use instances throughout the enterprise, with extra added every week. This consists of:
- Webex Audio: Bettering codec improvement 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 had been we in a position to help the wants of the enterprise in the present day, however we’re designing how our information facilities have to 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.
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