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 sooner 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 will probably additionally really feel daunting.
How do you shortly construct the information middle infrastructure wanted to energy AI workloads and sustain with vital 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 solution to run AI workloads which could be used to develop and take a look at new AI capabilities for Cisco merchandise. It would finally assist mannequin coaching and inferencing for a number of groups and dozens of use instances throughout the enterprise. And they wanted it finished shortly. want for the product groups to get improvements to our prospects as shortly as doable, 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 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 all good information middle, these are important for sustaining clean information circulate and minimizing delays, in addition to enhancing the responsiveness of the AI material.
- Dynamic congestion avoidance for varied workloads: AI workloads can differ considerably of their calls for on community and compute sources. Dynamic congestion avoidance would be sure that sources had 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 material: With a purpose to construct scalable infrastructure, a non-blocking material would guarantee enough bandwidth for information to circulate freely, in addition to allow a high-speed information switch — which is essential for dealing with giant information volumes typical with AI purposes. 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 supply insights into system efficiency and well being, which might enable 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 present information middle services 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 possibility. 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 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 duplicate information from different information 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 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 might shortly deploy superior AI capabilities in any surroundings and proceed so as to add them as we introduced extra services on-line.
Merchandise:
Supporting a rising surroundings
After making the preliminary infrastructure obtainable, the enterprise added extra use instances every week and we added extra AI clusters to assist them. We wanted a solution 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 components of our information middle operations, so it was straightforward to increase it to our AI infrastructure and didn’t require the group to study a further instrument.
The outcomes
Our group 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 the whole AI cluster and materials in 3 months, which was 80% sooner than the choice rebuild.
At this time, the surroundings helps greater than 25 use instances throughout the enterprise, with extra added every week. This contains:
- Webex Audio: Bettering codec improvement for noise cancellation and decrease bandwidth information prediction
- Webex Video: Mannequin coaching for background substitute, gesture recognition, and face landmarks
- Customized LLM coaching for cybersecurity merchandise and capabilities
Not solely had been we in a position to assist the wants of the enterprise at this time, 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 suppleness of Cisco’s networking, compute, and safety provides us confidence that we are able to preserve scaling with the enterprise.
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