- Non-public information facilities: 29.5%
- Conventional public cloud: 35.4%
- GPU as a service specialists: 18.5%
- Edge compute: 16.6%
“There may be little variation from coaching to inference, however the normal sample is workloads are concentrated a bit in conventional public cloud after which hyperscalers have vital presence in personal information facilities,” McGillicuddy defined. “There may be rising curiosity round deploying AI workloads on the company edge and edge compute environments as effectively, which permits them to have workloads residing nearer to edge information within the enterprise, which helps them fight latency points and issues like that. The large key takeaway right here is that the standard enterprise goes to want to be sure that its information heart community is able to assist AI workloads.”
AI networking challenges
The recognition of AI doesn’t take away among the enterprise and technical considerations that the know-how brings to enterprise leaders.
Based on the EMA survey, enterprise considerations embody safety threat (39%), value/finances (33%), fast know-how evolution (33%), and networking crew abilities gaps (29%). Respondents additionally indicated a number of considerations round each information heart networking points and WAN points. Issues associated to information heart networking included:
- Integration between AI community and legacy networks: 43%
- Bandwidth demand: 41%
- Coordinating site visitors flows of synchronized AI workloads: 38%
- Latency: 36%
WAN points respondents shared included:
- Complexity of workload distribution throughout websites: 42%
- Latency between workloads and information at WAN edge: 39%
- Complexity of site visitors prioritization: 36%
- Community congestion: 33%
“It’s actually not low-cost to make your community AI prepared,” McGillicuddy said. “You may must spend money on lots of new switches and also you may must improve your WAN or swap distributors. You may must make some adjustments to your underlay round what sort of connectivity your AI site visitors goes over.”
Enterprise leaders intend to spend money on infrastructure to assist their AI workloads and techniques. Based on EMA, deliberate infrastructure investments embody high-speed Ethernet (800 GbE) for 75% of respondents, hyperconverged infrastructure for 56% of these polled, and SmartNICs/DPUs for 45% of surveyed community professionals.