AI infrastructure occasionally. The subtitles celebrate the growing number of GPUs and scaling from Watts to Megawatty, but inside the company success is affected by something heavier: getting data, scale, security and operations to work in real production with real business and operational restrictions.
You can see the gap in the corporate infrastructure of the company AI. McKinSey Global Institute Institute by AI could generate up to $ 4.4 trillion into business profits, yet the register to Cisco AI, which is a read index, only $ 13 claims to be ready to support AI in AI and most AI initiative fails but fails But it fails, but fails, but fails, but fails, but fails but fails, but fails, but it is not in the course of the background.
Gap of business infrastructure AI
Most centers of production data centers have never been designed for GPU-design, data, multi-stage AI. Training of the model, fine -tuning and conclusion introduces new tension in IT environments. Here are some of these voltages and their resulting infrastructure requirements.
- GPUs that are fed data they need to process workload AI require high-performance, low latency, east-west operation on the scale.
- Heterogeneous piles must be supported, mixing bare metal, virtual machines and working load Kubernetes.
- Massive data from huge data sets requires cost -effective storage performance, optimized for location and movement.
- The accurate control of operational overheads must include framework tools across computer, textile and safety domains.
- Risk attitude must include protection of regulated data, intellectual property and model integrity.
Customers say that the most difficult part is not getting up an AI infrastructure, but operates AI as a reliable service in the face of these challenges.
Cisco’s Ai Focus
At the beginning of this year, Cisco Secure AI Factory with NVIDIA, a scalable, high -performance, safe AI infrastructure developed by Cisco, Nvidia and other strategic partners. It combines verified architecture, automated operations, ecosystem integration and built -in security.
AI below are how many customers start. You can consider them modular building blocks-pre-infrastructure units that calculate the volume, textiles, storage integration, software and security, so teams can quickly stop AI applications and methodically grow them. For organizations that go to the laboratory into production, Cisco AI provides the underwent, acceptable path.
A new option in Cisco AI POP is Cisco Nexus Hyperfabric AI-A on turnkey, cloud infrastructure AI for multiple clusters, multi-teenage ai. For customers who are trying to measure multiple boundaries Domas or Damains Gold, it provides hyperfabric AI model for deployment based on AI model.
Five operational objectives control the optimization of business infrastructure
- Time curriculum: pre -verified reports and automation of life cycle, which are performed by Cisco Intersight, Dashboard Cisco Nexus and AI deployment hyperfabric cycles, shorten the path from preparing the output model.
- Performance in scale: GPU-optimized Cisco UCS and non-blocking servers, low latency Nexus fabrics maintain expensive fed accelerators.
- Unified Operations: Unified Management and Observability – Use of forces across computational and layers of workload. When you start inference or grow for distributed training, the operating model remains the same.
- Responsible use of data anywhere: Integration with NetAPP similar partners, clean and huge data high width, secure data processing and pipes without customer lock.
- Built -in security and trust: Cisco AI defense controls, Cisco Hypershield and ISVAVALLENT EBPF help protect data, models and running behavior, which is crucial to the regulated industries.
Actual deployment, critical results
Global customers in health, finance and public research have already used Cisco AI architects in their production ostuters:
- Run a secure Inference Genai next to the correct data
- Domain -tuning models without moving sensitive mental property
- A rupture of workload across AI pods and devices as a scale of projects
The readiness of the infrastructure AI
Ask your team:
- Can we provide GPU capacity in days, not a quarter?
- Is our East-West network designed to saturate the GPU?
- Do we have politics, telemetry and security across data, models and running surroundings?
- Can we now support inferences and add training training without reworking?
- Are the operations united or stitched from point tools?
If any of them are not “not yet”, it is a modular approach, such as AI, quick readiness for AI infrastructure.
Built for AI. Ready for what will happen next.
The success of the company AI depends on the infrastructure, which is intelligent, safe and simple. With the modular AI POP and the expansion of production, when you need them, Cisco helps organizations to turn AI ambitions into execution-and-to-be reconstruction from scratch.
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