Value of AI at the Edge
Often debated alongside latency, the desire to optimize this heavy financial metric is a primary reason enterprises are shifting their agentic workflows and compute-intensive AI tasks out of the public cloud and into on-premises data centers.
Cost
It is the fundamental building block of text processed by a large language model—roughly equivalent to three-quarters of an English word—and serves as the standard unit for measuring both a model's context limit and its computing costs.
Token
This collaborative architecture built with Nvidia provides enterprises with the complete networking, compute, and software blueprint needed to securely deploy generative AI workloads on-premises.
Secure AI Factory
Providing a massive supply chain and performance advantage, this revolutionary architecture is the industry's first to unify both routing and switching capabilities onto a single programmable chip family, powering everything from the enterprise campus to the AI data center.Pointed Hood
Silicon One
Moving AI processing away from the cloud and directly onto local devices is primarily done to eliminate this specific time delay in data transmission.
Latency
The maximum amount of text (measured in tokens) an AI can “remember” and consider in a single conversation. If a prompt exceeds the window, the AI forgets older parts of the conversation.
Context Window
Designed to turn the network's outer boundary into its "front line," this integrated 3U platform converges compute, networking, storage, and zero-trust security to run agentic AI workloads and real-time inferencing directly where the data is created.
Unified Edge
Unveiled as the foundation for the AgenticOps vision, this unified platform provides a single "pane of glass" where human operators and AI agents collaborate to manage, monitor, and defend an enterprise's entire IT infrastructure.
Cloud Control
Keeping AI infrastructure on-premises is often mandated by this principle, ensuring an organization's sensitive information remains strictly under its own jurisdictional and geographic control.
Data Sovereignty
This refers to the macroeconomic models governing the supply, demand, and financial viability of running autonomous agents at scale.
Tokenomics
Often deployed to rapidly scale enterprise data centers, these modular, self-contained units bundle together the necessary compute, storage, and networking resources to power heavy AI workloads.
AI Pods
Launched to serve as the unified "front door" for Customer Experience, this AI-powered intelligence layer consolidates support, telemetry, and professional services to shift enterprise IT teams from reactive firefighting to predictive, proactive resilience.
Cisco IQ
Processing AI workloads locally helps conserve this critical measure of network capacity, which can easily be exhausted if massive amounts of raw data must be constantly streamed to the cloud.
Bandwidth
Built on an open standard championed by Anthropic, these back-end applications act as secure bridges, allowing AI models to hook directly into local files, databases, and enterprise tools to fetch real-time context.
MCP Servers
Launched to safeguard the enterprise AI transformation, this comprehensive security solution uses algorithmic red-teaming to validate models and deploys runtime guardrails to stop data leakage, prompt injections, and rogue agentic workflows.
AI Defense
Delivering a massive advantage in runtime resilience, this feature acts as a digital immune system by using eBPF technology to instantly deploy security shields onto live network infrastructure, blocking vulnerabilities without requiring a patch, reboot, or maintenance window.
Live Protect