RAG, LLMs & On-Prem AI — Why Cisco Is Running Large Language Models on Network Switches

RAG, LLMs & On-Prem AI — Why Cisco Is Running Large Language Models on Network Switches

At BRKAI-2920, Cisco demonstrated running a full RAG (Retrieval-Augmented Generation) pipeline on UCS servers — no cloud dependency, no data leaving your premises. Here's what this means for enterprise AI.

Understanding the Shift: On-Prem AI Networking and RAG

In my 25 years of training network engineers, I’ve seen technology evolve from simple routing protocols to complex, AI-driven networking solutions. Today, the buzz around large language models (LLMs) and Retrieval-Augmented Generation (RAG) is transforming how enterprises think about deploying AI. Traditionally, AI workloads, especially LLMs, relied heavily on cloud infrastructure due to their computational intensity. But recent advances, exemplified by Cisco’s latest demonstration, are turning this paradigm on its head.

So, what exactly is RAG? Think of RAG as a sophisticated way of combining AI with real-time data retrieval. Instead of just generating responses based on a static training dataset, RAG fetches relevant data from a knowledge base or database during inference, making responses more accurate and contextually relevant. This is akin to a researcher who consults multiple sources before answering a question, rather than relying solely on memory.

Implementing RAG on-premises means enterprises can now integrate advanced AI capabilities directly into their existing network infrastructure—specifically, on network switches and controllers—without depending on external cloud resources. This aligns with the broader trend of on-prem AI networking, where critical data privacy, latency, and compliance requirements make cloud-based AI less attractive.

Deep Technical Dive: How Cisco Is Doing It

During BRKAI-2920, Cisco showcased a groundbreaking approach: deploying a full RAG pipeline directly on UCS servers integrated with network switches. This deployment leverages the latest advancements in hardware acceleration, containerized AI workloads, and optimized data pipelines.

Hardware Foundations

Cisco's approach uses high-performance UCS servers equipped with AI accelerators—such as GPUs and FPGAs—capable of handling both retrieval and generation tasks simultaneously. These servers host the LLMs, which are fine-tuned for enterprise-level applications, and a retrieval system integrated with local data stores.

Pipeline Architecture

• Data Ingestion: Local data repositories (like enterprise databases, document stores, or knowledge graphs) feed data into the retrieval component.
• Retrieval Module: Uses vector search techniques, such as cosine similarity or approximate nearest neighbor algorithms, to fetch relevant data based on the user's query.
• Generation Module: The LLM, optimized for on-prem deployment, combines retrieved data with its generative capabilities to produce accurate, context-aware responses.
• Output: The response is delivered directly within the network infrastructure, ensuring low latency and high privacy.

Networking Integration

These AI workloads are integrated with Cisco's network fabric—be it Cisco IOS-XE or other platforms—allowing seamless routing, switching, and AI orchestration. This tight integration ensures AI processing is an intrinsic part of the network rather than an external add-on, paving the way for AI-enabled enterprise network infrastructure.

What the Cisco Live Data Shows

According to session BRKAI-2920, Cisco's demonstration is not just proof of concept; it’s a blueprint for future enterprise AI infrastructure. The data illustrates several key points:

• Latency Reduction: Running LLMs and RAG pipelines on local hardware reduces latency from seconds to milliseconds, critical for real-time enterprise applications like network automation and security.
• Data Privacy & Security: Keeping data on-prem ensures compliance with regulations such as GDPR, HIPAA, and industry-specific standards, eliminating risks associated with cloud data breaches.
• Cost Efficiency: Although the initial investment in hardware might be significant, operational costs decrease over time compared to cloud consumption, especially at scale.
• Scalability & Flexibility: Cisco’s architecture allows deployment of multiple AI models, customized for different enterprise needs, directly on network hardware.

Furthermore, Cisco highlighted that their on-prem LLM deployment can be integrated with existing network management and security systems, enabling proactive threat detection and automated troubleshooting—core components of modern enterprise AI.

Implications for Networking Professionals

This development signals a fundamental shift in our profession. No longer are network engineers only responsible for configuring routers and switches; they need to understand AI workloads, hardware acceleration, and data pipeline orchestration. Cisco’s demonstration underscores that AI is becoming embedded in the fabric of enterprise networks, and professionals must adapt accordingly.

From a career perspective, mastering AI & ML Deep Tech Diploma will be essential. You should be learning how to design, deploy, and troubleshoot AI workloads on network infrastructure, especially on-prem solutions that prioritize security and low latency.

What You Should Do Now

1. Build your foundational knowledge: Deepen your understanding of AI, ML, and deployment architectures through our AI & ML Deep Tech Diploma.
2. Learn about hardware acceleration: Familiarize yourself with GPU and FPGA integration in network environments. Cisco’s approach is hardware-aware, so understanding these components is critical.
3. Focus on on-prem deployment skills: Gain hands-on experience with deploying AI models locally, using containerization (Docker, Kubernetes) and orchestration tools.
4. Stay updated with Cisco innovations: Regularly review Cisco Live sessions, especially BRKAI-2920, to understand emerging best practices and architectures.
5. Engage with real-world projects: Implement small-scale RAG and LLM projects within your network labs to develop practical skills before tackling enterprise deployments.

Key Takeaways

• On-prem AI is no longer just a concept—it’s a practical reality, demonstrated vividly by Cisco’s recent deployment of RAG pipelines directly on network hardware.
• Deploying LLMs locally offers significant advantages in latency, data security, cost efficiency, and integration with existing network infrastructure.
• Understanding hardware acceleration, data pipelines, and AI orchestration is essential for modern network professionals.
• Future-proof your career by investing in AI and ML knowledge—especially in on-prem solutions tailored for enterprise networks.
• Continuous learning through Cisco Live sessions and hands-on projects will keep you ahead in this rapidly evolving field.
• On-prem AI deployment aligns with enterprise needs for privacy, compliance, and real-time responsiveness—making it a strategic imperative for modern networks.
• Networking professionals who master these new skills will be the architects of tomorrow’s intelligent, autonomous enterprise networks.

Frequently Asked Questions