DCLLM - Implementing and Operating LLM Inferencing Systems with Cisco and NVIDIA Data Center Technologies

Module 1: Large Language Model (LLM) Foundations

Objectives:

• Understand the architecture and mathematical principles of LLMs.
• Learn design trade-offs for scalability and performance.
• Explore emerging innovations in LLM development.

Topics:

• Transformer architecture, self-attention mechanism, and positional encoding.
• Types of LLMs: Encoder-only, decoder-only, and encoder-decoder.
• Training objectives: Masked language modeling (MLM), causal language modeling (CLM), and sequence-to-sequence modeling.
• Scaling laws and challenges: Parameter size, dataset size, and compute.
• Emerging architectures: Reformer, Longformer, and multi-modal LLMs.

Module 2: Data Collection and Preparation for LLM Training

Objectives:

• Understand data requirements for LLMs and their impact on performance.
• Learn techniques for sourcing, cleaning, and managing large-scale datasets.
• Explore NVIDIA and Cisco tools for efficient data handling.

Topics:

• Data sourcing: Open-source, proprietary, and domain-specific datasets.
• Preprocessing: Cleaning, deduplication, tokenization, and filtering.
• Data management: Sharding, scalable storage, and high-speed data transfer.
• Ethical considerations: Bias detection, privacy compliance, and fairness.

Module 3: Deployment of LLMs for Inferencing

Objectives:

• Deploy LLMs for production inferencing with high performance and scalability.
• Use NVIDIA TensorRT and Cisco Nexus Dashboard for optimized deployment.

Topics:

• Deployment architectures: On-premises, cloud, and hybrid.
• Optimizing inferencing with NVIDIA TensorRT: Precision calibration, layer fusion, and batching.
• Traffic management and load balancing with Cisco Nexus Dashboard.
• Exposing LLM APIs: RESTful and gRPC endpoints with security mechanisms.

Module 4: Optimizing LLM Models for Inferencing

Objectives:

• Optimize LLM inferencing pipelines for low latency and high throughput.
• Learn techniques like quantization, pruning, and model compression.

Topics:

• Quantization: FP16, INT8, and mixed precision.
• Pruning and knowledge distillation for lightweight models.
• TensorRT optimization: Dynamic batching and asynchronous execution.
• Benchmarking tools: NVIDIA Triton Inference Server, TensorRT Profiler.

Module 5: Scalable Pipeline Design for LLM Inferencing

Objectives:

• Build robust, scalable, and fault-tolerant pipelines for inferencing.
• Use batching, caching, and dynamic scaling for efficient pipelines.

Topics:

• Pipeline components: Batching, caching, and queuing.
• Load balancing with Cisco Nexus Dashboard for traffic optimization.
• Fault tolerance: Automatic failover and disaster recovery plans.
• Monitoring pipeline performance with NVIDIA DCGM and Cisco Nexus Dashboard.

Module 6: Monitoring, Logging, and Maintenance for LLM Systems

Objectives:

• Monitor and maintain LLM deployments using NVIDIA and Cisco tools.

Topics:

• Key metrics: Latency, throughput, GPU utilization, and memory usage.
• Monitoring tools: NVIDIA DCGM and Cisco Nexus Dashboard Insights.
• Maintenance workflows for hardware and software reliability.

Module 7: Security and Privacy Considerations in LLM Training and Inferencing

Objectives:

• Secure LLM pipelines using Cisco Nexus Dashboard, Cisco XDR, and NVIDIA tools.

Topics:

• NVIDIA runtime encryption and secure boot.
• Cisco Robust Intelligence for adversarial defense and vulnerability detection.
• Cisco XDR for unified threat detection and automated response.
• Traffic segmentation and endpoint authentication.

Module 8: Migrating from Cloud-Based Training to On-Premises Inferencing

Objectives:

• Transition LLM models from cloud training to on-premises Cisco infrastructure.

Topics:

• Migration strategies for exporting and deploying models.
• Data transfer optimization using Cisco Nexus Dashboard.
• Integrating models with on-premises inferencing pipelines.

Module 9: On-Premises Data Center Design for LLM Inferencing Systems

Objectives:

• Design an on-premises data center with Cisco and NVIDIA technologies.

Topics:

• Cisco UCS and NVIDIA GPUs for high-performance compute.
• Network design and automation with Cisco Nexus Dashboard.
• Storage solutions for large-scale data management.

Module 10: On-Premises Data Center Implementation for LLM Inferencing Systems

Objectives:

• Implement and configure an LLM inferencing data center using NVIDIA and Cisco technologies.

Topics:

• Physical setup: NVIDIA GPUs on Cisco UCS and Nexus networking configuration.
• Performance testing and validation of inferencing pipelines

Labs are designed to assure learners a whole practical experience, through the following practical activities:

• Exploring Transformer model architectures
• Compute attention scores manually for a small sequence.
• Preprocessing lab: Clean, deduplicate, and tokenize a dataset using NVIDIA RAPIDS.
• Tokenization exercise: Implement and analyze subword tokenization methods.
• Deploy an LLM as a REST API using NVIDIA TensorRT.
• Configure traffic policies in Cisco Nexus Dashboard for inferencing workloads.
• Apply quantization and pruning to optimize a pre-trained LLM.
• Benchmark latency, memory usage, and accuracy of optimized models.
• Design a scalable pipeline with batching and caching strategies.
• Configure routing and scaling policies for GPU nodes using Nexus Dashboard.
• Configure dashboards for real-time monitoring of GPU and network performance.
• Simulate hardware failures and evaluate maintenance workflows.
• Analyze and secure an LLM using Cisco Robust Intelligence.
• Configure Cisco XDR to monitor and respond to threats across pipelines.
• Export a cloud-trained model and deploy it on Cisco UCS for inferencing.
• Optimize data transfer pipelines for low-latency inferencing.
• Design a complete data center architecture for LLM inferencing.