# Running Large Language Models on Kubernetes: Industry Perspectives This case study summarizes a panel discussion between several industry experts about running Large Language Models (LLMs) on Kubernetes, exploring both technical and operational aspects of LLMOps. ## Panel Background The discussion featured several experienced practitioners: - Manjot - Former Google Kubernetes team member, now investing in LLM companies - Rahul - Founder of AI Hero, focused on LLM deployment and training - Patrick - Working on LLM planning with extensive Kubernetes experience - Shree - Engineer at Outer Bounds building machine learning platforms ## Key Technical Considerations ### GPU Management and Cost Optimization - GPU availability remains a significant challenge ### Kubernetes Architecture Considerations - Originally designed for microservices, not ML workloads - Strengths for LLM deployments: - Challenges: ### Workload Types and Considerations ### Training Workloads - Batch-oriented nature requires specific considerations - Deep learning frameworks like DeepSpeed need special handling - Data volumes in petabyte range create unique challenges - Container sizes and startup times become critical factors ### Inference Workloads - More aligned with traditional Kubernetes service patterns - Requires consideration of: ## Implementation Approaches ### For Organizations Starting Out - Consider managed solutions initially ### For Scale-up Organizations - Need more robust platform approach - Consider building internal platforms that: ## Architectural Patterns ### Component Architecture - Vector databases for embedding storage - Model serving infrastructure - Web applications and APIs - Feedback collection systems ### Infrastructure Considerations - Data privacy and VPC requirements - Cloud vendor selection and lock-in avoidance - Network architecture for model communication - Resource allocation and scheduling ## Best Practices and Recommendations ### Development Experience - Abstract Kubernetes complexity from data scientists - Provide simple deployment interfaces - Enable fast iteration cycles - Support proper debugging capabilities ### Production Considerations - Plan for proper GPU resource management - Consider cost implications early - Design for scalability and reliability - Implement proper monitoring and observability ## Industry Evolution and Future Directions ### Current State - Space moving rapidly with new tools emerging - No clear standardized best practices yet - Various approaches being tested in production ### Emerging Trends - Movement toward simplified deployment interfaces - Growing importance of cost optimization - Increasing focus on inference optimization - Evolution of architectural patterns ## Organizational Considerations ### Team Structure - Separate deployment and data science teams common - Need for infrastructure expertise - Balance between abstraction and control ### Business Considerations - Cost management crucial for startups - Trade-offs between managed services and custom infrastructure - Importance of velocity in model deployment - Need for reliable production infrastructure ## Technical Challenges and Solutions ### Container Management - Large model sizes impact container operations - Need for efficient image management - Startup time optimization crucial ### Resource Orchestration - GPU scheduling complexities - Memory management for large models - Network bandwidth considerations - Storage requirements for model artifacts ### Operational Aspects - Debugging and troubleshooting procedures - Monitoring and alerting setup - Resource utilization optimization - Cost management strategies This case study highlights how Kubernetes, while not perfect for all LLM workloads, provides a robust foundation for building LLM operations at scale. The key is understanding the trade-offs and implementing appropriate abstractions and optimizations based on specific use cases and organizational needs.