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Duration 21 hours
Course Outline
Introduction to Scaling Ollama
- Ollama’s architecture and key scaling considerations
- Identifying common bottlenecks in multi-user setups
- Best practices for preparing infrastructure
Resource Allocation and GPU Optimization
- Strategies for efficient CPU/GPU utilization
- Considerations for memory and bandwidth
- Applying container-level resource constraints
Deployment with Containers and Kubernetes
- Containerizing Ollama using Docker
- Operating Ollama within Kubernetes clusters
- Managing load balancing and service discovery
Autoscaling and Batching
- Designing autoscaling policies for Ollama
- Using batch inference techniques to optimize throughput
- Balancing latency and throughput trade-offs
Latency Optimization
- Profiling inference performance
- Implementing caching strategies and model warm-up
- Minimizing I/O and communication overhead
Monitoring and Observability
- Integrating Prometheus for metrics collection
- Creating dashboards with Grafana
- Setting up alerting and incident response for Ollama infrastructure
Cost Management and Scaling Strategies
- Cost-aware GPU allocation methods
- Evaluating cloud versus on-premises deployment options
- Formulating strategies for sustainable scaling
Summary and Next Steps
Requirements
- Proficiency in Linux system administration
- Knowledge of containerization and orchestration concepts
- Experience with deploying machine learning models
Target Audience
- DevOps engineers
- ML infrastructure teams
- Site reliability engineers
