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Duration 21 hours
Course Outline
Introduction to Scaling Ollama
- Ollama's architectural overview and scaling factors
- Typical bottlenecks in multi-user setups
- Key practices for preparing infrastructure
Resource Allocation & GPU Optimization
- Strategies for maximizing CPU/GPU efficiency
- Managing memory and bandwidth usage
- Applying resource constraints at the container level
Deployment with Containers & Kubernetes
- Packaging Ollama using Docker
- Deploying Ollama within Kubernetes clusters
- Implementing load balancing and service discovery
Autoscaling & Batching
- Creating autoscaling policies for Ollama
- Using batch inference to boost throughput
- Balancing latency against throughput
Latency Optimization
- Analyzing inference performance
- Implementing caching and model warm-up protocols
- Minimizing I/O and communication overhead
Monitoring & Observability
- Integrating Prometheus for metric collection
- Creating dashboards using Grafana
- Setting up alerts and incident response for Ollama infrastructure
Cost Management & Scaling Strategies
- Optimizing GPU allocation with cost in mind
- Evaluating cloud versus on-premises deployment options
- Planning for sustainable scaling
Summary & Next Steps
Requirements
- Background 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 (SREs)
