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Course Outline
Introduction to Scaling Ollama
- Ollama’s architecture and scaling considerations
- Common bottlenecks in multi-user deployments
- Best practices for infrastructure readiness
Resource Allocation and GPU Optimization
- Efficient CPU/GPU utilization strategies
- Memory and bandwidth considerations
- Container-level resource constraints
Deployment with Containers and Kubernetes
- Containerizing Ollama with Docker
- Running Ollama in Kubernetes clusters
- Load balancing and service discovery
Autoscaling and Batching
- Designing autoscaling policies for Ollama
- Batch inference techniques for throughput optimization
- Latency vs. throughput trade-offs
Latency Optimization
- Profiling inference performance
- Caching strategies and model warm-up
- Reducing I/O and communication overhead
Monitoring and Observability
- Integrating Prometheus for metrics
- Building dashboards with Grafana
- Alerting and incident response for Ollama infrastructure
Cost Management and Scaling Strategies
- Cost-aware GPU allocation
- Cloud vs. on-prem deployment considerations
- Strategies for sustainable scaling
Summary and Next Steps
Requirements
- Experience with Linux system administration
- Understanding of containerization and orchestration
- Familiarity with machine learning model deployment
Audience
- DevOps engineers
- ML infrastructure teams
- Site reliability engineers
21 Hours
