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Duration 21 hours
Course Outline
Introduction to Scaling Ollama
- Understanding Ollama’s architecture and scaling factors
- Identifying common bottlenecks in multi-user setups
- Best practices for infrastructure preparedness
Resource Allocation and GPU Optimization
- Strategies for efficient CPU/GPU usage
- Memory and bandwidth management considerations
- Enforcing container-level resource limits
Deployment with Containers and Kubernetes
- Packaging Ollama with Docker
- Operating Ollama within Kubernetes clusters
- Implementing load balancing and service discovery
Autoscaling and Batching
- Creating autoscaling policies for Ollama
- Using batch inference to boost throughput
- Balancing latency against throughput
Latency Optimization
- Analyzing inference performance
- Applying caching strategies and model warm-up
- Minimizing I/O and communication overhead
Monitoring and Observability
- Integrating Prometheus for metric collection
- Designing dashboards using Grafana
- Setting up alerts and incident response for Ollama infrastructure
Cost Management and Scaling Strategies
- Optimizing GPU allocation for cost efficiency
- Evaluating cloud versus on-premises deployment options
- Formulating strategies for sustainable growth
Summary and Next Steps
Requirements
- Proficiency in Linux system administration
- Knowledge of containerization and orchestration principles
- Experience with deploying machine learning models
Target Audience
- DevOps Engineers
- ML Infrastructure Teams
- Site Reliability Engineers
