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Duration 21 hours
Course Outline
Introduction to Scaling Ollama
- Architectural overview of Ollama and key scaling factors
- Identifying common bottlenecks in multi-user deployments
- Best practices for preparing infrastructure for scale
Resource Allocation and GPU Optimization
- Strategies for efficient CPU and GPU utilization
- Considerations for memory and bandwidth management
- Managing resource constraints at the container level
Deployment with Containers and Kubernetes
- Containerizing Ollama using Docker
- Executing Ollama within Kubernetes clusters
- Implementing load balancing and service discovery
Autoscaling and Batching
- Designing effective autoscaling policies for Ollama
- Using batch inference techniques to optimize throughput
- Balancing latency against throughput trade-offs
Latency Optimization
- Profiling inference performance for insights
- Applying caching strategies and model warm-up procedures
- Minimizing I/O and communication overhead
Monitoring and Observability
- Integrating Prometheus for metrics collection
- Constructing dashboards with Grafana
- Establishing alerting and incident response protocols for Ollama infrastructure
Cost Management and Scaling Strategies
- Cost-aware approaches to GPU allocation
- Evaluating cloud versus on-prem deployment options
- Strategies for achieving sustainable scaling
Summary and Next Steps
Requirements
- Practical experience in Linux system administration
- Solid understanding of containerization and orchestration principles
- Familiarity with deploying machine learning models
Target Audience
- DevOps engineers
- ML infrastructure teams
- Site reliability engineers
