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Course Outline
Introduction to Scaling Ollama
- Overview of Ollama’s architecture and key scaling considerations.
- Identification of 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 regarding memory usage and bandwidth.
- Application of container-level resource constraints.
Deployment with Containers and Kubernetes
- Containerizing Ollama using Docker.
- Running Ollama within Kubernetes clusters.
- Managing load balancing and service discovery.
Autoscaling and Batching
- Designing effective autoscaling policies for Ollama.
- Utilizing batch inference techniques to optimize throughput.
- Understanding the trade-offs between latency and throughput.
Latency Optimization
- Profiling inference performance for insights.
- Implementing caching strategies and model warm-up techniques.
- Reducing I/O and communication overhead.
Monitoring and Observability
- Integrating Prometheus for metrics collection.
- Building comprehensive dashboards with Grafana.
- Establishing alerting mechanisms and incident response protocols for Ollama infrastructure.
Cost Management and Scaling Strategies
- Approaches to cost-aware GPU allocation.
- Evaluating cloud versus on-premises deployment considerations.
- Strategies for achieving sustainable scaling.
Summary and Next Steps
Requirements
- Experience with Linux system administration.
- Understanding of containerization and orchestration technologies.
- Familiarity with the deployment of machine learning models.
Target Audience
- DevOps engineers.
- Machine learning infrastructure teams.
- Site reliability engineers.
21 Hours
