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Duration 21 hours
Course Outline
Introduction to Scaling Ollama
- Ollama’s architecture and key scaling factors
- Typical bottlenecks in multi-user setups
- Best practices for preparing infrastructure
Resource Allocation and GPU Optimization
- Strategies for maximizing CPU and GPU efficiency
- Considerations for memory and bandwidth management
- Applying resource constraints at the container level
Deployment with Containers and Kubernetes
- Packaging Ollama using Docker
- Deploying Ollama within Kubernetes clusters
- Managing load balancing and service discovery
Autoscaling and Batching
- Formulating autoscaling policies for Ollama
- Using batch inference to boost throughput
- Balancing latency against throughput requirements
Latency Optimization
- Analyzing inference performance via profiling
- Implementing caching and model warm-up techniques
- Minimizing I/O and communication costs
Monitoring and Observability
- Connecting Prometheus for metrics collection
- Creating dashboards using Grafana
- Setting up alerts and incident response for Ollama infrastructure
Cost Management and Scaling Strategies
- Allocating GPUs with cost awareness
- Evaluating cloud versus on-premises deployment options
- Developing strategies for sustainable growth
Summary and Next Steps
Requirements
- Hands-on experience with Linux system administration
- Solid understanding of containerization and orchestration principles
- Knowledge of deploying machine learning models
Audience
- DevOps engineers
- ML infrastructure teams
- Site reliability engineers
