Ollama Scaling & Infrastructure Optimization Training Course
Ollama serves as a platform for executing large language and multimodal models locally and at scale.
This instructor-led training, available online or onsite, targets intermediate to advanced engineers seeking to scale Ollama deployments for environments requiring multi-user support, high throughput, and cost efficiency.
Upon completion of this training, participants will be equipped to:
- Set up Ollama for distributed workloads and multi-user access.
- Optimize the allocation of CPU and GPU resources.
- Apply strategies for autoscaling, batching, and reducing latency.
- Monitor and optimize infrastructure to balance performance with cost efficiency.
Course Format
- Interactive lectures and discussions.
- Practical labs focused on deployment and scaling.
- Live optimization exercises in real-world environments.
Customization Options
- For tailored training sessions, please reach out to us to arrange.
Course Outline
Introduction to Scaling Ollama
- Ollama’s architecture and key scaling factors
- Common bottlenecks in multi-user setups
- Best practices for preparing infrastructure
Resource Allocation and GPU Optimization
- Strategies for efficient CPU and GPU utilization
- Memory and bandwidth considerations
- Resource constraints at the container level
Deployment with Containers and Kubernetes
- Containerizing Ollama using Docker
- Deploying Ollama within Kubernetes clusters
- Managing load balancing and service discovery
Autoscaling and Batching
- Designing autoscaling policies for Ollama
- Batch inference techniques to enhance throughput
- Navigating latency versus throughput trade-offs
Latency Optimization
- Profiling inference performance
- Implementing caching strategies and model warm-up procedures
- Minimizing I/O and communication overhead
Monitoring and Observability
- Integrating Prometheus for metrics collection
- Creating dashboards with Grafana
- Setting up alerting and incident response for Ollama infrastructure
Cost Management and Scaling Strategies
- Cost-aware GPU allocation
- Factors to consider when choosing between cloud and on-premises deployments
- Approaches for sustainable scaling
Summary and Next Steps
Requirements
- Experience in Linux system administration
- Knowledge of containerization and orchestration technologies
- Familiarity with deploying machine learning models
Target Audience
- DevOps engineers
- ML infrastructure teams
- Site reliability engineers
Open Training Courses require 5+ participants.
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