Ollama Scaling & Infrastructure Optimization Training Course
Ollama serves as a robust platform designed for executing large language models (LLMs) and multimodal models both locally and at a large scale.
This instructor-led live training, available either online or onsite, is specifically tailored for intermediate to advanced-level engineers looking to scale Ollama deployments within multi-user, high-throughput, and cost-efficient environments.
Upon completing this training, participants will be equipped to:
- Configure Ollama to handle multi-user scenarios and distributed workloads effectively.
- Optimize the allocation of GPU and CPU resources.
- Implement strategies for autoscaling, batching, and reducing latency.
- Monitor and fine-tune infrastructure to ensure optimal performance and cost efficiency.
Course Format
- Interactive lectures and discussions.
- Practical, hands-on labs focused on deployment and scaling.
- Real-world optimization exercises conducted in live environments.
Customization Options
- For requests regarding customized training for this course, please contact us to arrange.
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.
Need help picking the right course?
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Ollama Scaling & Infrastructure Optimization Training Course - Enquiry
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