Ollama Scaling & Infrastructure Optimization Training Course
Ollama serves as a platform designed for executing large language and multimodal models locally and at scale.
This instructor-led, live training (available online or on-site) is targeted at intermediate to advanced engineers seeking to scale Ollama deployments for environments that are multi-user, high-throughput, and cost-effective.
Upon completing this training, participants will be able to:
- Configure Ollama to handle multi-user and distributed workloads.
- Optimize the allocation of GPU and CPU resources.
- Implement strategies for autoscaling, batching, and reducing latency.
- Monitor and optimize infrastructure to ensure both performance and cost efficiency.
Format of the Course
- Interactive lectures and discussions.
- Hands-on labs focused on deployment and scaling.
- Practical optimization exercises conducted in live environments.
Course Customization Options
- To request a customized training session for this course, please contact us to make arrangements.
Course Outline
Introduction to Scaling Ollama
- Ollama’s architecture and scaling considerations.
- Common bottlenecks encountered in multi-user deployments.
- Best practices for ensuring infrastructure readiness.
Resource Allocation and GPU Optimization
- Strategies for efficient CPU/GPU utilization.
- Considerations regarding memory and bandwidth.
- Resource constraints at the container level.
Deployment with Containers and Kubernetes
- Containerizing Ollama using Docker.
- Running Ollama within Kubernetes clusters.
- Implementing load balancing and service discovery.
Autoscaling and Batching
- Designing autoscaling policies for Ollama.
- Batch inference techniques to optimize throughput.
- Navigating the trade-offs between latency and throughput.
Latency Optimization
- Profiling inference performance.
- Implementing caching strategies and model warm-up techniques.
- Reducing I/O and communication overhead.
Monitoring and Observability
- Integrating Prometheus for metrics collection.
- Building dashboards with Grafana.
- Setting up alerting and incident response for Ollama infrastructure.
Cost Management and Scaling Strategies
- Cost-aware GPU allocation.
- Considerations for cloud versus on-premises deployments.
- Strategies for sustainable scaling.
Summary and Next Steps
Requirements
- Experience with Linux system administration.
- Understanding of containerization and orchestration concepts.
- Familiarity with the deployment of machine learning models.
Audience
- DevOps engineers.
- ML infrastructure teams.
- Site reliability engineers.
Open Training Courses require 5+ participants.
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