Ollama Scaling & Infrastructure Optimization Training Course
Ollama is a platform for running large language and multimodal models locally and at scale.
This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level engineers who wish to scale Ollama deployments for multi-user, high-throughput, and cost-efficient environments.
By the end of this training, participants will be able to:
- Configure Ollama for multi-user and distributed workloads.
- Optimize GPU and CPU resource allocation.
- Implement autoscaling, batching, and latency reduction strategies.
- Monitor and optimize infrastructure for performance and cost efficiency.
Format of the Course
- Interactive lecture and discussion.
- Hands-on deployment and scaling labs.
- Practical optimization exercises in live environments.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Course Outline
Introduction to Scaling Ollama
- Ollama’s architecture and scaling considerations
- Common bottlenecks in multi-user deployments
- Best practices for infrastructure readiness
Resource Allocation and GPU Optimization
- Efficient CPU/GPU utilization strategies
- Memory and bandwidth considerations
- Container-level resource constraints
Deployment with Containers and Kubernetes
- Containerizing Ollama with Docker
- Running Ollama in Kubernetes clusters
- Load balancing and service discovery
Autoscaling and Batching
- Designing autoscaling policies for Ollama
- Batch inference techniques for throughput optimization
- Latency vs. throughput trade-offs
Latency Optimization
- Profiling inference performance
- Caching strategies and model warm-up
- Reducing I/O and communication overhead
Monitoring and Observability
- Integrating Prometheus for metrics
- Building dashboards with Grafana
- Alerting and incident response for Ollama infrastructure
Cost Management and Scaling Strategies
- Cost-aware GPU allocation
- Cloud vs. on-prem deployment considerations
- Strategies for sustainable scaling
Summary and Next Steps
Requirements
- Experience with Linux system administration
- Understanding of containerization and orchestration
- Familiarity with machine learning model deployment
Audience
- DevOps engineers
- ML infrastructure teams
- Site reliability engineers
Open Training Courses require 5+ participants.
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