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 onsite, targets intermediate to advanced engineers seeking to scale Ollama deployments for environments that require multi-user support, high throughput, and cost efficiency.
Upon completion of this training, participants will be capable of:
- Configuring Ollama to handle multi-user and distributed workloads.
- Optimizing the allocation of GPU and CPU resources.
- Implementing strategies for autoscaling, batching, and reducing latency.
- Monitoring and refining infrastructure to enhance both performance and cost efficiency.
Course Format
- Interactive lectures and discussions.
- Practical labs focused on deployment and scaling.
- Hands-on optimization exercises conducted in live environments.
Course Customization Options
- To request a customized version of this course, please contact us to make arrangements.
Course Outline
Introduction to Scaling Ollama
- Ollama’s architecture and key scaling considerations.
- Common bottlenecks encountered in multi-user deployments.
- Best practices for preparing the infrastructure.
Resource Allocation and GPU Optimization
- Strategies for efficient CPU/GPU utilization.
- Considerations for memory and bandwidth.
- Applying resource constraints at the container level.
Deployment with Containers and Kubernetes
- Containerizing Ollama using Docker.
- Running Ollama within Kubernetes clusters.
- Managing load balancing and service discovery.
Autoscaling and Batching
- Designing autoscaling policies for Ollama.
- Utilizing 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.
- Reducing I/O and communication overhead.
Monitoring and Observability
- Integrating Prometheus for metrics collection.
- Constructing dashboards with Grafana.
- Establishing alerting and incident response for Ollama infrastructure.
Cost Management and Scaling Strategies
- Implementing cost-aware GPU allocation.
- Evaluating considerations for cloud versus on-premises deployment.
- Adopting 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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