Documentation
Deployment
Deploy and operate Llama models at scale.
Production deployment with Llama
Deploying and operating Llama models at scale requires a comprehensive approach that spans infrastructure, model selection, experimentation, security, and cost management.
The guides in this section introduce foundational concepts and decision points that are critical to successfully integrate Llama models into production enterprise environments.
Private Cloud Deployment
Private cloud deployment offers full control over infrastructure, security, and compliance.
Learn about key architectural patterns, including VPC isolation, cross-region replication, and multi-cloud strategies.
Production Deployment Pipelines
Transitioning from experimentation to production requires robust, automated pipelines that manage the full lifecycle of Llama models in production.
Learn how to use production deployment pipelines to automate the model lifecycle, from data ingestion and validation; through fine-tuning and evaluation; to rollouts, A/B testing, and performance monitoring.
Infrastructure Migration
Migrating from external providers to Llama involves a structured methodology that reduces risk and ensures continuity.
Learn how to use continuous validation of quality, latency, and cost for smooth migration from other providers to Llama.
Model Versioning and Migration
Llama models use a clear versioning system. Major releases introduce architectural changes, such as mixture-of-experts, while minor versions add targeted improvements.
Learn how to choose the right Llama model version by evaluating performance, compatibility, and operational trade-offs, using baseline measurement and compatibility testing to minimize risk.
Accelerator Management
Large language models require specialized hardware accelerators, such as GPUs or TPUs, to deliver cost-effective and low-latency inference.
Learn about key selection factors—including memory, compute power, availability, and cost–to determine hardware needs for specific models and applications. Maximize utilization through batching, caching, and job scheduling to improve cost-effectiveness.
Autoscaling and Resource Optimization
Autoscaling addresses fluctuating demand and high memory requirements. Horizontal scaling (e.g., Kubernetes) and vertical scaling optimize resource allocation. Monitoring queue depth, GPU utilization, and latency enables proactive scaling.
Learn techniques like quantization, dynamic batching, and using spot instances to further reduce costs.
Self-Hosting for Regulated Industries
For industries with strict data privacy requirements, self-hosting Llama models ensures data control and compliance.
Learn about deployment patterns including air-gapped, private network, and hybrid architectures. Implementing security controls—such as PHI detection, audit logging, and encryption—is essential for protecting sensitive data and meeting regulatory standards.
Security in Production
Securing Llama deployments requires a multi-layered approach that addresses threats at the infrastructure, data, application, and operational levels.
Read about industry-standard security techniques like zero-trust and least-privilege principles. Learn how to mitigate LLM-specific threats such as prompt injection and insecure output handling via security gateways and robust input/output validation. Understand how continuous monitoring, audit logging, and incident response planning can help ensure security & compliance in production environments.
Cost Projection and Optimization
Accurate cost projection and total-cost management is essential for sustainable LLM deployments. Cost drivers include token processing (input, output, and cached tokens), GPU hardware, cloud infrastructure, and hidden factors such as compliance, monitoring, and model versioning.
Learn how to accurately forecast costs by understanding workload patterns and optimizing utilization; use batch processing, spot instances, and right-sizing to help control ongoing costs.
Comparing Costs
Choosing the optimal deployment model (managed APIs, serverless GPU, GPU rental, or bare metal ownership) depends on workload characteristics, privacy requirements, and operational priorities. Each option presents distinct trade-offs in terms of cost structure, scalability, latency, and control.
Learn how to evaluate these options by gaining a clear understanding of token throughput, utilization patterns, and the specific needs of the application.
