LLM Deployment and Serving Optimization
Moving from model training to production use requires deploying the large language model and serving inference requests efficiently. This chapter concentrates on the operational aspects of this transition, focusing on the unique scale and resource demands of LLMs.
You will examine techniques for:
- Packaging large models and their dependencies.
- Optimizing inference speed and throughput using specialized servers (like Triton or vLLM) and hardware acceleration.
- Reducing model size and computational requirements through methods such as quantization and knowledge distillation.
- Implementing deployment patterns like canary releases and A/B tests suitable for LLMs.
- Managing variable request loads with autoscaling.
- Evaluating options for serverless GPU inference.
The objective is to provide practical approaches for building performant, scalable, and cost-aware LLM serving systems.
Sections
4.1 Challenges in Serving Large Models
4.2 Model Packaging and Containerization for LLMs
4.3 GPU Inference Server Optimization
4.4 Implementing Model Quantization Techniques
4.5 Knowledge Distillation for Deployment
4.6 Advanced Deployment Patterns (Canary, A/B Testing)
4.7 Autoscaling Inference Endpoints
4.8 Serverless GPU Inference Considerations
4.9 Practice: Deploying a Quantized Model
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