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Inference Server Overview

Inference Servers enable you to deploy trained LDM models and serve real-time predictions at scale. They provide a scalable, high-performance infrastructure for serving production models with low latency and high throughput.

What is an Inference Server?​

An Inference Server is a dedicated infrastructure component that hosts and serves trained LDM models for real-time predictions. It provides the compute resources, networking, and management capabilities needed to serve models in production environments.

Key Characteristics:

  • Model Deployment: Deploy trained models to dedicated inference infrastructure
  • Scalable Serving: Automatically scale based on prediction demand
  • Low Latency: Optimized for sub-millisecond prediction times
  • High Throughput: Process millions of predictions per second
  • Resource Management: Efficient GPU allocation and management

Why Use Inference Servers?​

Inference Servers are essential for:

  • Production Serving: Serve models in production with high availability
  • Real-Time Predictions: Deliver predictions with ultra-low latency
  • Scalability: Scale serving capacity based on demand
  • Resource Efficiency: Optimize GPU usage for inference workloads
  • Model Management: Manage multiple model deployments efficiently

Use Cases​

Inference Servers are used for:

  • Real-Time Predictions: Serve predictions for real-time applications
  • Batch Predictions: Process large batches of predictions efficiently
  • API Services: Expose models via RESTful APIs
  • Production Workloads: Serve production models with high availability
  • A/B Testing: Deploy multiple model versions for comparison

Next Steps​