Inference Workflow
Last updated
Last updated
Workflow
Client Request:
Incoming requests for AI model inference are received from applications, APIs, or end-users.
Load Balancer:
Distributes incoming requests evenly across the available inference servers.
Ensures optimal utilization of resources and prevents server overloading.
Inference Servers:
Hosts AI models and processes inference requests in real time.
Comprises multiple GPU Servers (1, 2, … N) to handle parallel requests.
GPU Servers:
GPU Server 1, GPU Server 2, GPU Server N: Executes inference tasks with accelerated processing, ensuring low-latency predictions.
Dynamically scales with the workload to maintain performance.
Auto-scaler:
Monitors workload demands and automatically adjusts the number of active GPU servers.
Optimizes resource allocation to balance cost and performance.
Model Registry:
Centralized repository for managing AI models, including versioning, metadata, and deployment configurations.
Ensures inference servers always serve the latest and most accurate model versions.
Key Features
Load Balancing: Prevents bottlenecks and distributes traffic efficiently for seamless operation.
Scalability: The auto-scaler dynamically adapts resources to handle fluctuations in workload.
Real-Time Processing: High-performance GPU servers ensure rapid and accurate predictions.
Model Management: The model registry simplifies model deployment, updates, and rollback processes.
Benefits
High Availability: Redundant and distributed infrastructure ensures uninterrupted service.
Cost Efficiency: Auto-scaling minimizes resource wastage by scaling up or down based on demand.
Flexibility: Supports diverse models and workloads, from real-time recommendations to image recognition.
Reliability: Centralized model management ensures consistency and reduces errors in serving models.
Swarm’s inference architecture provides a robust foundation for deploying AI models at scale, enabling users to deliver low-latency, high-accuracy predictions reliably.
