Hands-On Distributed LLM Inference System: Guide to Thousand-Level Concurrency Architecture Design

This project is an open-source project for the CSE354 Distributed Computing course, aiming to build a distributed LLM inference system that supports over 1000 concurrent users while balancing low latency and high availability. The system integrates RAG enhancement capabilities, three load balancing strategies, and a complete fault tolerance mechanism. It has been verified on the RTX A6000 GPU of the Thunder Compute platform using the Llama 3.2 1B model, providing a practical architectural reference for LLM service deployment in production environments.

Project Background and Objectives

With the widespread application of LLMs in various industries, building large-scale concurrent inference services has become a key engineering challenge. This course project aims to implement a distributed LLM inference system that supports over 1000 concurrent users in a real GPU environment, serving not only as an academic exercise but also providing a production-level deployment reference. The project has been verified on the RTX A6000 GPU of the Thunder Compute platform using the Llama 3.2 1B model for testing.

Core Components of System Architecture

The system adopts a layered architecture to decouple request processing, model inference, and resource management:

• API Gateway Layer: Unified entry point responsible for request routing, traffic control, authentication and authorization, and protocol conversion;
• Inference Service Layer: Core computing layer including model instances, batch processing optimization, KV caching, and dynamic scaling;
• Retrieval Enhancement Layer: Integrates RAG, supporting document indexing, semantic retrieval, and context assembly;
• Storage and Cache Layer: Includes vector database, session cache, and result cache.

Load Balancing and Fault Tolerance Mechanisms

Load Balancing Strategies

1. Round Robin Scheduling: Simple uniform distribution, suitable for scenarios where node performance is similar;
2. Least Connections: Assign to nodes with the fewest active connections, adapted to scenarios with large differences in request processing time;
3. Weighted Response Time: Dynamically adjust weights based on node performance and load to maximize throughput, suitable for latency-sensitive scenarios.

Fault Tolerance Mechanisms

• Health Check: Active probing + passive monitoring to determine node status;
• Failover: Remove faulty nodes, reroute requests, alert, and auto-recover;
• Request Retry: Automatically retry failed requests to ensure idempotency;
• Data Consistency: Session affinity + state synchronization + eventual consistency.

RAG Implementation and Performance Optimization

RAG Retrieval Enhancement

• Document Processing: Parse multi-format documents → text chunking → embedding generation → index construction;
• Retrieval Flow: Query embedding → similarity search → reordering → context construction;
• Generation Enhancement: Inject retrieved context to reduce LLM hallucinations.

Performance Optimization

• GPU Memory Optimization: INT8/INT4 quantization, gradient checkpointing, paged attention;
• Batch Processing Optimization: Dynamic batching, continuous batching, request bucketing;
• Asynchronous Architecture: Non-blocking IO, coroutine scheduling, streaming response;
• Cache Strategies: Prefix matching cache, semantic cache, multi-level cache.

Real Environment Verification Results

Test Configuration

• Model: Llama 3.2 1B;
• GPU: NVIDIA RTX A6000 (48GB VRAM);
• Concurrent users: 1000+;
• Scenarios: Q&A, code generation, text summarization.

Performance Metrics

• Throughput: Hundreds of requests per second;
• Latency: Average second-level response;
• Success rate: 99.9%+;
• GPU utilization: 80%+.

The verification results prove the effectiveness of the architecture and provide confidence for production deployment.

Deployment, Operation, and Scalability

Deployment and Operation

• Containerization: Docker configuration (CUDA base image, multi-stage build, environment variable injection);
• K8s Orchestration: Deployment for replica management, Service for load balancing, HPA for auto-scaling, Ingress for unified entry;
• Monitoring and Alerting: Prometheus metrics, Grafana visualization, ELK log aggregation, anomaly alerts.

Scalability

• Model Hot Update: Parallel deployment → gray switch → full switch → old version offline;
• Multi-Model Support: Parallel deployment of multiple models, automatic request routing, resource sharing and isolation;
• Cross-Region Deployment: Multi-region clusters, intelligent traffic scheduling, cross-region failover.

Practical Experience, Summary, and Outlook

Practical Experience

• Key Decisions: Async-first, layered decoupling, intelligent load balancing, comprehensive fault tolerance;
• Common Pitfalls: Over-batching, cache invalidation, resource contention, monitoring blind spots;
• Optimization Suggestions: Tune batch processing parameters, establish benchmark tests, pay attention to cold start and long-tail latency, reserve resources for sudden surges.

Summary and Outlook

This project has implemented a distributed LLM inference system supporting thousand-level concurrency, providing practical references for production deployment. As LLM scales and application scenarios expand, distributed inference technology will become more important, and the value of open-source projects will stand out.