To architect a RAG (Retrieval-Augmented Generation) system for high concurrency with minimal latency degradation, focus on three key areas: scaling the vector database, distributing LLM workloads, and optimizing system-wide resource usage.
1. Scaling the Vector Database The vector database is often the first bottleneck in high-concurrency scenarios. Use a distributed architecture that supports horizontal scaling, such as sharding data across nodes. For example, tools like Milvus or Pinecone allow partitioning indexes across servers to parallelize query processing. Implement approximate nearest neighbor (ANN) algorithms like HNSW or IVF to reduce search latency, trading a small accuracy loss for significant speed gains. Caching frequently accessed embeddings or query results (e.g., using Redis) can further reduce database load. Additionally, pre-filtering using metadata (e.g., time ranges or categories) narrows the search space, decreasing compute per query.
2. Parallelizing LLM Workloads Deploy multiple LLM instances behind a load balancer to distribute inference requests. For cost efficiency, use smaller/faster models (e.g., Mistral-7B instead of Llama-70B) where possible, and reserve larger models for complex queries. Asynchronous processing with a queue (e.g., RabbitMQ or Kafka) decouples retrieval from generation, preventing LLM bottlenecks from blocking the entire pipeline. Batch processing—grouping similar queries—can improve GPU utilization for models that support batched inference. For stateless interactions, consider precomputing common responses or using distillation to create lighter-weight models.
3. System-Wide Optimization Adopt a microservices architecture to independently scale components like embedding generation, retrieval, and LLM inference. Use GPU-accelerated inference servers (e.g., vLLM or Triton) with autoscaling to handle traffic spikes. Optimize network overhead by colocating services in the same cloud region and using binary serialization (Protocol Buffers) instead of JSON. Implement rate limiting and circuit breakers to prevent cascading failures. Monitor latency at each stage (embedding, retrieval, generation) using distributed tracing tools like OpenTelemetry to identify and resolve bottlenecks dynamically.
Example: A news Q&A system could shard its vector database by topic, cache trending queries, and use a cluster of quantized Mistral-7B instances. Pre-filtering by publication date reduces retrieval scope, while asynchronous queues handle sudden traffic spikes during breaking news events. This balances cost, accuracy, and responsiveness under load.