Challenges of Running RAG Pipelines on Serverless Functions
The article discusses the difficulties of running retrieval-augmented generation (RAG) pipelines on serverless functions like AWS Lambda. It highlights issues like cold starts, model loading, and memory constraints that can impact the performance and scalability of RAG workflows on serverless architectures.
Why it matters
This article provides a realistic assessment of the challenges in running advanced AI/ML pipelines like RAG on serverless infrastructure, which is crucial for developers and architects evaluating their options.
Key Points
- 1Serverless functions require loading large models and dependencies on each cold start, which can take 5-15 seconds
- 2Memory constraints in serverless functions can limit the size of models and data that can be used in RAG pipelines
- 3Serverless functions may not be able to handle the throughput and latency requirements of production RAG workloads
Details
The author explains that while serverless functions seem like an attractive option for running RAG pipelines due to their auto-scaling and pay-per-use benefits, there are significant challenges in practice. The primary issues are around cold starts and the time it takes to load large language models and dependencies in the serverless environment. Even small models can take 5-15 seconds to load, which is unacceptable for most API response time requirements. Additionally, serverless functions have memory constraints that limit the size of models and data that can be used in the RAG pipeline. The author cautions that these performance and scalability issues may make it difficult to run production-ready RAG workflows on serverless architectures without significant engineering effort.
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