Enterprise RAG: Serving & LLM Infrastructure

A legal document Q&A system serves 500 concurrent users. Every request shares an identical 2,000-token system prompt, appends variable-length retrieved document chunks, and then generates an answer. Without infrastructure-level optimizations, the LLM recomputes key-value tensors for that shared prompt 500 times, fragments GPU memory across wildly different sequence lengths, and leaves compute idle while short responses wait for long ones to finish. The result is blown latency budgets, wasted hardware, and a system that cannot scale.

With a robust evaluation framework established in the previous lesson, the bottleneck shifts to delivering those evaluated, high-quality RAG responses within enterprise latency and throughput targets. This lesson dissects four infrastructure techniques that production serving systems use to solve these problems. KV caching eliminates redundant prefix computation. PagedAttention manages GPU memory without fragmentation. Speculative decoding attacks the sequential generation bottleneck. Continuous batching maximizes GPU utilization under variable traffic. Together, they form the integrated serving stack that interviewers expect you to reason about.

KV caching for prefix reuse

During autoregressive generation, a transformer computes key and value tensors for every token at every layer. Without caching, generating each new token forces the model to recompute KV pairs for all preceding tokens, making the computational cost grow quadratically with sequence length. A KV cacheA GPU memory buffer that stores previously computed key and value tensors from each transformer layer so they can be reused during subsequent generation steps rather than recomputed. eliminates this redundancy by storing these tensors after the initial computation, so each new generation step only processes the single new token.

Prefix-aware caching in enterprise RAG

The benefit compounds in enterprise RAG because requests share structure. When every request begins with the same system prompt (“You are a legal assistant specialized in contract law…”), prefix-aware KV caching computes the shared prefix once and reuses those cached tensors across all concurrent requests. For a 2,000-token shared system prompt with a large model, prefix caching eliminates roughly 60–70% of the prefill computationThe initial forward pass that processes all input tokens (prompt plus retrieved context) before the model begins generating output tokens. per request, directly reducing time-to-first-token (TTFT).

Frameworks like vLLM implement automatic prefix caching by detecting common ...