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Optimize for the prefill and decode split
Inference happens in two distinct stages. Prefill handles the initial prompt and is limited by raw compute, while decode generates the response and is limited by how fast VRAM can be read. If your application handles massive documents, choose high-compute GPUs like the H100. If you need fast streaming for short interactions, prioritize memory bandwidth over raw processing power.
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Implement prefix caching for long prompts
Re-processing the same system instructions or long conversation histories for every message is a waste of money. Prefix caching stores the mathematical state of common text blocks in GPU memory. This allows the server to skip the heavy prefill phase for repeat context, which can reduce input costs and latency by a factor of ten.
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Replace round-robin routing with cache awareness
Standard load balancers treat every server as interchangeable, but AI servers are stateful because of the KV cache. If a follow-up message lands on a new server, the GPU must re-read the entire conversation from scratch. Use an LLM-aware router like LLM-D to send requests back to the specific GPU that already holds the user's conversation state.
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Manage memory as the primary bottleneck
Processing power is rarely the limit for small businesses, memory is. Most GPUs sit idle while waiting for weights to move from VRAM to the cores. Focus on maximizing throughput by using continuous batching in tools like vLLM. This allows one memory read cycle to serve multiple users simultaneously, significantly lowering the cost per token.
Why it matters
Small businesses often overspend on GPU clouds by using inefficient standard web patterns. Understanding how memory and caching interact allows builders to serve more users on existing hardware without sacrificing response speed or hitting capacity limits.