LLM Infrastructure Sizing: From Hardware Requirements to Production Capacity

Conventional VRAM calculators often overlook the importance of memory requirements for serving production traffic, focusing instead on whether a model can load. This oversight is largely due to the significant memory consumption by the KV cache, especially at production batch sizes, which surpasses the needs of merely loading model weights. A comprehensive approach to memory calculation for LLM inference should consider model weights, KV cache, activations, and framework overhead, which are crucial for determining infrastructure requirements for production workloads. The guide emphasizes the need for accurate throughput capacity calculations and the decision-making involved in self-hosting versus using APIs, considering factors like cost per token and utilization rates. It also highlights the importance of understanding the memory and throughput demands based on concurrent user requirements and outlines the cost implications of self-hosting, including engineering time and infrastructure expenses. For effective deployment, the guide suggests strategies such as data parallelism and model optimization through tools like vLLM to enhance throughput and cost efficiency, ensuring that models not only run but also serve production traffic effectively.

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