AI BriefingCalculating Large-Scale Inference Costs with a Simple Formula
Key point
This explains how to calculate per-user inference costs for a SaaS product using GPU specs and model parameters.
Details
When using AI models as part of a product stack, cost calculation based on GPU performance—A100, H100, B200, etc.—is essential. Even as models or hardware evolve, the principle for calculating the per-user dollar cost remains clear.
For an accurate calculation, the following information is needed.
- GPU hardware specs: Memory Bandwidth and Peak Throughput
- Context Length: assumed to be 200k tokens in the example
- Number of active parameters: assumed to be 32B on a single GPU
- Product characteristics: whether it is based on user prompts, and the Duty Cycle of the user profile
The core computation of an LLM is Matrix Multiplication. Each operation involves memory access and floating-point operations, and efficiency can be improved by reducing the number of memory accesses through optimization techniques such as Tiling.
Ultimately, the LLM inference process is one of predicting the next word by repeatedly applying the Attention function to the input sequence, and the computational efficiency of this process is a key factor determining the profitability of a SaaS product.
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