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How to estimate tokens, GPUs, and cost for an LLM feature with AI System Design

Use the estimation and cost sections to size tokens, GPUs, and spend before launch.

  • Research
  • Intermediate
  • 45 minutes
  • From the README
  • Any agent

Who it is forA platform or ML engineer who must give a budget and hardware estimate for a new LLM feature before it is approved.

The problemTeams often launch LLM features without estimating token volume, GPU needs, or cost per request, then face surprise bills.

The outcomeA rough estimate of tokens per day, GPUs needed, and monthly cost, with the assumptions written down.

What you need first

  • A web browser to open the README at https://github.com/amitshekhariitbhu/ai-system-design
  • Basic familiarity with software system design concepts such as caching, load balancing, and queues
  • A practice problem to design, such as a document Q&A assistant or a customer support agent

Steps

  1. Open the Back-of-the-envelope Estimation for AI section and read Token Estimation, GPU Estimation, and Cost Estimation.
  2. Read Tokens, Latency, and Throughput to understand TTFT, TPS, throughput, and cost per token.
  3. Write down your expected requests per day and average input and output tokens per request.
  4. Apply the formulas from the estimation sections to get tokens per day and GPUs needed.
  5. Read Cost Optimization and list which of the seven options you will test first.

Prompt for your agent

Using the repo amitshekhariitbhu/ai-system-design, follow the Back-of-the-envelope Estimation for AI section to estimate tokens, GPUs, and cost for <target>. Output a short table of the inputs, formulas, and results. Verify that every formula used appears in the README and that each assumption is stated.

Caveat. The estimates depend on your own traffic and model numbers, which the README does not supply, so treat the output as a starting point rather than a quote.

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