Understanding the True Cost of Large Language Models : Short Executive Perspective series by Rohan Sharma
In the ever-expanding universe of artificial intelligence, LLMs are akin to the latest supercomputers — powerful, promising, but with a price. Let’s dive into what it really costs to chat up LLM’s.
A Penny for Your Thoughts, or a Few for Your Tokens
Starting with OpenAI, they offer access to their models through an API, which operates on a token-based system. Here’s the breakdown: ‘prompt tokens’ are your conversation starters, and ‘sampled tokens’ are the AI’s responses.
💰The cost? $0.03 per 1,000 prompt tokens and double that for the sampled tokens.
Small LLMs: The Budget-Friendly Choice
There’s an alternative route — smaller LLMs. These models, boasting 7–15 billion parameters, are like the compact cars of the AI world. Great for general tasks but might not hit the mark for more specialized needs.
The Grandeur of Gigantic LLMs
On the flip side, we have the Goliaths of the LLM world like GPT 4– models with a staggering 100–200 billion parameters. These LLM’s can dissect and understand complex queries with remarkable accuracy but at a premium cost.
The Cost of computational Power
Running a smaller LLM might require a robust GPU, like the NVIDIA V100, costing approximately $3 per hour or roughly $2000/month.
If you opt for a larger model, expenses could soar to $25/hour roughly translating to around $18,000 per month.
These costs only account for GPU usage, without considering other associated expenses like storage , data transfer, and potential finetuning processes
In Summary
The world of LLMs is a landscape of choices, each with its own cost-benefit analysis.
Understanding the cost implications is key for any Enterprise / startup looking to harness the power of AI.
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