LLM Inference Explained: What Product Builders Need to Know
Over the last two years, I’ve spent a lot of time learning and experimenting with LLMs.
I’ve condensed all of that knowledge into this one-hour session, for which I’m writing this blog post.
The discussion explores the technical and business aspects of LLM inferencing, a topic every product builder working with AI should understand.
The talk highlights three core questions:
What exactly is LLM inferencing?
Why is it so costly?
How can we calculate ROI for AI products?
Here’s a breakdown of the main takeaways in simple terms.
What is LLM Inferencing?
At its simplest, LLM inferencing is prediction.
When you enter a prompt into ChatGPT or another large language model, the system predicts the most likely next word repeatedly until it forms a complete response.
The process involves three key parts:
Input and Tokenization
Your text is split into small chunks called tokens (for example, “running” becomes “run” + “ing”). Both your input and the AI’s response are made up of tokens, each converted into numeric IDs that computers can process.
The Role of GPUs
Predicting tokens involves massive matrix multiplications, which require the parallel computing power of GPUs (Graphics Processing Units). CPUs aren’t efficient here because they process tasks sequentially.
Model Size and Accuracy
Larger models (like GPT-4 with over a trillion parameters) tend to generate more accurate results since they’re trained on massive datasets. Smaller models are faster and cheaper but may not perform as well.
Why Does LLM Inferencing Cost So Much?
Unlike traditional software, AI products have high ongoing costs because every interaction consumes significant compute resources.
Here’s why costs stack up:
Input + Output Tokens
You pay for both the tokens in the prompt and the tokens in the model’s response. This is very different from typical APIs.High Concurrency
With millions of users making requests at once, providers need huge GPU clusters running 24/7—leading to enormous operational expenses.Training vs. Inference vs. Fine-tuning
Training happens once and costs millions (e.g., training GPT-4).
Inferencing is continuous, happening every time a user queries the model.
Fine-tuning customizes a base model for a specific use case, like law or healthcare.
Cost Optimization
A common strategy is model routing: simple queries are directed to smaller, cheaper models, while complex queries go to large, powerful LLMs. This balances cost and quality.
How to Calculate ROI for AI Products
The return on investment (ROI) for AI products goes beyond direct revenue.
It’s about the value delivered to users compared to the cost of serving them.
The Formula
ROI = Value Delivered / Cost of ServingWhat Counts as Value?
Time saved (e.g., AI drafting documents in minutes instead of hours)
Operational savings (e.g., AI chatbots reducing the need for human support agents)
Better user experience that boosts retention and loyalty
When users consistently save time, effort, or money, your AI product shows strong ROI, even if revenue impact isn’t immediate.
Final Thoughts
LLM inferencing may sound deeply technical, but for product builders, it boils down to three things: understanding how predictions work, managing costs, and ensuring the product delivers real value to users.
If you’d like to dive deeper, check out the full session: “LLM Inference Explained!” on Product Talk with Malthi.