AI Inference, Explained (What Happens After You Hit Send)

We've all gotten pretty used to opening ChatGPT, typing in a question, and receiving a response within seconds. Whether it's writing code, summarizing documents, or helping troubleshoot something, the experience is fairly straightforward.

Behind the scenes, there's quite a bit of infrastructure involved. Every request needs to be processed by a model running on computing resources, often GPUs, with memory and networking supporting the workload.

Multiply that by thousands of users sending requests simultaneously, and the infrastructure required to support AI applications becomes considerably more complex.

This process is called AI inference.

The Basics

AI inference is the process of using an already trained machine learning model to generate predictions or responses based on new inputs.

During training, a model learns patterns and relationships from large amounts of data. During inference, it uses those learned patterns to process new information.

For a large language model (LLM), the process generally involves four steps:

1. Tokenization: Your prompt is broken into tokens, which can represent words, parts of words, or punctuation.
2. Input processing: The model processes the tokens and their context using the parameters established during training.
3. Token generation: The model predicts the next token and continues generating additional tokens until the response is complete.
4. Delivery: The generated tokens are converted into readable text and returned to the application.

LLMs typically generate responses one token at a time, which is why text often appears gradually rather than all at once.

Why It Exists

Training an AI model requires significant computing power, large datasets, and considerable time. Once training is complete, the model can be deployed to serve requests without repeating the training process.

This allows businesses to build AI-powered applications using existing models rather than developing their own from scratch.

For example, a company building an internal support assistant could use an existing LLM to interpret employee questions, retrieve information from internal documentation, and generate responses.

The application would still need to manage authentication, data retrieval, and the overall user experience, but the model itself would already be trained.

Inference makes this possible, although each request still consumes computing resources. The amount of compute required depends on the model, the length of the input, and the number of tokens generated.

Common Pitfalls

Running a model successfully during development doesn't necessarily mean it'll perform the same way in production.

• Latency: Larger models and longer prompts can increase the time required to generate responses.
• Token consumption: Long conversation histories and detailed responses increase the amount of processing required.
• Concurrency: Handling multiple simultaneous requests requires sufficient compute capacity and efficient scheduling.
• GPU utilization: Dedicated GPUs continue to incur costs even when request volumes are low.
• Model selection: Larger models can introduce unnecessary cost and latency for relatively simple tasks.

Applications can also experience inconsistent response times when traffic increases or when requests contain significantly different amounts of context.

Why It Matters

Cloud providers offer several approaches to deploying and running AI inference workloads.

Services such as Amazon Bedrock, Microsoft Foundry, and DigitalOcean Inference Engine provide managed access to AI models through APIs, reducing the infrastructure that developers need to manage directly.

Organizations can also deploy open-weight models on dedicated GPU infrastructure using inference engines such as vLLM or NVIDIA Triton Inference Server.

Managed inference is often convenient for applications with variable traffic, particularly when teams want to avoid managing GPU infrastructure.

Dedicated deployments offer greater control over model configurations, resource allocation, and serving performance, but require additional operational work.

Cost structures differ as well. Managed APIs commonly charge based on token usage, while self-managed GPU deployments typically charge for provisioned compute capacity.

For applications with consistently high request volumes, dedicated infrastructure may become more economical, provided the GPUs are being utilized efficiently.

The TAM Lens

When planning an AI deployment, workload characteristics influence both the infrastructure requirements and the overall cost.

An internal document summarization tool processing a few hundred requests daily has different requirements from a customer-facing AI assistant handling thousands of conversations.

Response time expectations also vary. A background processing job may tolerate longer inference times, while an interactive application generally needs responses to begin arriving quickly.

Traffic patterns, average token consumption, model size, and expected growth all contribute to infrastructure planning.

From a cost perspective, it's useful to estimate the average expense per request and project how that changes with usage. For dedicated deployments, GPU utilization and the number of concurrent requests the infrastructure can support become equally important.

These considerations help determine whether managed inference or dedicated compute is appropriate for the workload.

How to Stay Sane

• Understand the workload: Define the application's requirements before selecting a model or deployment method.
• Monitor token consumption: Track input and output tokens to understand usage patterns and costs.
• Measure inference latency: Include time to first token and total response time in performance monitoring.
• Evaluate model sizes: Compare smaller and larger models against the application's accuracy and performance requirements.
• Review GPU utilization: For dedicated deployments, monitor resource usage and identify opportunities to improve efficiency.
• Test realistic traffic: Use representative prompts and concurrency levels when evaluating production readiness.

Final Thoughts

AI inference is responsible for turning trained models into applications that people can use every day.

As AI adoption grows, the infrastructure supporting inference will play an increasingly important role in application performance, reliability, and operating costs.

Understanding how inference works, along with the different ways it can be deployed, makes it easier to choose infrastructure that supports both the technical requirements of an application and the business behind it.