In this article
Inference runs an already prepared model on new requests. The GPU supplies computation, but a complete service also needs an API, network, access controls and operations.
When a user sends a language model a message and receives an answer, inference takes place. The service applies an already prepared model to a new request. This is a distinct stage in the model lifecycle: the model does not need to be fully trained again for each answer.
For a server owner, hardware can therefore be considered part of an operating service, rather than solely a research team’s tool.
From message to answer
First, text becomes a sequence of tokens. A token may represent part of a word, a whole word or another text fragment. The model then processes the input sequence and generates a response. The exact token count depends on the model’s tokenizer, so character counts cannot always be converted using one fixed ratio.
An operator must distinguish input-context processing from sequential response generation. A long document and a short question requiring a long answer create different workload profiles even with similar total token counts.
Why use a GPU?
A language model performs large numbers of operations on numerical arrays. GPUs suit this computation, but suitability depends on memory, architecture and the supported software environment. Having a graphics card alone does not guarantee appropriate speed or economics.
Alongside model weights, memory is needed for request working data. A model loading onto a GPU does not necessarily mean it can support the required context and several users simultaneously. The VRAM guide explores this further.
What the operator adds
A working service combines:
- a model and compatible execution engine;
- an interface for receiving requests and returning answers;
- access management and workload limits;
- monitoring of errors and performance;
- procedures for updates and recovery after failures.
For example, vLLM provides a compatible API server for serving models. Having that server alone does not resolve commercial operations.
Assessment notes
A GPU pool combines compute nodes and software infrastructure for model serving. Selling inference through an external platform requires separate provider onboarding.
If you are choosing between research and service workloads, read the inference and training comparison.