--- title: "LLM Inference: What businesses need to know" description: "Learn what LLM inference is, how it works, how it differs from training, and which hardware and frameworks support it. Read our article now!" date: 2026-10-06 tags: ["AI","LLM","inference"] url: https://www.exoscale.com/blog/llm-inference/ --- # LLM Inference: What businesses need to know ![Cover](https://www.exoscale.com/blog/llm-inference/cover.png) **Quick summary of this article** - Inference uses an already trained LLM for new requests, while training changes the model itself. - The system first processes the prompt, then generates the response token by token. - Hardware provides the required compute and memory, while inference frameworks organize how models and requests are handled. - Large models, long prompts, traffic peaks, and unused accelerator capacity can increase response times and resource use. - Different applications set different priorities. Assistants need fast responses, batch workloads favor throughput, and agentic workflows need reliable multi-step execution. Large language models (LLMs) are the brains of AI applications such as text generation, code generation, translation, conversational assistants, and more. After training establishes the model’s learned parameters, these models generate outputs from new inputs for real users and applications. This article explains inference in the context of LLMs and how it differs from training. ## What is LLM Inference? In an LLM, inference uses patterns and capabilities acquired during training to produce an output without changing the model’s learned parameters. The process runs each time a user or application submits a request. Depending on the task, the result may be an answer to a question, a document summary, a translation, generated code, or extracted information from supplied content. In production, inference needs a model serving layer that makes the model available to applications. That layer loads the model, manages and schedules requests, monitors execution, and returns responses to applications. ### LLM Inference vs. Training: What is the difference? Training and inference differ mainly in what happens to the model’s parameters and when each process runs. The table below summarises the most important differences between LLM training and inference. | Aspect | LLM training | LLM inference | | :---- | :---- | :---- | | **Purpose** | Develop or modify model capabilities using large datasets | Use a trained model to process new prompts | | **Input** | Training examples prepared for model development or adaptation | New requests submitted by users or applications | | **Model weights** | Updates learned weights to improve or specialize model behavior | Keeps learned weights unchanged during each request | | **Processing** | Uses computationally intensive optimization and backpropagation to calculate weight updates | Performs forward-only processing to generate a result | | **Frequency** | Runs before deployment and may recur during retraining or fine-tuning cycles | Runs repeatedly whenever a production application receives a request | | **Position in the lifecycle** | Creates or adapts the model before a version enters production | Operates after deployment as part of continuous model use | | **Cost structure** | Concentrates compute and engineering costs in development or adaptation cycles | Creates ongoing compute, infrastructure, and operating costs as requests continue | Fine-tuning remains part of training because it changes the model’s parameters for a task or domain. ## How LLM Inference works Each user or application request passes through several stages before the response is returned: 1. An application sends a prompt to the model serving system 2. The prompt is tokenized into units that may represent parts of words, complete words, punctuation, whitespace, or special symbols. 3. The model processes the whole list of tokens 4. The model generates output tokens sequentially, with each new token extending the response 5. The application receives the completed output or streams tokens as they become available ![How Inference works](https://www.exoscale.com/blog/llm-inference/how-llm-inference-works.png) This computation has two principal stages: prefill and decode. During prefill, the model processes the complete input prompt. In the decode stage, it produces the response one token at a time. The so-called key-value (KV) cache stores information from earlier processing, preventing repeated computations as the model generates further tokens. For a detailed explanation of how LLM Inference works, read our article [Inside an LLM: From Prompt to Tokens](https://www.exoscale.com/blog/llm-from-prompt-to-tokens/). ## LLM Inference hardware and frameworks The main hardware components and their characteristics are: * **Graphics processing units (GPUs)** and other accelerators commonly support large models and real-time applications * **Central processing units (CPUs)** can suit smaller models, testing, batch processing, or workloads with less demanding latency requirements * **Accelerator memory** limits how much model and request-related data can remain available during execution * **Memory bandwidth** matters because the system must move model data between memory and compute resources throughout LLM inference An LLM inference engine is the software layer that loads a model, runs it on available hardware, and serves outputs to applications. In production, this layer can: * schedule requests and combine them through dynamic or continuous batching, * manage memory and the KV cache, * stream generated tokens, * coordinate execution across one or more accelerators, * provide APIs for applications to access the model, * and supply monitoring data for the inference service. Examples of LLM inference frameworks include vLLM, Hugging Face Text Generation Inference, SGLang, TensorRT-LLM, and MAX. ![Inference frameworks and hardware work together](https://www.exoscale.com/blog/llm-inference/inference-stack.png) Hardware and frameworks operate as a combined inference stack for LLMs. A framework must support the selected model and use the underlying processors and memory effectively. ## Performance, cost, and operational challenges of LLM Inference Changes that improve throughput or hardware utilization can also affect latency, memory use, and cost. These factors interact closely with each other to shape the user experience and determine whether a service can remain predictable in the face of changing demand. * **Compute and memory requirements:** Limited compute or accelerator memory can restrict concurrency and available capacity for large models. * **Long inputs and outputs:** Longer prompts require more processing, while longer responses extend generation. Both increase KV cache requirements and the compute cost of each request. * **Traffic peaks:** Many concurrent requests can exceed available capacity, create queues, and produce inconsistent response times across otherwise similar requests. * **Low infrastructure utilization:** Dedicated accelerator capacity continues to generate costs during quiet periods, even when applications use only a small share of it. * **Scaling complexity:** Capacity decisions must consider model loading, memory limits, request queues, and available accelerators. * **Security and output governance:** Production systems need safeguards around model access and sensitive application data. Output quality requires separate controls because language models can also produce inaccurate or unsuitable responses. Priorities vary by workload: Interactive assistants often require low and consistent latency because users wait for each response. Batch document processing places more emphasis on throughput and cost efficiency because immediate results matter less. Agentic applications may trigger several model calls for one task, making end-to-end reliability and accumulated latency especially important. ## LLM engineering and inference optimization LLM engineering combines model selection and preparation with inference software, hardware, and deployment configuration. LLM inference optimization focuses on improving how efficiently this setup runs. Optimization can involve different approaches: * **Model selection:** A smaller or more task-appropriate model can reduce resource requirements. * **Memory:** Quantization represents model weights using lower-precision numerical formats. This can reduce memory use when the hardware and inference runtime support the selected format. * **Request efficiency:** Batching processes several requests together to use hardware more efficiently. * **Caching:** KV caching reuses information during generation, while prefix caching can reuse shared prompt segments across requests. * **Faster execution:** Speculative decoding, optimized attention implementations, and improved computation kernels can reduce processing overhead.These kernels are specialized software routines that help the hardware execute the LLM’s required calculations more efficiently. * **Scaling across hardware:** Multi-GPU execution, model parallelism, and tensor parallelism may distribute computation across several accelerators. ## Common enterprise use cases for LLM Inference LLM inference supports enterprise applications, from conversational assistants to document processing and software development. Each use case creates different requirements for latency, throughput, context handling, reliability, and cost. These are the main use cases for LLM Inference: ### Conversational assistants and customer support Customer-facing chatbots, internal HR or IT help desks, and employee knowledge assistants use company information to generate contextual responses. These applications depend on low latency and consistent response times because users interact with them directly. ### Document processing and summarization Organizations can process reports, contracts, meeting transcripts, and more through summarization, information extraction, or content transformation. Some workloads run in scheduled batches, while others begin when an employee uploads a document or requests an analysis. Batch processing typically prioritizes throughput and cost efficiency. User-triggered processing may also require predictable completion times. ### Enterprise search and retrieval-augmented generation Search assistants can retrieve relevant company information before generating a contextual answer grounded in internal documents. This approach is commonly known as retrieval-augmented generation (RAG). Its performance depends on the retrieval system and the generation stage. Slow document retrieval, data transfer, or model output can increase the total response time. ### Software development Development tools can provide code completion, generate code, support debugging, or translate natural-language requirements into code. These use cases often require responsive generation because developers work interactively. They may also need enough context to account for existing files, conventions, or earlier instructions within the task. ### AI agents and workflow automation Agentic applications coordinate several steps and connect models with tools, application programming interfaces, and business systems. One task may require multiple inference calls before the workflow reaches a result. Multiple model calls can compound latency, cost, and failure risk across the workflow. ## Frequently asked questions about LLM Inference ### What is meant by inference in LLM? Inference is the use of a trained LLM to process a new input and produce a result. Unlike training, this process does not modify the model’s parameters. This computation runs whenever an application submits a request. Model serving surrounds it with functions such as request handling, scheduling, API access, and monitoring. ### How does LLM inference work? The system converts a prompt into tokens and processes the input before generating the response token by token. Prefill handles the input, while decode generates the output. A KV cache stores information that can be reused during generation. ### Is an LLM an inference model? An LLM is the model, while inference is the process of using that model to produce an output. ### What are LLM inference engines? LLM inference engines are software runtimes that execute trained models and handle production requests. They sit between the model and the applications that use it, managing how requests reach the available compute resources.