* [Topics](/en/topics "Topics") * [Artificial intelligence](/en/topics/ai "Artificial intelligence") * vLLM vs. Ollama: When to use each framework vLLM vs. Ollama: When to use each framework =========================================== Published May 12, 2026•*4*-minute read Copy URL Jump to section --------------- Differences between vLLM and OllamaWhat is vLLM?What is Ollama?What do vLLM and Ollama have in common?When to choose vLLMWhen to choose OllamavLLM vs. Ollama for inferenceHow Red Hat can help What is the difference between vLLM and Ollama? ----------------------------------------------- When integrating [large language models (LLMs)](/en/topics/ai/what-are-large-language-models)into an AI application, [vLLM](/en/topics/ai/what-is-vllm) works well for high-performance production, and Ollama is great for local development. Each tool caters to different ends of the LLM deployment spectrum: vLLM is ideal in [enterprise](/en/topics/ai/what-is-enterprise-ai) settings, whereas Ollama works best for small-scale projects. When considering the differences between vLLM and Ollama, think of Ollama as a sports car and vLLM as a bullet train. With Ollama, you can move at a fast pace, but you can’t bring many people with you. With vLLM, you can both move fast and serve a lot of people at once. Ultimately, the choice between vLLM and Ollama depends on your expertise as a developer as well as the size and scope of your project. For developers experimenting locally, Ollama is a fantastic starting point. But for teams moving toward large-scale production, vLLM provides the foundation needed to reliably and efficiently serve LLMs at scale. [Ollama or vLLM? How to choose the right serving tool](https://developers.redhat.com/articles/2025/07/08/ollama-or-vllm-how-choose-right-llm-serving-tool-your-use-case#) What is vLLM? ------------- vLLM is a library of open source code that helps LLMs quickly and efficiently perform calculations at scale. The overall goal of vLLM is to maximize throughput (tokens processed per second) to serve many users at once. vLLM includes both an inference *server* (to manage network traffic) and an inference *engine* (to boost computational speed): * The inference server component manages the queue of users waiting for service and handles external network traffic. The inference server’s job isn't to perform intensive computation; it's to handle the communication protocol that gets data in and out of the system. * The inference engine component speeds up generation by optimizing graphics processing unit (GPU) usage. It increases computational speed by applying algorithms like PagedAttention to manage the key value (KV) cache and continuous batching for optimized request scheduling. Both the inference server and the inference engine are responsible for achieving low latency—or the time between a user hitting “send” and an output being delivered. The inference server is designed to not add unnecessary latency. It does this by accepting requests, passing them to the engine, and sending responses back across the network as quickly as possible. The inference engine is responsible for actively removing latency by organizing the GPU’s computations. By speeding up processing in this way, vLLM can serve hundreds of users simultaneously in 1 instance. vLLM allows organizations to do more with less in a market where the hardware for LLM-based applications comes with a hefty price tag. It can handle high traffic and is designed for large-scale scenarios. This means it’s ideal for latency-sensitive, multiuser deployments. Overall, [vLLM outperforms Ollama](https://developers.redhat.com/articles/2025/08/08/ollama-vs-vllm-deep-dive-performance-benchmarking#) when serving multiple requests simultaneously. [Read more about vLLM](/en/topics/ai/what-is-vllm) 4 key considerations for implementing AI technology --------------------------------------------------- [Get the resource](/en/resources/4-considerations-for-implementing-ai-technology-checklist "Get the resource") What is Ollama? --------------- Ollama is an open source tool that lets users run LLMs locally and privately. This means you can download, update, and manage an LLM from your laptop without sending any private information to a cloud. Ollama is derived from the [llama.cpp](https://github.com/ggml-org/llama.cpp) project, an open source library that performs inference on various LLMs. Ollama automates some of the more difficult steps involved in compiling, configuring, and managing the underlying components, thereby hiding these complexities from the end user. Built for simplicity, Ollama requires minimal setup and is generally considered to be intuitive and good for beginners. Experienced developers may use Ollama for experimenting with different LLMs and rapid prototyping. What features do vLLM and Ollama have in common? ------------------------------------------------ vLLM and Ollama are both LLM serving frameworks that provide developers access to LLMs they can use to build AI applications. Specifically, an LLM serving framework is a software component that performs [inference](/en/topics/ai/what-is-ai-inference) within the larger application architecture. While vLLM and Ollama cater to different types of users, they share several fundamental features: * Both are open source tools. * Both include an inference server component. * Both let users run LLMs on their own hardware rather than relying on third-party APIs. * Both are designed to make the most of users’ available hardware to accelerate inference speed. * Both support multimodal models, meaning they can process more than just text. * Both support [retrieval-augmented generation (RAG)](/en/topics/ai/what-is-retrieval-augmented-generation), a technique that allows developers to supplement the existing data in an LLM with external knowledge/data of their choosing. [Find benefits and use cases of fast, efficient inference](/en/products/ai/fast-efficient-inference) When to choose vLLM ------------------- The advanced features vLLM offers require deeper technical understanding. Because of this, vLLM caters more to seasoned developers and arguably has a steeper learning curve than Ollama. For example, vLLM can handle models of any size, including those with billions of parameters. To get the most out of the technology, developers should understand how to carry out concepts like [distributed inference.](/en/topics/ai/what-is-distributed-inference) It also has a lot of potential for [fine-tuning](/en/topics/ai/rag-vs-fine-tuning) for specific use cases. To get the best performance, developers should be familiar with methods like [parameter-efficient fine-tuning (PEFT)](/en/topics/ai/what-is-peft) and [LoRA/QLoRA](/en/topics/ai/lora-vs-qlora). In summary, vLLM is for developers who need to squeeze every drop of performance potential from their servers and deploy reliable, scalable applications. Remember our bullet train analogy? vLLM is good at serving a lot of users in a short period of time. When to choose Ollama --------------------- Ollama lets users download and run LLMs on personal computers and provides a simple way to test AI models. However, the primary goal of Ollama is accessibility, not scalability. If users make additional requests, they must wait in a queue. For this reason, developers choose Ollama when they want convenience and don’t need to serve a lot of users. Ollama operates offline by default. This means that once you’ve downloaded a model, it can function without an internet connection. While vLLM also offers maximum privacy, it requires setting up a private server or configuring a secured cloud environment. This extra step requires more expertise. Using vLLM vs. Ollama for inference ----------------------------------- Both vLLM and Ollama have an inference server component. This means both vLLM and Ollama take incoming requests, unpack the data, send it to the engine, and package the response to send back to the end users’ application. However, vLLM is an inference engine, and Ollama is not. This means vLLM can optimize inference in ways Ollama cannot. As an inference engine, vLLM is better at managing memory and handling multiple users at the same time (concurrency): **Managing memory:** vLLM uses the PagedAttention algorithm to manipulate the structure of the GPU’s memory. It frees up space on the GPU, which creates potential to run more requests at once. This process enables high concurrency. **Concurrency:** vLLM uses continuous batching to manage data flow and make the best use of the GPU, so it can handle many users/requests at once. [Compare performance for Ollama and vLLM](https://developers.redhat.com/articles/2025/08/08/ollama-vs-vllm-deep-dive-performance-benchmarking#) How Red Hat can help -------------------- [Red Hat® AI](/en/products/ai) is built for fast, flexible, and efficient inference through its [vLLM-powered](/en/topics/ai/what-is-vllm) server. It reliably connects models to your data to unify the customization and development of specialized agents on a single platform. Built on an open source foundation, our products give you full control of AI workflows from end-to-end at any scale. The Red Hat AI portfolio includes [Red Hat AI Inference](/en/products/ai/inference), an inference stack that provides the operational control to run any model on any accelerator across the hybrid cloud. Get fast, efficient, and cost-effective inference at scale. [Explore Red Hat AI](/en/products/ai) Blog Artificial intelligence (AI) ---------------------------- See how our platforms free customers to run AI workloads and models anywhere. [Read the blog](/en/blog/channel/artificial-intelligence "Artificial intelligence (AI)") Navigate AI with Red Hat: Expertise, training, and support for your AI journey ------------------------------------------------------------------------------ Discover how Red Hat Services can help you overcome AI challenges—no matter where you are in your AI journey—and launch AI projects faster. [Get the resource](/en/resources/services-for-linux-ai-ebook "Navigate AI with Red Hat: Expertise, training, and support for your AI journey") Keep reading ------------ ### What is agentic AI? Agentic AI is a software system designed to interact with data and tools in a way that requires minimal human intervention. [Read the article](/en/topics/ai/what-is-agentic-ai "article | What is agentic AI?") ### What is generative AI? Generative AI is a kind of artificial intelligence technology that relies on deep learning models trained on large data sets to create new content. [Read the article](/en/topics/ai/what-is-generative-ai "article | what is generative ai?") ### What are large language models? A large language model (LLM) is a type of artificial intelligence that uses machine learning techniques to understand and generate human language. [Read the article](/en/topics/ai/what-are-large-language-models "article | What are large language models?") Artificial intelligence resources --------------------------------- ### Featured product * #### [Red Hat AI](/en/products/ai) Flexible solutions that accelerate AI solution development and deployment across hybrid cloud environments. 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