Ollama vs. vLLM - The comparison for self-hosted LLMs in corporate use

More and more companies are considering running Large Language Models (LLMs) on their own hardware** rather than via cloud APIs. The reasons for this are data protection, cost control and independence from large US providers.

Two open source frameworks are at the center of this: Ollama and vLLM. Both enable the local execution of LLMs, but differ greatly in terms of architecture, performance and target group. While Ollama offers a quick, uncomplicated introduction, vLLM is aimed at productive, scalable company environments.

In this article, we compare both systems from a technical and business perspective - with specific sources, benchmarks and recommendations for practical use.

Table of Contents

• Ollama - The easy way to get started with self-hosted AI
• vLLM - Performance and scaling for production systems
• Direct technical comparison
• When is which solution worthwhile?
• Conclusion: Two frameworks, two strategies - one goal
• Get in touch with us
• List of sources

Ollama - The easy way to get started with self-hosted AI

Ollama is an open source tool that makes it possible to run language models such as Llama 3, Mistral, Phi-3 or Gemma locally - without a cloud, API key or internet connection. According to the official documentation, every inference is executed directly on the user's own hardware, which ensures maximum data security.

Installation is simple:

curl -fsSL https://ollama.com/install.sh | sh
ollama run llama3

A model runs on a local server or notebook within a few minutes. Ollama offers an OpenAI-compatible API so that existing clients (e.g. LangChain or LlamaIndex) can be easily integrated.

This simplicity makes Ollama ideal for developers, research teams and smaller companies who want to quickly build their own LLM applications - such as internal chatbots, automation tools or knowledge bases.

At WZ-IT, we use Ollama in combination with Open WebUI - a user-friendly web interface that enables teams to use LLMs without a command line. Find out more in our comparison between Open WebUI and AnythingLLM.

However, community benchmarks show that Ollama reaches its limits with high parallelism and large models.

vLLM - Performance and scaling for production systems

The vLLM project, developed by the UC Berkeley Sky Computing Group, is a high-performance framework for production-scale LLM inference.

Its architecture is optimized for efficiency:

• PagedAttention - a memory management mechanism that improves token caching and uses GPU memory fragment-free
• Dynamic Batching - automatic grouping of parallel requests to improve throughput and latency
• Multi-GPU and Distributed Serving - Scalability across multiple servers or clusters

In benchmarks by Red Hat and Berkeley, vLLM achieved up to 10 times higher throughput than Ollama - with the same hardware and identical models.

This makes vLLM suitable for companies that want to operate LLMs as an API service, SaaS platform or internal AI layer - i.e. wherever there are many simultaneous users or requests.

The downside: the setup requires experience with GPU infrastructure, Docker / Kubernetes and monitoring. The result is a high-performance, scalable LLM server based on open source.

Direct technical comparison

When is which solution worthwhile?

Companies that want to get started quickly and easily - for example with an internal AI assistant or prototype - clearly benefit from Ollama. Installation is simple, costs remain manageable and operation can even take place on existing servers without a GPU. Ollama is therefore the logical first step for many pilot projects or self-hosted setups.

However, as soon as multiple simultaneous users, high response frequencies or scaled API accesses are added, the difference becomes apparent: this is where vLLM is technically and economically superior. The efficiency per request is significantly higher, and dynamic batching reduces both GPU consumption and latency times - decisive factors for productive systems with several hundred users.

A hybrid strategy is also possible: Ollama as a development and test platform, vLLM as a production layer for high-performance deployments. Both systems can be combined with each other via OpenAI-compatible endpoints.

Conclusion: Two frameworks, two strategies - one goal

Both projects have a clear raison d'être:

• Ollama stands for speed, data protection and user-friendliness - ideal for internal applications and an uncomplicated introduction to self-hosted AI.
• vLLM offers high performance, scalability and efficiency - the right choice for productive enterprise deployments with many users and APIs.

Which framework is the right one depends on where your company is on the AI journey: When starting out, Ollama is recommended; when growing and professionalizing, vLLM is the more powerful foundation.

At WZ-IT, we support both approaches - from the installation and operation of Open WebUI to the provision of high-performance GPU servers for vLLM deployments. You can find out more about our AI server offering in our article on GDPR-compliant AI inference with GPU servers.

Get in touch with us

Would you like to use Ollama or vLLM in your company? Do you need support with installation, migration or operation?

WZ-IT offers:

• Advice on tool selection (Ollama vs. vLLM)
• Installation and configuration in your infrastructure
• Managed hosting in German data centers
• GPU server for high-performance LLM deployments
• Integration with Open WebUI or AnythingLLM
• Training for your team
• 24/7 monitoring and support

📅 Book your free and non-binding initial consultation: Schedule appointment

📧 E-mail: [email protected]

We look forward to supporting you with your LLM strategy!

List of sources

• Ollama – Official Documentation
• Ollama – Library of Available Models
• Thoughtbot Blog – How to use Open Source LLM Model Locally
• vLLM – GitHub Repository
• vLLM – Performance Benchmarks (UC Berkeley)
• vLLM – Distributed Serving Documentation
• vLLM – OpenAI Compatible Server
• vLLM Research Paper – PagedAttention (ArXiv)
• Red Hat Developers – Ollama or vLLM: How to choose the right LLM serving tool
• Red Hat Developers – Ollama vs vLLM: Deep Dive Performance Benchmarking
• McDermott, R. (2024) – Performance vs Practicality: A Comparison of vLLM and Ollama
• vLLM – Installation Guide