VLLM vs. Ollama

The rise of Large Language Models (LLMs) has transformed AI-driven applications, with developers relying on optimized inference frameworks. Two prominent solutions in this space are VLLM and Ollama.

About LangChat

LangChat  is an enterprise-level AIGC project solution in the Java ecosystem, integrating RBAC and AIGC large model capabilities to help enterprises quickly customize AI knowledge bases and enterprise AI robots.

Supported AI models:  Gitee AI / Ali Tongyi / Baidu Qianfan / DeepSeek / Douyin Doubao / Zhipu Qingyan / Zero One Everything / iFlytek Spark / OpenAI / Gemini / Ollama / Azure / Claude and other large models.

• Official website: http://langchat.cn/

Open source address:

• Gitee: https://gitee.com/langchat/langchat
• Github: https://github.com/tycoding/langchat

The rise of Large Language Models (LLMs) has transformed AI-driven applications, enabling everything from chatbots to automatic code generation. However, running these models efficiently remains a challenge as they typically require large amounts of computing resources.

To address this, developers rely on optimized inference frameworks designed to maximize speed, minimize memory usage, and integrate seamlessly into applications. Two prominent solutions in this space are VLLM and Ollama—each of which addresses different needs.

• VLLM is an optimized inference engine that provides high-speed token generation and efficient memory management, making it ideal for large-scale AI applications.
• Ollama is a lightweight and user-friendly framework that simplifies the process of running open source LLMs on your local machine.

So, which one should you choose? In this comprehensive comparison, we’ll break down their performance, ease of use, use cases, alternatives, and step-by-step setup to help you make an informed decision.

1. Overview of VLLM and Ollama

Before we dive into the details, let’s understand the core purpose of both frameworks.

VLLM (Very Large Language Model) is an inference optimization framework built by SKYPILOT to improve the efficiency of LLM running on GPUs. It focuses on:

• Generate tokens quickly using continuous batching.
• Efficient memory usage through PagedAttention allows processing of large context windows without consuming excessive GPU memory.
• Seamlessly integrates into AI workflows and is compatible with major deep learning platforms such as PyTorch and TensorFlow.

VLLM is widely used by AI researchers and enterprises that need high-performance inference at scale.

Ollama is a local LLM runtime that simplifies the deployment and use of open source AI models. It provides:

• Prepackaged models such as LLaMA, Mistral, and Falcon.
• Optimized CPU and GPU inference for running AI models on commodity hardware.
• A simple API and CLI allow developers to start LLM with minimal configuration.

Ollama is a great choice for developers and AI enthusiasts who want to experiment with AI models on their personal machines.

2. Performance: Speed, Memory, and Scalability

Performance is a key factor in choosing an inference framework. Let’s compare VLLM and Ollama in terms of speed, memory efficiency, and scalability.

Key performance indicators:

VLLM leverages PagedAttention to maximize inference speed and efficiently handle large context windows. This makes it the solution of choice for high-performance AI applications such as chatbots, search engines, and AI writing assistants.

Ollama offers decent speeds but is limited by local hardware. It works well for running smaller models on MacBooks, PCs, and edge devices, but struggles with very large models.

Conclusion: Ollama is more suitable for beginners, while VLLM is the choice for developers who need deep customization.

3. Use cases: When to use VLLM instead of Ollama?

Best Use Cases for VLLM

• Enterprise AI applications (e.g., customer service bots, AI-driven search engines)
• Deploy cloud-based LLM on high-end GPUs (A100, H100, RTX 4090, etc.)
• Fine-tune and run custom models
• Applications that require large context windows

Not suitable for: Personal laptops, casual AI experiments

Best Use Cases for Ollama

• Run LLM on Mac, Windows or Linux without cloud resources
• Experiment with models locally without complex setup
• Developers who want to integrate AI into their applications using a simple API
• Edge computing applications

Not suitable for: Large-scale AI deployments, heavy GPU workloads

Conclusion: VLLM is suitable for AI engineers, while Ollama is suitable for developers and hobbyists.

4. Get started quickly

VLLM needs to install dependencies first:

pip install vllm

Run inference on the LLaMA model:

from vllm import LLM
llm = LLM(model= "meta-llama/Llama-2-7b" )
output = llm.generate( "What is VLLM?" )

OllamaTo install Ollama (Mac/Linux):

brew install ollama

Then download and run the model:

ollama run mistral

Calling Ollama's API:

import requests
response = requests.post( "http://localhost:11434/api/generate" , json={ "model" :  "mistral" ,  "prompt" :  "Tell me a joke" })
print (response.json())

Conclusion: Ollama is easier to install, while VLLM offers more customization.