In today's AI revolution, setting up the infrastructure often proves more challenging than solving the actual business problems with AI. Running Large Language Models (LLMs) locally comes with its own set of resource challenges.

This is where inference engines become crucial - they're specialized tools that help run LLM models like Llama, Phi, Gemma, and Mistral efficiently on your local machine or server, optimising resource usage.

Two popular inference engines stand out: VLLM and Ollama. While both enable local LLM deployment, they cater to different needs in terms of usage, performance, and deployment scenarios.

My journey into LLMs began with Ollama - it was as simple as downloading the desktop app and typing ollama run llama2 to get started.

Running a powerful AI model locally could be this straightforward?

Ollama

• User-friendly and easy to use locally

• Great for running models on your personal computer

• Has a simple command-line interface.

• Handles model downloading and management automatically

• Works well on Mac (especially with Apple Silicon) and Linux

• More focused on single-user, local deployment

• Includes built-in model library and easy model sharing

vLLM:

• More focused on performance and scalability

• Better suited for production/server deployments

• More complex to set up but offers better performance

• Better for handling multiple simultaneous users

• More focused on high-throughput scenarios

• Requires more technical knowledge to set up and use