What will be a good comparison of vLLM and Ollama

vLLM is an inference engine designed to serve large language models efficiently.

It was developed by researchers from UC Berkeley and is optimized for maximum throughput and low latency.

Key Features:

Efficient multi-user and multi-prompt batching.

Uses PagedAttention: avoids GPU memory waste, improving model scalability.

Supports OpenAI-compatible API, so it can be a drop-in replacement for OpenAI APIs in local setups.

Typically used for serving models like LLaMA, Mistral, Falcon, etc., very fast.

Use Case:

You already have a quantized or full-precision model (e.g., LLaMA 2).

You want to host and serve the model at scale (e.g., in production or RAG pipelines).

You care about maximizing throughput on GPUs.

🧩 What is Ollama?

Ollama is a user-friendly tool to run LLMs on local machines — especially on macOS and Linux — with a simple CLI.

✅ Key Features:

Built-in model download, run, and prompt interface.

Easy CLI/desktop setup: ollama run llama2.

Uses GGUF/GGML quantized models (optimized for CPU and smaller GPUs).

Great for developers, tinkerers, and offline use.

🧠 Use Case:

You want to experiment with LLMs locally.

You're working on a laptop or desktop (e.g., M1/M2 Mac, low-power GPU).

You don't need high-performance batch serving.