vLLM

📖 What is vLLM?

vLLM is an open-source, high-throughput LLM serving engine developed at UC Berkeley. It utilizes PagedAttention memory management to serve open models at maximum throughput.

💡 Why It Matters

vLLM became the standard self-hosted inference engine for serving open-weights LLMs in production environments.

🎯 Workplace Use Cases

• Self-hosting production LLM API infrastructure with high concurrency
• Serving open-source models (DeepSeek, Llama 3) on private GPU clusters
• Maximizing GPU memory utilization during batch inference

🚀 How to Use It

Install via `pip install vllm` and launch the OpenAI-compatible server using `python -m vllm.entrypoints.openai.api_server`.

✨ Key Features

• PagedAttention algorithm for optimized KV cache memory management
• OpenAI-compatible API server endpoint
• Supports continuous batching and multi-GPU tensor parallelism

💳 Pricing & Plans

Free Tier: 100% open-source software (Apache 2.0 License).