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This is wonderful, thanks Daniel. I have a perfect use-case for this. ??

Just took a deep dive into vLLM, and wow – what a brilliant piece of engineering! ?? The examples, Hugging Face UV jobs, and script integrations are absolutely fantastic. Now I finally understand what vLLM really is: ?? A turbo-loader for existing transformer models! It doesn’t reinvent the wheel – it supercharges inference efficiency by: Efficient batch scheduling and token streaming Full GPU utilization via tensor parallelism Supporting long context + multi-user chat scenarios Especially valuable for: ? High-throughput chatbots ? Large-scale document processing ? RAG pipelines, embeddings, summaries, and more I made a little diagram to visualize how I now think of vLLM under the hood. Unfortunately, I’m running on ROCm (AMD GPU) and not CUDA — so I’d have to build a light custom version myself for now. Still, it's a great concept and an exciting development in the open LLM space! Bottom line: vLLM doesn't just serve one model – it transforms how we deploy and scale inference, especially on limited hardware or shared infrastructure. If you're building production-grade LLM tools, vLLM is a game-changer.

Super cool! Quick question/doubt: if you have a setup with 10 GPUs and around 10,000 users each sending requests every few seconds, can you control parameters like `--max-batch-size`, `--max-num-batched-tokens`, or `--batch-interval` in vLLM to fine-tune how requests are batched and scheduled across GPUs, and how does dynamic batching handle high-concurrency scenarios to balance efficiency and latency?

Thanks for all you do!