Comparing vLLM vs DeepSpeed for enhancing real-time AI model performance?

All answers to this question.

I made the switch from DeepSpeed to vLLM about six months ago and haven't looked back. DeepSpeed is great for training and some inference tasks, but vLLM’s PagedAttention is specifically designed for high-concurrency serving. In our benchmark, vLLM handled about 3x more concurrent users before the latency became unacceptable. The deployment is also much simpler; the OpenAI-compatible API server it provides out of the box makes it a breeze to integrate with existing frontends while maintaining peak AI model performance.

   Answered 2025-11-28 by Sharon Martinez

How does vLLM handle multi-GPU setups for larger models like Llama-70B?

   Answered 2025-11-30 by Matthew Turner

• It uses Ray or native multiprocessing for tensor parallelism. It splits the model across GPUs very efficiently. This allows you to scale the AI model performance linearly with your hardware. If you have a 4x A100 node, you can run massive models with sub-second latency, which is quite a feat for something as heavy as a 70B parameter model.

     Commented 2025-12-02 by Jason Phillips

vLLM is definitely more optimized for the "serving" aspect of the lifecycle. The continuous batching is a game changer.

   Answered 2025-12-03 by Michelle Lewis

• Agreed. If you're looking for "production-grade" and "high-throughput," vLLM is the current industry standard for squeezing every drop of AI model performance out of your GPUs.

     Commented 2025-12-05 by Joshua Parker