How does the vLLM framework specifically improve the throughput of an AI model performance?

All answers to this question.

The primary way vLLM improves performance is by solving the memory bottleneck inherent in the Key-Value (KV) cache. In traditional systems, memory is pre-allocated in large, contiguous blocks, leading to "internal fragmentation" where up to 80% of GPU memory is wasted. PagedAttention allows the KV cache to be stored in non-contiguous memory spaces, much like virtual memory in operating systems. This allows for near-zero waste and enables significantly larger batch sizes. In my experience, switching to vLLM increased our throughput by nearly 24x compared to native PyTorch setups without any loss in accuracy.

   Answered 2025-05-18 by Brenda Marshall

That sounds impressive, but does vLLM also handle dynamic request lengths efficiently without manual padding?

   Answered 2025-05-20 by Michael Peterson

• Yes, Michael! vLLM uses a technique called continuous batching. Unlike static batching where the engine waits for all sequences in a batch to finish, vLLM can insert new requests as soon as an old one completes a token. This keeps the GPU utilized at nearly 100% and drastically reduces the "Time Per Output Token" for users, which is a huge win for real-time AI model performance.

     Commented 2025-05-21 by David Sullivan

It really comes down to GPU utilization. vLLM makes it possible to serve way more users on a single A100 or H100 than before.

   Answered 2025-05-22 by Susan Kelly

• I agree with Susan. The reduction in operational costs is the hidden benefit here. By packing more requests into the same hardware, you lower your cost-per-token significantly while maintaining high-speed AI model performance.

     Commented 2025-05-24 by Kimberly Evans