How does vLLM’s PagedAttention actually double GPU throughput?

All answers to this question.

I find the OpenAI Agents SDK way easier for prototyping. The "Agent" and "Handoff" objects are much more intuitive than the old LCEL syntax.

   Answered 2025-07-10 by Megan Kelly

• I agree, Megan. For developers who aren't AI specialists, the lower learning curve of the OpenAI Agents SDK makes it the better choice for quick deployment.

     Commented 2025-07-12 by Bradley Cooper

It’s definitely not just marketing. The bottleneck in LLM serving isn't usually the computation; it's the KV (Key-Value) cache memory. Traditional systems reserve a giant, contiguous block of memory for the maximum possible sequence length, which wastes up to 60-80% of VRAM on empty space. vLLM’s PagedAttention breaks that cache into small "pages" (usually 16 tokens). It only allocates memory as needed. This "zero fragmentation" approach allows you to fit much larger batches on the same GPU, effectively doubling or tripling your throughput without buying more hardware.

   Answered 2026-01-20 by Kimberly Davis

Does vLLM still maintain this performance edge when using quantized models like FP8 or INT4?

   Answered 2026-02-02 by Jeffrey Moore

• Absolutely, Jeffrey. vLLM has native support for FP8 and AWQ quantization. When you combine PagedAttention with 8-bit or 4-bit weights, you can serve massive models like Llama 3.1 70B on a single A100 or even a high-end consumer 3090/4090. The performance gain is actually more noticeable with quantization because you can push the batch sizes even higher before hitting the VRAM limit.

     Commented 2026-02-10 by Tyler Henderson