vAttention is a simple, performant and more portable dynamic memory manager for serving large language models. Leveraging CUDA support for demand paging, vAttention stores KV cache in contiguous virtual memory and uses on-demand allocation for physical memory. In doing so, we also introduce various LLM-specific optimizations to address the latency and fragmentation challenges that arise when using demand paging to serve LLMs on GPUs. vAttention supports various attention kernels out-of-the-box and significantly improves LLM serving throughput compared to using the state-of-the-art PagedAttention based kernels of FlashAttention and FlashInfer.