vLLM Deployment on Dedicated GPU

PagedAttention is an attention algorithm developed by the vLLM team that manages key-value (KV) cache memory like an operating system manages virtual memory. This drastically reduces memory waste and allows for much higher throughput during inference. It is the core technological breakthrough that makes vLLM deployments so efficient on dedicated GPUs.

Yes, vLLM is specifically optimized to serve large language models like Meta's Llama 3 and Mistral architectures. As long as your dedicated GPU has sufficient VRAM to load the model weights, vLLM will execute highly optimized tensor operations. It fully supports Hugging Face model formats out of the box.

Yes, continuous batching is natively supported to maximize hardware utilization. Instead of waiting for an entire batch of sequences to finish generating, vLLM dynamically swaps requests in and out of the active computation. This results in significantly higher token generation rates compared to traditional static batching.

You need enough dedicated GPU VRAM to hold the model weights, the KV cache space, and the active inference states. For example, running a 7-billion parameter model in 16-bit precision generally requires at least 14GB to 16GB of VRAM. vLLM allows you to explicitly configure the percentage of GPU memory reserved for the KV cache to prevent out-of-memory errors.