serving-llms-vllm

Serve large language models for high-throughput inference using PagedAttention and continuous batching techniques. This approach maximizes GPU utilization, enabling low-latency model serving with high request concurrency for production environments.

Yes, you can deploy a 70B parameter model on a single GPU using quantization methods like AWQ, GPTQ, and FP8. These techniques reduce memory constraints, allowing massive models to run on limited hardware while maintaining sub-second time-to-first-token latency.

Yes, vllm provides an OpenAI-compatible API endpoint for serving models. You can start an OpenAI-compatible API server to handle inference requests, ensuring seamless integration with existing applications built for the OpenAI API standard.

PagedAttention solves memory bottlenecks during LLM serving by implementing continuous batching that maximizes GPU utilization. This technique manages memory constraints inherent in deploying large language models, enabling high-throughput and low-latency inference.

Achieve sub-second time-to-first-token latency for a production chatbot by deploying models with continuous batching and PagedAttention. This ensures high request concurrency and low-latency inference on NVIDIA GPU infrastructure.