Six Frameworks for Efficient LLM Inferencing

Large language model (LLM) inferencing has evolved rapidly, driven by the need for low latency, high throughput and flexible deployment across heterogeneous hardware.

As a result, a diverse set of frameworks has emerged, each offering unique optimizations for scaling, performance and operational control.

From vLLM’s memory-efficient PagedAttention and continuous batching to Hugging Face TGI’s production-ready orchestration and NVIDIA Dynamo’s disaggregated serving architecture, the ecosystem now spans research-friendly platforms like…