卢翔龙-NVIDIA：LLM 推理和服务部署技术总结及未来展望-掘金.pdf

1、NVIDIA：LLM推理和服务部署技术总结及未来展望卢翔龙NVIDIA资深解决方案架构师目录C O N T E N T S大模型技术趋势01.TensorRT-LLM02.03.FP804.Triton Inference Server for LLM目录C O N T E N T S大模型技术趋势01.TensorRT-LLM02.03.FP804.Triton Inference Server for LLMProduction Language Apps Increasing need for deep learning in language applicationsChat,tran

2、slation,summarization,search,generation,etc.models are important for correct resultsModel accuracy directly correlates to helpfulness for users“Online”deployment require Ensure a great experience with applications Multi-functional,accurate models are large making them slow during inference&Deploying

3、 massive models for real-time applicationsMaking cost effective deployments challengingLarge Language Model EcosystemLlama,Falcon,Starcoder,ChatGLM,MPT,&more70-200 Billion parameter or moreRapid evolution makes optimization challengingLLaMaGPTFalconStarcoderChatGLMMPTImage from Mooler0410/LLMsPracti

4、calGuideYang,J.,Jin,H.,Tang,R.,Han,X.,Feng,Q.,Jiang,H.,Hu,X.(2023).Harnessing the Power of LLMs in Practice:A Survey on ChatGPT and Beyond.arXiv Cs.CL.Retrieved from http:/arxiv.org/abs/2304.13712Need a performant,robust,&extensible solution forcost-effective,real-time LLM deployments目录C O N T E N T

5、 S大模型技术趋势01.TensorRT-LLM02.03.FP804.Triton Inference Server for LLMTensorRT-LLM Optimizing LLM InferenceSoTA Performance for Large Language Models for Production DeploymentsChallenges:LLM performance is crucial for real-time,cost-effective,production deployments.Rapid evolution in the LLM ecosystem,

6、with new models&techniques released regularly,requires a performant,flexible solution to optimize models.TensorRT-LLMis an open-sourcelibrary to optimize inference performance on the latest Large Language Models for NVIDIA GPUs.It is built on TensorRT with a simple Python API for defining,optimizing