![]() That’s why NVIDIA introduced TensorRT-LLM, a comprehensive library for compiling and optimizing LLMs for inference. It can be difficult to decide which of these are right for your use case, and to navigate the interactions between these techniques and their sometimes-incompatible implementations. Many optimization techniques have risen to deal with this, from model optimizations like kernel fusion and quantization to runtime optimizations like C++ implementations, KV caching, continuous in-flight batching, and paged attention. ![]() That fact can make them expensive and slow to run without the right techniques. But, as organizations and application developers around the world look to incorporate LLMs into their work, some of the challenges with running these models become apparent. Large language models (LLMs) have revolutionized the field of artificial intelligence and created entirely new ways of interacting with the digital world. ![]() This open-source library is now available for free on the /NVIDIA/TensorRT-LLM GitHub repo and as part of the NVIDIA NeMo framework. Today, NVIDIA announces the public release of TensorRT-LLM to accelerate and optimize inference performance for the latest LLMs on NVIDIA GPUs.
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