How NVIDIA Uses LLaMA

The article discusses the operational challenges of deploying large language models (LLMs) and introduces LLMOps as a framework for managing their lifecycle.

Stanford University researchers developed an LLM system called STARA to streamline legal research by identifying redundant and outdated laws.

The article discusses the transformative role of domain-adapted large language models (LLMs) with reasoning capabilities in accelerating battery research.

The article discusses the Llama 3. 1 collection of large language models (LLMs) and their applications in enterprise settings.

The article discusses the deployment of LoRA (Low-Rank Adaptation) fine-tuned models using NVIDIA NIM, highlighting the advantages of customizing large language models (LLMs) for specific tasks.

The article discusses the integration of Metaflow and NVIDIA Triton Inference Server for developing and deploying machine learning models.

NVIDIA has released TensorRT-LLM, an open-source library designed to optimize inference performance for large language models (LLMs) on NVIDIA GPUs.

NVIDIA SteerLM is a novel technique designed to simplify the customization of large language models (LLMs) during inference.

The article introduces the NVIDIA NeMo Data Curator, a scalable tool designed for curating trillion-token multilingual datasets for training large language models (LLMs).