LLM
NVIDIA and Outerbounds Revolutionize LLM-Powered Production Systems
NVIDIA and Outerbounds collaborate to streamline the development and deployment of LLM-powered production systems with advanced microservices and MLOps platforms.
Ollama Enables Local Running of Llama 3.2 on AMD GPUs
Ollama makes it easier to run Meta's Llama 3.2 model locally on AMD GPUs, offering support for both Linux and Windows systems.
LangChain Unveils Assistant Editor for LangGraph Studio
LangChain announces the Assistant Editor for LangGraph Studio, enabling easier configuration of LLM-powered agents through a visual interface.
LangGraph.js v0.2 Enhances JavaScript Agents with Cloud and Studio Support
LangChain releases LangGraph.js v0.2 with new features for building and deploying JavaScript agents, including support for LangGraph Cloud and LangGraph Studio.
TEAL Introduces Training-Free Activation Sparsity to Boost LLM Efficiency
TEAL offers a training-free approach to activation sparsity, significantly enhancing the efficiency of large language models (LLMs) with minimal degradation.
AMD Radeon PRO GPUs and ROCm Software Expand LLM Inference Capabilities
AMD's Radeon PRO GPUs and ROCm software enable small enterprises to leverage advanced AI tools, including Meta's Llama models, for various business applications.
NVIDIA's Blackwell Platform Breaks New Records in MLPerf Inference v4.1
NVIDIA's Blackwell architecture sets new benchmarks in MLPerf Inference v4.1, showcasing significant performance improvements in LLM inference.
MIT Research Unveils AI's Potential in Safeguarding Critical Infrastructure
MIT's new study reveals how large language models (LLMs) can efficiently detect anomalies in critical infrastructure systems, offering a plug-and-play solution.
Understanding Decoding Strategies in Large Language Models (LLMs)
Explore how Large Language Models (LLMs) choose the next word using decoding strategies. Learn about different methods like greedy search, beam search, and more.
Strategies to Optimize Large Language Model (LLM) Inference Performance
NVIDIA experts share strategies to optimize large language model (LLM) inference performance, focusing on hardware sizing, resource optimization, and deployment methods.