Modern AI applications increasingly rely on models that combine huge parameter counts with multi-million-token context windows. Whether it is AI agents following months of conversation, legal assistants reasoning through gigabytes of case law as big as an entire encyclopedia set, or coding copilots navigating sprawling repositories, preserving long-range context is essential for relevance and…
]]>Data centers are an essential part of a modern enterprise, but they come with a hefty energy cost. To complicate matters, energy costs are rising and the need for data centers continues to expand, with a market size projected to grow 25% from 2023 to 2030. Globally, energy costs are already negatively affecting data centers and high-performance computing (HPC) systems. To alleviate the energy…
]]>AI processing requires full-stack innovation across hardware and software platforms to address the growing computational demands of neural networks. A key area to drive efficiency is using lower precision number formats to improve computational efficiency, reduce memory usage, and optimize for interconnect bandwidth. To realize these benefits, the industry has moved from 32-bit precisions to…
]]>NVIDIA A30 GPU is built on the latest NVIDIA Ampere Architecture to accelerate diverse workloads like AI inference at scale, enterprise training, and HPC applications for mainstream servers in data centers. The A30 PCIe card combines the third-generation Tensor Cores with large HBM2 memory (24 GB) and fast GPU memory bandwidth (933 GB/s) in a low-power envelope (maximum 165 W).
]]>Five months have passed since v1.0, so it is time for another round of the MLPerf training benchmark. In this v1.1 edition, optimization over the entire hardware and software stack sees continuing improvement across the benchmarking suite for the submissions based on NVIDIA platform. This improvement is observed consistently at all different scales, from single machines all the way to industrial…
]]>MLPerf is an industry-wide AI consortium tasked with developing a suite of performance benchmarks that cover a range of leading AI workloads widely in use. The latest MLPerf v1.0 training round includes vision, language and recommender systems, and reinforcement learning tasks. It is continually evolving to reflect the state-of-the-art AI applications. NVIDIA submitted MLPerf v1.0…
]]>Data scientists and researchers work toward solving the grand challenges of humanity with AI projects such as developing autonomous cars or nuclear fusion energy research. They depend on powerful, high-performance AI platforms as essential tools to conduct their work. Even enterprise-grade AI implementation efforts—adding intelligent video analytics to existing video camera streams or image…
]]>NVIDIA DGX SuperPOD trains BERT-Large in just 47 minutes, and trains GPT-2 8B, the largest Transformer Network Ever with 8.3Bn parameters Conversational AI is an essential building block of human interactions with intelligent machines and applications – from robots and cars, to home assistants and mobile apps. Getting computers to understand human languages, with all their nuances…
]]>The pace of AI adoption across diverse industries depends on maximizing data scientists’ productivity. NVIDIA releases optimized NGC containers every month with improved performance for deep learning frameworks and libraries, helping scientists maximize their potential. NVIDIA continuously invests in the full data science stack, including GPU architecture, systems, and software stacks.
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