Michael Williams

With NVIDIA NIM and NIM Agent Blueprints, medical researchers across the public sector can jump-start their adoption of state-of-the-art, optimized AI models to accelerate their work. 🔗 https://nvda.ws/3NnkMYy Learn how #NVIDIAInception startups Abridge & ConcertAI leverage NVIDIA AI solutions to improve efficiency, reduce administrative burdens, and advance medical research and patient care. 🩺 Abridge ConcertAI

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NVIDIA announced a new NVIDIA AI Foundry service and NVIDIA NIM™ inference microservices to supercharge #generativeAI for the world’s enterprises with the Llama 3.1 collection of openly available models. Aramco is among the first enterprises to access the new NVIDIA NIM microservices for Llama 3.1. https://bit.ly/4dj1DlA

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Data processing just got a major upgrade. 🎊 The new NeMo Retriever Llama 3.2 embedding NIM microservice cuts vector storage by 35x, enabling you to handle more data and scale AI solutions faster—all on a single server. 🛠️ Try the NIM now: ➡️ bit.ly/4gm4DQ4 #RAG

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Data processing just got a major upgrade. 🎊 The new NeMo Retriever Llama 3.2 embedding NIM microservice cuts vector storage by 35x, enabling you to handle more data and scale AI solutions faster—all on a single server. 🛠️ Try the NIM now: ➡️ bit.ly/4gm4DQ4 #RAG

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Effortless Integration for Smarter Insights: CloudEagle.ai Meets Bitbucket Integrating CloudEagle.ai with Bitbucket takes just minutes and gives you: ✔ Simplified App Tracking: No more manual reports, get the app usage data you need, when you need it. ✔ Centralized Control: Manage your Bitbucket security from the CloudEagle dashboard. ✔ Proactive Monitoring: Catch unusual login activity before it becomes a problem. Ready for a smarter, more secure development workflow? Book a demo today - https://lnkd.in/gXAhChrt #saas #saasmanagement #toolstuesday Atlassian #jira #toolstuesday

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Using generative AI, NVIDIA operations built an AI Planner agent, developed on NVIDIA Inference Microservices (NIM). The agent leverages LLM, NeMo Retriever and CuOpt NIM to reduce re-planning time from hours to just seconds. https://nvda.ws/4bIfS2T

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🌟 Boost your AI #inference throughput by up to 3.6x. ⚡ Learn how in this technical deep dive ➡️ We now support speculative decoding and tripling token throughput with our NVIDIA #TensorRT-LLM. Perfect for your generative #AI apps.

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Learn about NVIDIA TAO 5.5: A powerful training framework with new foundation models, multi-modal sensor fusion, and advanced features like text prompt-based auto-labeling and knowledge distillation. Read more. https://nvda.ws/3XjvXY2 #NVIDIAMetropolis

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With NVIDIA AI Foundry, enterprises and nations can supercharge #generativeAI with a service spanning curation, synthetic data generation, fine-tuning, retrieval, guardrails, and evaluation to deploy custom AI at Meta Llama 3.1 NVIDIA NIM Microservices.

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Their success is unsurprising! People are gold. Value them and invest in them.

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📣 Just announced: NVIDIA NIM Agent Blueprints to streamline #generativeAI development. These new reference applications include partner microservices, one or more AI agents, reference code, customization documentation and a Helm chart for: ✅ Digital humans ✅ Multimodal PDF data extraction ✅ Virtual screening Get started:

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NVIDIA AI Releases cuPyNumeric: A Drop-in Replacement Library for NumPy Bringing Distributed and Accelerated Computing for Python NVIDIA has introduced cuPyNumeric, an open-source library designed to be a drop-in replacement for NumPy, providing GPU acceleration at cluster scale without the need to modify existing Python code. Built on the RAPIDS ecosystem, cuPyNumeric aims to solve the limitations of traditional NumPy by leveraging CUDA and Dask for efficient parallel execution, significantly reducing computational time. Researchers can now seamlessly scale their workflows to entire GPU clusters, achieving faster results with minimal changes. This advancement represents a key step forward in making high-performance computing accessible to data scientists and researchers while preserving the simplicity of Python workflows. Read the full article: https://lnkd.in/gmYGNQFT GitHub Page: https://lnkd.in/gbSf-Wab Details: https://lnkd.in/g6RZ2kar NVIDIA NVIDIA AI Charlelie Laurent

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CEO of NVIDIA Jensen Huang: “We never talk about market share in our company… Why are you fighting people for market share? The whole concept of market share says there’s a whole bunch of other people doing the same thing. And if they’re doing the same thing, why are we doing it? Why am I squandering the lives of these incredibly talented people to do something that has already been done? --- Found this helpful? Follow for more content like this. --- #startups #business #startup #entrepreneurship

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👋 Say hello to the NVIDIA app! NVIDIA app supercharges RTX GPUs by bringing creator apps, AI-powered tools/features, streamlined settings, and updated drivers to your fingertips. 🙌 Plus check out the latest #RTXRemix update!

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Ready to deploy AI faster and safer? Check out how #NVIDIA NeMo Guardrails and #LangChain Templates can help you build safer #LLMs. Read more > https://nvda.ws/4aTZYkM

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