Discover the difference between GPUs and NPUs to find the perfect Teguar product for your AI needs. Enhance your technology today! https://hubs.la/Q02DK-6L0
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VP level GM Business Development | Server & Datacenter | Technology and Semiconductor | Fortune 50 | HPC, AI, Quantum, CPU (x86, Arm, RISC-V), GPU, Accelerators | At Intel, Ex-Dell, Ex-Cray
Think you need a cutting-edge GPU for generative AI? Think again. A hacker showcased how a Commodore 64, running on a 1.023MHz CPU with 64KB of RAM, can generate 8x8 sprites. While code optimization on a modern machine played a role, the feat demonstrates that AI innovation knows no bounds, even on a 42-year-old system. Check out the fascinating story here: [Link to the article] #AI
NPU who? Nah, I'll do my AI image generation on a Commodore 64 thanks very much
pcgamer.com
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Product and Technology Designer & Delivery Specialist | Associate Professor at Centro de Informática (UFPE), Associate Research Fellow at SoftexRecife, Timbaleiro e Filho de Gandhi
How to Use Nvidia's Chat With RTX AI Chatbot on Your Computer Interested in Nvidia's new Chat with RTX AI chatbot? Here's how you can try it out! https://lnkd.in/dYekKu7v
How to Use Nvidia's Chat With RTX AI Chatbot on Your Computer
makeuseof.com
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Inflection AI thanks NVIDIA, Microsoft, and CoreWeave for their collaboration and support in building the AI cluster that made the training of Inflection-1 and Inflection-2 possible. Inflection-2 was trained on 5,000 NVIDIA H100 GPUs in fp8 mixed precision, amounting to approximately 10²⁵ FLOPs. This newly-released model is set to be integrated into Pi, the chatbot introduced by Inflection. Inflection-2 outperformed the largest, 70 billion parameter version of LLaMA 2, xAI's Grok-1, Google's PaLM 2 Large, and Anthropic's Claude 2, trailing only behind GPT-4 in the MMLU task.
Inflection-2: The Next Step Up
inflection.ai
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"People who know how to use AI will replace people who don't" What's a Prompt Engineer vs a Prompt Professional? Will Prompt Engineering always exist? What comes next? What will the AI workforce look like in a year? More Engineers? Less? Why is there a surge in art on news websites? Is there going to be a demand for Chip designers? Will AI take over Prompt Engineering? When AGI? If you are curious about these questions, you need to watch this great interview of Jonathan Ross from Groq by Ram Ahluwalia, CFA from Lumida Wealth -----summary by AI (naturally)-------- - Jonathan is the CEO and founder of Groq - Groq has built a high-performance AI - Jonathan developed the TPU while at Google as a skunk work project - Groq's focus is on providing an API for their service and selling the systems and chips - They primarily target Enterprise customers who need low latency - They have designed their own chip - They can run open source models faster than traditional methods - Groq does not directly compete with Nvidia or AMD. Jonathan Ross, CEO and founder of Groq, features in an interview where he discusses the company's high-performance AI and its potential for revolutionizing the industry. Ross, who previously developed the TPU while working at Google, explains that Groq's chip is designed to be faster and more efficient than traditional GPUs. The company offers an API service, allowing customers to purchase tokens of capacity to run open source models. Groq is primarily targeting enterprise customers that require low latency for their applications, such as financial services and customer service tools. Ross believes that the winners and losers of the AI application layer have not yet been resolved, and that the industry is still in the early stages of development, with many advancements yet to be made. He also discusses the personal motivations behind developing Groq and the company's unique positioning in the market. Groq has designed its own chip and is focused on offering faster and more efficient services to customers, rather than directly competing with companies such as Nvidia or AMD. Bullet Summary: - The company's chip is faster and more efficient than traditional GPUs - Groq offers API services for enterprise customers requiring low latency applications - Jonathan Ross, CEO and founder of Groq, previously developed the TPU while working at Google - The AI industry is still in its early stages and has many advancements yet to be made - Groq has a unique positioning in the market and focuses on offering fast and efficient services to customers rather than competing with other companies. ------------------ P.S. If you still haven't jumped into AI yet, just get started. There are no rules. I have nearly 700 chats with OpenAI now from starting 12 months ago, and I'm trying apps like create.xyz, relume, big-AGI, Librechat, Groq, and more. Just follow your curiosity and take deep dives into AI models and tech that interest you.
AI Chip Wars: LPUs, TPUs & GPUs w/ Jonathan Ross, Founder Groq
https://www.youtube.com/
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In the latest #MLPerf industry benchmarks, NVIDIA H100 Tensor Core GPUs took #AI training to new heights, setting six new performance records and delivering the highest performance on every training test. https://nvda.ws/47ranmI #HPC
Acing the Test: NVIDIA Turbocharges Generative AI Training in MLPerf Benchmarks
blogs.nvidia.com
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Yesterday was full of exciting AI news from Nvidia, Stability, and OpenAI. 🤖 Here's a quick overview & links to the releases👇 🔵 ChatGPT Gets Memory OpenAI has been experimenting with personalized memory for some time now. Finally, we are a step closer to a real personal AI assistant. 🧠 The new feature allows ChatGPT to filter important and recurring information into a separate memory databank that travels between chats. So, basically, it's dynamic custom instruction on steroids. Technically, it's probably a simple agent connected to a vector databank - nice QoL feature. 🚀 It's currently tested with a small set of users. 🔵 Nvidia Taps into Local LLMs Small but powerful language models that run directly on your device have been on the rise lately. 🌐 These models not only keep your data private but also promise to be more personalized, faster, and integrated than cloud-powered AI. 📱 Now, Nvidia has launched an open-source app "Chat with RTX" that allows you to easily run a local model via their software - if you have an Nvidia graphics card. 🤷♂️ 🔵 Stability Releases Stable Cascade Stability AI has released their new Stable Cascade Model. The model is built on the Würstchen Architecture, allowing for a much smaller latent diffusion space. 🌭 This leads to way faster inference times than Stable Diffusion models and a quality and prompt adherence that can match Midjourney. 😎 LoRas and other add-ons also work with this model. The only downside: The model is open-source but not for commercial use like the previous ones. 🚫💰 Here are the links to the different Repositories & Downloads : Stable Cascade: https://lnkd.in/eY347m_h Chat with RTX: https://lnkd.in/egJTVT_E Chat with RTX normal download: https://lnkd.in/eCVe5r64 -------------------------------------------------------- Follow me if you like this content & hit the notification button to never miss a post. 🔔 In the coming days we'll dive deeper into the different technologies. If you already have a question just DM me. 🗞
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New article up on medium! 📝 This time a quick overview of some common techniques for optimizing the inference time of your machine learning model. It includes: 📌 Converting to #ONNX for up to easy but significant performance gains 📌 Using NVIDIA's #TensorRT to optimize models on GPUs (2-3X speed increase in our benchmark) 📌 Choosing the right hardware: standard CPUs, NVIDIA GPUs for parallel processing, or Graphcore IPUs specialized for ML? https://lnkd.in/eGRkJ2RR UbiOps - powerful AI model serving & orchestration
How to optimize the inference time of your machine learning model
medium.com
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CXL may have limited scope with AI training workloads using GPU with HBM.
CXL a no-go for AI training – Blocks and Files
https://blocksandfiles.com
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Discover the power of L40S GPU for AI and 3D Rendering. Check out our Medium article to learn more about this game-changing innovation in high-performance computing: https://lnkd.in/ejr9jkse #AI #3DRendering #ML #GPU #HPC
L40S: Blazing Speeds for AI & 3D Rendering
medium.com
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Achieve up to 4X increase in inference throughput with 3X memory reduction by optimizing AI model training for Arm Ethos-U NPUs using the NVIDIA Embedded TAO Toolkit. 👉 Intrigued? You may also want to check out our upcoming webinar to explore how you can leverage pre-trained models from NVIDIA TAO on edge devices with Edge Impulse: https://lnkd.in/eaEYrWXw
Optimizing AI for Arm Ethos-U Using NVIDIA TAO Toolkit
community.arm.com
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