Patrick Burke’s Post

Two components form the foundation of strength in the AI sector for any organization: Data and compute. If you’ve got lofty goals to ride the AI wave, this is where you should focus. Chasing the latest algorithm, architecture, or research idea is generally a distraction in the long run. That’s not to say those things aren’t important; they certainly are. It just means that you’d be better off allocating your resources to the former two items if you truly want to compete. This has already been the name of the game with today’s AI heavy hitters, from OpenAI to Tesla Autopilot. What we’ve learned is that there are multiple ways to skin the cat, but they all generally need lots of data and lots of compute. A large scale, robust method of gathering data that your competitors can’t will likely position you better than a whole department of AI researchers. That might sound obvious to some, but unfortunately with all the glitz and glamor around the field many tech leaders don’t grasp this.

"Training GPT-3 is estimated to have required over 10^23 floating point operations in total—that’s a number with 23 zeros after it! Only massively parallel GPUs clustered together can handle this scale of computation in any reasonable timeframe. This results in an estimated carbon footprint of over 626,000 pounds—nearly 5 times the emissions of the average American car’s lifetime. // Also, research suggests that training GPT-3 in Microsoft’s U.S. data centers can directly consume 700,000 liters of clean freshwater, enough for producing 370 BMW cars or 320 Tesla electric vehicles, and these numbers would have been tripled if GPT-3 were trained in Microsoft’s Asian data centers." — AINow Institute ("The Climate Costs of Big Tech") & Pengfei Li, Jianyi Yang ("Making AI Less 'Thirsty'"). The sustainability of an AI cloud provider is crucial in today's world, where the environmental impact of large-scale AI operations cannot be ignored. As enterprises increasingly invest in AI, it's essential to choose a provider that aligns with their ESG goals. Soluna Cloud is the right choice for enterprises committed to sustainability while leveraging the immense benefits of AI. Our AI cloud is powered by renewable energy, significantly reducing the carbon footprint associated with AI training and operations. By choosing Soluna Cloud, you ensure your AI initiatives support your sustainability goals, not hinder them. Protect your ESG commitments and unleash the power of AI with Soluna Cloud. Visit SolunaCloud.com to learn more. #AI #DeepLearning #Innovation #Sustainability

"Training GPT-3 is estimated to have required over 10^23 floating point operations in total—that’s a number with 23 zeros after it! Only massively parallel GPUs clustered together can handle this scale of computation in any reasonable timeframe. This results in an estimated carbon footprint of over 626,000 pounds—nearly 5 times the emissions of the average American car’s lifetime. // Also, research suggests that training GPT-3 in Microsoft’s U.S. data centers can directly consume 700,000 liters of clean freshwater, enough for producing 370 BMW cars or 320 Tesla electric vehicles, and these numbers would have been tripled if GPT-3 were trained in Microsoft’s Asian data centers." — AINow Institute ("The Climate Costs of Big Tech") & Pengfei Li, Jianyi Yang ("Making AI Less 'Thirsty'"). The sustainability of an AI cloud provider is crucial in today's world, where the environmental impact of large-scale AI operations cannot be ignored. As enterprises increasingly invest in AI, it's essential to choose a provider that aligns with their ESG goals. Soluna Cloud is the right choice for enterprises committed to sustainability while leveraging the immense benefits of AI. Our AI cloud is powered by renewable energy, significantly reducing the carbon footprint associated with AI training and operations. By choosing Soluna Cloud, you ensure your AI initiatives support your sustainability goals, not hinder them. Protect your ESG commitments and unleash the power of AI with Soluna Cloud. Visit SolunaCloud.com to learn more. #AI #DeepLearning #Innovation #Sustainability

"Training GPT-3 is estimated to have required over 10^23 floating point operations in total—that’s a number with 23 zeros after it! Only massively parallel GPUs clustered together can handle this scale of computation in any reasonable timeframe. This results in an estimated carbon footprint of over 626,000 pounds—nearly 5 times the emissions of the average American car’s lifetime. // Also, research suggests that training GPT-3 in Microsoft’s U.S. data centers can directly consume 700,000 liters of clean freshwater, enough for producing 370 BMW cars or 320 Tesla electric vehicles, and these numbers would have been tripled if GPT-3 were trained in Microsoft’s Asian data centers." — AINow Institute ("The Climate Costs of Big Tech") & Pengfei Li, Jianyi Yang ("Making AI Less 'Thirsty'"). The sustainability of an AI cloud provider is crucial in today's world, where the environmental impact of large-scale AI operations cannot be ignored. As enterprises increasingly invest in AI, it's essential to choose a provider that aligns with their ESG goals. Soluna Cloud is the right choice for enterprises committed to sustainability while leveraging the immense benefits of AI. Our AI cloud is powered by renewable energy, significantly reducing the carbon footprint associated with AI training and operations. By choosing Soluna Cloud, you ensure your AI initiatives support your sustainability goals, not hinder them. Protect your ESG commitments and unleash the power of AI with Soluna Cloud. Visit SolunaCloud.com to learn more. #AI #DeepLearning #Innovation #Sustainability

🚀 Amazon's Bold Move in AI: Introducing Project Rainer with Anthropic 🌐 At the recent Re:Invent conference, Amazon showcased a groundbreaking endeavor: a mega AI supercomputer known as Project Rainer, developed in collaboration with Anthropic. Amazon is set to redefine the landscape of AI capabilities. Why is this important? 1. **Powerful Infrastructure**: Once finished, this supercomputer will be the world’s largest AI machine, equipped with hundreds of thousands of cutting-edge Trainium 2 chips. It aims to leap fivefold in capacity compared to current AI clusters. 2. **Affordable Innovation**: AWS's Trn2 UltraServer clusters are being marketed as 30-40% cheaper alternatives to Nvidia GPUs, extending Amazon's competitive edge in the cloud AI services market. 3. **Next-Gen AI Hardware**: Trainium 3, AWS’s next-gen training chip, promises a quadruple performance boost, paving the way for more efficient AI models. 4. **Generative AI Tools**: Amazon is not stopping at hardware. Enhancements like Model Distillation for leaner AI models and Bedrock Agents for managing AI agents are designed to make AI deployment more affordable and reliable. 5. **Automated Reasoning**: A new verification tool to improve accuracy in AI outputs is particularly critical for sectors that cannot afford errors, such as insurance and customer service. As competition heats up, Amazon's deepening partnership with Anthropic and their relentless focus on cloud-based AI infrastructure have the potential to disrupt the status quo dominated by giants like Nvidia. What’s your take on Amazon's strategy in AI? Could they be the next big player in reshaping artificial intelligence applications? Let’s discuss below! 👇 #AI #CloudComputing #Innovation #AmazonAI #FutureTech

Advancing Responsible AI with Gemma Open Models   Summary: Google introduces Gemma, a family of open models and tools, empowering developers worldwide responsibly.  ▪️Gemma 2B and 7B models were released with pre-trained and instruction-tuned variants. ▪️The Responsible Generative AI Toolkit facilitates safer AI applications with Gemma. ▪️Toolchains support inference and supervised fine-tuning across JAX, PyTorch, and TensorFlow. ▪️Ready-to-use Colab, Kaggle notebooks, and integration with popular tools streamline Gemma adoption. ▪️Gemma models deploy easily on laptops, workstations, Google Cloud, Vertex AI, and GKE. ▪️Optimisation for NVIDIA GPUs and Google Cloud TPUs ensures industry-leading performance. ▪️Terms permit responsible commercial usage and distribution for all organisations. Gemma achieves state-of-the-art performance, surpassing larger models on key benchmarks. Link to huggingface blog: https://lnkd.in/gWsV7AuX To know more: https://lnkd.in/ghDMxVd7 Join me on the cutting edge of responsible AI! Follow me to learn more about LLMs. Let's build a responsible AI future together! #AI #ResponsibleAI #GemmaOpenModels #Developers #MachineLearning #Innovation #llms

Elon Musk and the xAI team just announced the Colossus 100k H100 cluster is now online, proclaiming it the most powerful AI training system in the world.  Built in a record 122 days, could this rapid innovation be steering us toward a world where only a few AI platforms dominate? As big companies race to grab data from emerging marketplaces, we could end up with models that think alike and a market where competition is muted. On the other hand, we're seeing breakthroughs with smaller language models and smarter data pruning that could signal platforms focusing on specific insights rather than drowning in similar data. It reminds us of the industry rush to aggregate big data, only to realize the real power comes from turning oceans into data lakes. Wherever the AI landscape leads, it’s evolving quickly and will require expert partners, tools and technology to lead the way. Read more details about the Colossus launch here in the full Fortune article: https://lnkd.in/g9Uc2FGg #ElonMusk #Innovation #FutureOfAI #DataLakes

"Training GPT-3 is estimated to have required over 10^23 floating point operations in total—that’s a number with 23 zeros after it! Only massively parallel GPUs clustered together can handle this scale of computation in any reasonable timeframe. This results in an estimated carbon footprint of over 626,000 pounds—nearly 5 times the emissions of the average American car’s lifetime. // Also, research suggests that training GPT-3 in Microsoft’s U.S. data centers can directly consume 700,000 liters of clean freshwater, enough for producing 370 BMW cars or 320 Tesla electric vehicles, and these numbers would have been tripled if GPT-3 were trained in Microsoft’s Asian data centers." — AINow Institute ("The Climate Costs of Big Tech") & Pengfei Li, Jianyi Yang ("Making AI Less 'Thirsty'"). The sustainability of an AI cloud provider is crucial in today's world, where the environmental impact of large-scale AI operations cannot be ignored. As enterprises increasingly invest in AI, it's essential to choose a provider that aligns with their ESG goals. Soluna Cloud is the right choice for enterprises committed to sustainability while leveraging the immense benefits of AI. Our AI cloud is powered by renewable energy, significantly reducing the carbon footprint associated with AI training and operations. By choosing Soluna Cloud, you ensure your AI initiatives support your sustainability goals, not hinder them. Protect your ESG commitments and unleash the power of AI with Soluna Cloud. Visit SolunaCloud.com to learn more. #AI #DeepLearning #Innovation #Sustainability

Exciting News from Google: Introducing Gemma Open Models for AI Development! 🌟 Google has just unveiled Gemma, a new generation of lightweight, state-of-the-art open models built from the same research and technology used to create the Gemini models. This is a significant leap forward in responsible AI development, offering tools and model weights to support innovation, collaboration, and responsible use. Key highlights of Gemma: • Two sizes available: Gemma 2B and Gemma 7B, with pre-trained and instruction-tuned variants. • A Responsible Generative AI Toolkit to guide the creation of safer AI applications. • Toolchains for inference and supervised fine-tuning across major frameworks like JAX, PyTorch, and TensorFlow. • Integration with popular tools such as Hugging Face, MaxText, NVIDIA NeMo, and TensorRT-LLM. • Optimization across AI hardware platforms, including NVIDIA GPUs and Google Cloud TPUs. Gemma models are designed with Google’s AI Principles in mind, ensuring safety and reliability. They’re available for responsible commercial usage and distribution, making them accessible to organizations of all sizes. This is a fantastic opportunity for developers and researchers to leverage cutting-edge AI technology for responsible innovation. Check out more details and get started with Gemma. https://lnkd.in/eQUUUSQS #AI #MachineLearning #OpenModels #Gemma #ResponsibleAI #googleai

🚀 Nvidia, Test-Time Scaling, and the AI Revolution We’re All Living Through $19 billion in net income? Yeah, Nvidia had a good quarter. But the real fireworks? Jensen Huang’s comments about test-time scaling—the method behind OpenAI’s o1 model that’s changing the game for AI. Here’s the deal: test-time scaling allows AI to “think” better by using extra compute during the inference phase (you know, after you hit ‘enter’ on a prompt). Huang called it a “new scaling law” and one of the most exciting developments in AI. Why this matters: 🔍 AI inference—what happens after training—is becoming the main event. 💥 Startups like Groq and Cerebras are jumping into the race, but Huang says Nvidia’s scale and reliability give them the edge. 🔮 The goal? A world where inference drives AI innovation and makes the technology as common as electricity. Huang’s optimism matches what we’ve been seeing: AI is still improving with every new data set, every new chip, and every new experiment. If inference takes off the way he’s betting, we’re looking at a massive shift in how AI integrates into our world. But here’s the question I want to ask you: Is this the next big thing in AI, or just another chapter in the hype machine? 💡 Dive into the full article here: https://lnkd.in/dpy_V5FY Let me know what you think in the comments. #AI #Nvidia #Innovation #TestTimeScaling