Ben (Xiaojun) Li

Keeping your data close to the GPUs is essential, so we offer VAST Data as part of our #GPUasaService AI Platform. Let us help you #StepontheGaaS and move your #AI strategy forward. #Fast #Secure #Private Dominic Ken Michael Bill Traci

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🚀 𝗥𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗶𝘇𝗶𝗻𝗴 𝗔𝗜: 𝗔𝗳𝗳𝗼𝗿𝗱𝗮𝗯𝗹𝗲 𝗚𝗣𝗨 𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 𝗶𝘀 𝗛𝗲𝗿𝗲! 🖥️👁️ 🌟 The future of cost-effective GPU cloud computing has arrived, transforming how we train and deploy ML and computer vision models. Check out these game-changing startups offering incredibly affordable GPU solutions: 1️⃣ RunPod 🏃‍♂️💨 Empowering developers with on-demand GPU instances. RunPod's flexible pricing and user-friendly interface make high-performance computing accessible to all. 2️⃣ Vast.ai 🌊🧠 Pioneering a unique marketplace for GPU rentals. Vast.ai connects GPU owners with AI researchers, creating a win-win ecosystem for affordable compute power. 3️⃣ Lambda ☁️🔬 Offering turnkey GPU cloud solutions tailored for AI workloads. Lambda Labs' pre-configured environments streamline the development process for data scientists. 4️⃣ Paperspace 📄💻 Providing a seamless platform for GPU-accelerated applications. Paperspace's intuitive tools and collaborative features make it a favorite among AI teams. Paperspace is now part of DigitalOcean 5️⃣ CoreWeave 🔧☁️ Specializing in large-scale GPU deployments for enterprise clients. Coreweave's cutting-edge infrastructure supports the most demanding AI and rendering tasks. 6️⃣ Genesis Cloud ☁️🌱 Focusing on sustainability in GPU cloud computing. Genesis Cloud combines eco-friendly practices with high-performance GPU instances for guilt-free AI development. 7️⃣ FluidStack💧🥞 Harnessing idle GPUs worldwide to create a distributed supercomputer. Fluidstack's innovative approach offers cost-effective solutions for resource-intensive tasks. 8️⃣ Vultr 🚀💪 Delivering GPU-powered virtual machines with global reach. Vultr's extensive network of data centers ensures low-latency access to GPU resources worldwide. 9️⃣ JarvisLabs.ai 🤖🧪 Crafting AI-optimized cloud environments for researchers. Jarvis Labs' specialized tools and support accelerate breakthroughs in machine learning. 🔟 Banana 🍌💻 Simplifying ML model deployment with serverless GPU infrastructure. Banana.dev's user-friendly platform makes AI accessible to developers of all skill levels. These innovative companies are democratizing high-performance GPU computing, making it available to everyone from solo developers to large enterprises! 💪💼With prices that'll make your wallet smile 😄💰 and performance that'll blow your mind 🤯 #AIML #ComputerVision #AIStartups #CloudComputing #gpu #GPUPower Image credit : nvidia.com

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Getting started with AI can seem daunting, especially when contemplating its safety against data privacy. But past that fear lies a fix for itself: AI tools can be harnessed to overcome cybersecurity hurdles. Join us for an insightful discussion at the intersection of security, building resilience, and emerging technologies like AI, IoT, and others such as operational technology, and products intersecting with information technology and engineering. Get inspired, learn something new, and connect with peers, partners, and Googlers!

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That’s why we never ask for business plans, 3-5 year financial projections or similar documents in the early days! Who are the founders, what makes them special and how big is the market, that’s it 👌 Beats any “business plan”, business model canvas, or any of the Strategyzer alternative documents IMHO

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🌟 Exploring the Intersection of Business and Technology re AI! 🌟 Before my decade-long focus on AI, connecting technology and business in various domains has always been my passion; This is why I enjoyed this conversation with David Luan - No BS, No Twitter exaggerations, No clickbate - Just a super well informed, super smart David sharing his insights from years of hands-on experience: #Startegy, #Software, #Hardware, #Platforms, #ModelSpace and everything in between - LMK what you think of his projections. In This Episode: 1. The Biggest Lessons from OpenAI and Google Brain: What did OpenAI realize that no one else did that allowed them to steal the show with ChatGPT? Why did it take 6 years after the introduction of transformers for ChatGPT to be released? What are 1-2 of David’s biggest lessons from his time leading teams at OpenAI and Google Brain? 2. Foundation Models: The Hard Truths: Why does David strongly disagree that the performance of foundation models is at a stage of diminishing returns? Why does David believe there will only be 5-7 foundation model providers? What will separate those who win vs those who do not? Does David believe we are seeing the commoditization of foundation models? How and when will we solve core problems of both reasoning and memory for foundation models? 3. Bunding vs Unbundling: Why Chips Are Coming for Models: Why does David believe that Jensen and Nvidia have to move into the model layer to sustain their competitive advantage? Why does David believe that the largest model providers have to make their own chips to make their business model sustainable? What does David believe is the future of the chip and infrastructure layer? 4. The Application Layer: Why Everyone Will Have an Agent: What is the difference between traditional RPA vs agents? Why are agents a 1,000x larger business than RPA? In a world where everyone has an agent, what does the future of work look like? Why does David disagree with the notion of “selling the work” and not the tool? What is the business model for the next generation of application-layer AI companies? https://lnkd.in/dUpWWzvN

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UIUC/Georgia State create LlamaF, an architecture with FPGA-based accelerationdesigned to improve the inference performance of large language models (LLMs) such as TinyLlama 1.1B on embedded systems. As LLMs transform natural language processing across multiple sectors, their implementation on resource-constrained devices presents challenges due to significant memory and computational requirements.   LlamaF addresses these challenges through post-training quantization and an architecture specifically optimized for embedded FPGAs. By employing a group-wise quantization strategy, it reduces the model size from 4.4GB to 1.1GB, which lowers off-chip memory bandwidth needs while maintaining predictive performance. The architecture includes a fully pipelined accelerator for group-wise quantized matrix-vector multiplication (GQMV), allowing for asynchronous computation during weight transfers and resulting in notable performance enhancements.   Testing on the Xilinx ZCU102 platform highlights LlamaF's capabilities, achieving a speedup in inference speed ranging from 14.3 to 15.8 times and a 6.1 times improvement in power efficiency compared to executing the model solely on the ZCU102 processing system. This performance enhancement is largely attributed to the efficient execution of matrix operations, which are a significant portion of the runtime during inference.   The design of LlamaF integrates advanced software techniques, including task-level scheduling that overlaps off-chip parameter transfers with kernel execution, thereby improving throughput and reducing latency. The hardware design is organized into three primary stages: pre-processing, dot-product, and accumulation, which together facilitate efficient data flow and computation for LLM inference.   LlamaF signifies a notable advancement in the acceleration of LLMs on embedded FPGAs, enabling more effective deployment of these sophisticated models in environments with limited resources. Arxiv: https://lnkd.in/exhriYyj

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Ever heard the one about the company that built a trillion-dollar business from "zero billion" products? 🤯 Jensen Huang shared mind-blowing insights on the Acquired episode! 🎧 Key Takeaways: - Spotting the "zero billion" opportunities: Recognize hidden gems with massive potential. Intuition is key to seize the opportunity. - Leading is hard: In traditional enterprises, information flows top to bottom, causing delays. Nvidia everyone gets the information at the same time so leading is hard. - Building a company like a computer: Organize teams like a system, with each layer contributing to the whole. This episode is a goldmine for leadership, tech, and business brilliance enthusiasts! #NVIDIA #leadership #innovation #AcquiredFM Listen here: https://lnkd.in/gWcyYexE

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3 Key Insights You Might Have Missed from #RaySummit 2024 🌟 🎥 Catch #theCUBE’s full coverage: https://lnkd.in/g8ZtnnAk  #GenAI is rapidly reshaping the enterprise landscape, and companies such as Anyscale are leading the charge. Anyscale Ray is simplifying #AIInfrastructure, making it easier for businesses to scale efficiently, according to experts interviewed during Ray Summit 2024. Here are three important takeaways: • AI infrastructure complexity: Anyscale Ray is helping businesses streamline AI adoption by simplifying the fragmented ecosystem of tools and platforms. This means faster innovation and better outcomes for AI projects. • Uber’s scalable GPU clusters: By leveraging Ray, Uber has significantly upgraded its AI capabilities, running over 20,000 model training jobs every month — something that was nearly impossible before. • Runway AI’s generative filmmaking revolution: Runway is using Ray to power groundbreaking tools, like video-to-video transformations, that are changing the way visual effects and film production are done. 🤓 Catch the full interviews and more highlights from #RaySummit 2024. #EnterpriseTech #ScalableAI #CXOinsights #CloudComputing

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Last chance to join our webinar TODAY on RayTurbo! RayTurbo delivers improved performance, cost, and efficiency for workloads ranging from data processing to model training and model serving. Come learn why RayTurbo is the leading option for Ray users to supercharge their AI initiatives. https://lnkd.in/egfumWWM

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How do AI software engineering agents work? No better place to start than "at the source:" the state-of-the-art open source AI coding agent: SWE-bench has quietly become the industry standard AI coding agent benchmark. The team behind SWE-bench built SWE-agent (at release, state-of-the-art open source AI agent) and with Ofir Press (a member of the team building SWE-bench and SWE-agent), we dive deep on how it works: Read the deepdive here: https://lnkd.in/e3Ha39Rz

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Really interesting to see this: how Europe is moving up. It is interest to contrast this qualitative survey with the #siliconcatalyst analysys that UK is sixth in Europe in funding value fir silicon investment. And I will put in a plug for our #UKTIN Forward Capabilities paper on recommendations for UK Semiconductor sector: https://lnkd.in/eNNAsCAz

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Leadership Insights from Nvidia's Jensen Huang From a recent conversation between Patrick Collison (Stripe) and Jensen Huang (Nvidia). Here are the game-changing takeaways: 1️⃣ Craft & Tenure: Great products come from long-standing teams. Their encoded knowledge creates magic. Sridhar Vembu of Zoho reiterates this culture too, and ZOHO has one of the lowest attrition, which we aspire to at Jarvislabs. 2️⃣ AI as Industrial Revolution: Imagine a world with negligible cost of intelligence. It's happening now! The cost of tokens/intelligence is dropping. 3️⃣ AI Engagement: Every company must embrace AI or risk obsolescence. 4️⃣ Open Culture: Huang's 60 direct reports and no private 1:1s. Information equality drives innovation. 1:1 makes people privileged, we maintain 99% transparency at Jarvislabs. Key Quote: "While operational excellence produces good products, truly great products come from the 'craft' of long-term teams." 👉 How are you leveraging AI in your work? Have you experienced the power of long-term collaboration?

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Nvidia Publishes A Competitive Llama3-70B QA / Retrieval-Augmented Generation (RAG) Fine-Tune Model Llama3-ChatQA-1.5-8B and Llama3-ChatQA-1.5-70B are the two versions of this state-of-the-art model that come with 8 billion and 70 billion parameters, respectively. These models, which were first trained with Megatron-LM, have been converted to the Hugging Face format for accessibility and convenience. Building on the success of ChatQA, a family of conversational QA models with performance levels comparable to GPT-4, Llama3-ChatQA-1.5 was developed. ChatQA greatly improves zero-shot conversational QA outcomes with Large Language Models (LLMs) by introducing a unique two-stage instruction tweaking strategy. Full read: https://lnkd.in/guPxrkjX Llama3-ChatQA-1.5-8B: https://lnkd.in/gzfRhXPr Llama3-ChatQA-1.5-70B : https://lnkd.in/gh42ji9y #artificialintelligence #rag #llms #ai NVIDIA NVIDIA AI

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The Cerebras IPO looking to break up the NVIDIA monopoly might be successful in the short-term for an IPO pop, but long term will likely fail. NVIDIA’s dominance doesn’t come just from its chip but from the CUDA software that is the de facto standard and can take advantage of chip architecture improvements. No wonder why 87% of Cerebras’ revenue is from the Middle East. The people buying it aren’t the engineers using it. https://lnkd.in/ePRqdFpt

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Just witnessed the most epic impromptu panel discussion take-over by none other than John Chen (VP of Technology at NVIDIA) at Monte Jade West Science & Technology Association talk with Brandon Wang, Wen H. Hsieh, Ph.D., Jay Lee, Achin Bhowmik and Wei Li: GPUs are far ahead of all the rest of the infrastructure behind AI/LLM datacenters. It’s time to invest in the next frontier of AI infrastructure folks! Not just Cloud compute, hybrid cloud+edge, networking, power, edge RL, edge inference and edge firmware orchestration just to name a few areas we’re excited about!

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