Access to computing power is a major challenge in AI, particularly for academic researchers. A recent survey showed that 66% of scientists across 35 institutions report dissatisfaction with their resources due to budget constraints. Phison’s aiDAPTIV+ bridges this gap, making AI fine-tuning easy and affordable for SMBs: 1️⃣ Cost-Effective: Avoid cloud fees with an on-prem solution. 2️⃣ Secure: Keep data in-house. 3️⃣ Simple Setup: Train large models with just a few GPUs. Check out the article highlighting the computing gap: https://vist.ly/3mp2pjn Learn more about aiDAPTIV+: https://vist.ly/3mp2pjk #Phison #aiDAPTIVPlus #AI #SMBs #Tech #Innovation #LLMTraining
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🚀🚀 FEDML has added an exciting set of new GenAI models, including: 🖥️ Two tiny LLMs with great performance (Gemma 2B by Google and Phi-2 by Microsoft). We have users already deploying them at the low-end GPU devices (e.g., NVIDIA Orin) using FEDML Nexus AI; 🏥 A large LLM tailored for healthcare (Meditron-70B by researchers from EPFL); 🖼️ A fun image synthesizer (InstantID by InstantX) that is great at fast zero-shot identity-preserving image generation. You can now use these models on FEDML Nexus AI platform to: 🗝️ access via high-quality APIs; ☁️ create a dedicated deployment on the FEDML cloud, your desired public/private cloud, on-premise servers, or edge devices (for tiny LLMs), with autoscale and detailed observability support. 🤖 create powerful AI agents with them in seconds with a built-in RAG and API integration support; 🛠️ fine-tune them on your own data. Have fun building here: https://lnkd.in/g9cnBaxn #FEDML #LLMs #GenAI #MLOps
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Global Product Marketing Director @ MEMPHIS Electronic | Product Marketing, Business Development, Head of Memory Competence Center
🔹 #AI is the fastest-growing workload inside both cloud and enterprise data centers. Driven by large language models (#LLMs). 🔹Two types of AI – #training and #inference. 🔹#Training consists of teaching the model how to manipulate the data. 🔹#Inference is the use of the model for your AI needs in real world applications. 🔹Most chips are used in the inference AI – about 82% of them in 2024 and increasing to 89% in 2029. Source: #Omidia
🔹 #AI is the fastest-growing workload inside both cloud and enterprise data centers. Driven by large language models (LLMs). 🔹Two types of AI – training and #inference. 🔹Training consists of teaching the model how to manipulate the data. 🔹Inference is the use of the model for your AI needs in real world applications. 🔹Most chips are used in the inference AI – about 82% of them in 2024 and increasing to 89% in 2029. Source: Omidia #semiconductor #Nvidia #amd #intel #server #datacenter
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25K AI + DC Community | AI Data Centers Brand Ambassador | On a Mission Building Next Gen Digital Infrastructure | AI-Ready Data Centers | AI Compute | GPU Cloud | AI Cloud Infra Leader | Hyperscalers| AI/HPC Solutions
ONGOING SAGA: HOW MUCH MONEY WILL BE SPENT ON AI CHIPS? Everybody knows that companies, particularly hyperscalers and cloud builders but now increasingly enterprises hoping to leverage generative AI, are spending giant round bales of money on AI accelerators and related chips to create AI training and inference clusters. But just you try to figure out how much. We dare you. The numbers are all over the place. And that is not just because it is tough to draw the lines that separate AI chippery from the stuff surrounding it that comprises a system. Part of the problem with estimating AI market size is no one really knows what happens to a server once it is built and sold and what it is used for. How do you know, for instance, how much AI work or HPC work a machine loaded up with GPUs is really doing? https://lnkd.in/gZe7aVDR
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Introducing the new Azure AI infrastructure VM series ND MI300X v5. Industry-leading high-bandwidth memory (HBM) capacity and bandwidth targeting generative inferencing and AI training Artificial intelligence is transforming every industry and creating new opportunities for innovation and growth. On top of this, AI models are continually advancing and becoming more complex and accurate. More powerful computers with purpose-built AI accelerators that have resources like high bandwidth memory (HBM), specialized data formats, and exceptional compute performance are needed to fuel these technological advances. To meet this need, Azure... #techcommunity #azure #microsoft https://lnkd.in/g9iYb_Uy
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Lead Research Data Scientist | Expert in Generative AI | Postgraduate in AI & ML @IIIT-B | Ex-IBM, Ex-Wells Fargo, Ex-HP
Want to Increase LLM Throughput? Here’s the Solution! Looking to boost your AI inference capabilities? AMD’s MI300X might be the answer. Key Highlights: 👉 Streaming Performance: At a target average latency of 5 seconds, two MI300X with tp=1 service 33% more requests per second than two H100s with tp=2. Fewer accelerators, same quality of service. 👉 Real-World Benchmark: With streaming enabled, MI300X delivers higher throughput for every TPOT compared to H100 and Groq. This is essential for handling higher traffic volumes in LLM applications. Comparison: 👉 MI300X vs. H100: The MI300X offers 33% more requests per second with fewer accelerators, ensuring cost efficiency and high performance. 👉 MI300X vs. Groq: While Groq shows strong performance in language-based tasks, it is limited in scope as it hasn't been implemented for vision-based tasks. The MI300X, however, offers higher throughput for every TPOT, making it a superior choice for generating text faster at higher traffic volumes and for fine-tuning and inferencing models according to specific business use cases. Conclusion: AMD’s MI300X outperforms both NVIDIA’s H100 and Groq in offline and online inference tasks for MoE architectures like Mixtral 8x7B. It offers higher throughput and faster response times, making it a top choice for enterprises aiming to scale AI inference efficiently. Source:https://lnkd.in/gJuUnajz #AI #GenAI #LLM #largelanguagemodel #llmthroughput
MI300X vs. H100 for LLM Inference | TensorWave
tensorwave.com
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Couldn’t make it to NVIDIA GTC 2024 in-person? Adrian Tineo, Ph.D., our AI Advisor at NexGen Cloud, will be presenting on-demand at #GTC2024's virtual event. Join his talk "Which GPU for Open Source #LLM Applications? Inference Performance Insights" to learn which GPU setup is right for you. Find out how to scale up and down your #OpenSource #AI workloads at Hyperstack, our on-demand GPU cloud platform, optimised for enterprise workloads and max performance efficiency. Don't miss out on valuable insights. Join us on-demand! #Hyperstack #GTC #ArtificialIntelligence #LLMs #LargeLanguageModels #Innovation #GenerativeAI #NLP #NVIDIA #GPU #Cloud #CloudComputing #GPUisWhatWeDo
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Experimenting with Stable Diffusion running on GPU Cloud. Masking (Inpaint) and img2img techniques. Base model in Rhino from M.Arch thesis project, Experiential Routine. #stablediffusion #ai #aiarchitecture
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Why does it matter where the physical infrastructure of AI is located when the computing power of AI can be accessed over the internet? It has to do in part with who gets to make the rules...
Tech policy professor at Oxford and Aalto. How economics and geopolitics shape cloud and AI. Digital Economic Security Lab DIESL.
Where in the world is AI located? Training and deploying AI systems requires lots of computing power. This "AI compute" is typically supplied by cloud providers like Amazon AWS using #Nvidia GPUs. In a new paper with Bóxī Wú and Zoe Jay Hawkins, we set out to map the physical infrastructures behind this cloud AI compute. We found that countries fall into three categories: 1. "Compute North": predominantly rich countries hosting data centres with the latest GPUs for training frontier AI models; 2. "Compute South": predominantly mid-income countries hosting older GPUs relevant for deploying but not training frontier AI; 3. "Compute Desert": countries with no cloud AI compute at all. We also found that China is hosting the greatest number of cloud AI compute regions, while the United States is hosting the latest and most powerful GPUs models. But cloud AI compute can be accessed remotely over the Internet -- so why does its location matter? Two main answers: 1. AI governance: countries on whose territory AI physically resides are in a strong position to set the rules that govern it. 2. Tech geopolitics: countries could use their territorial jurisdiction to cut off rivals' access to AI compute resources located on their territory. TIME published a story on our study today -- I'll post a link to the story and to the the peer-reviewed study in the comments. Oxford Internet Institute, University of Oxford Aalto University Jesus College Oxford #AI #cloud #AWS #Azure #AIAct
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Customer Data and AI @ Accenture Song, Ex- Deloitte Digital🔹 Board Member 🔹 University Lecturer 🔹 Former Epidemiologist 🔹 Startups Coach & Mentor 🔹 Invited Speaker and Writer
The locations of advanced AI chips are closely-held industry secrets. Location has significant implications: 1. Geopolitical competition 2. AI governance—which governments have the power to regulate how AI is built and deployed. This paper shows a map with a proxy for that information. The locations of individual "regions," or datacenter clusters containing GPUs, that are available for hire from the world's major cloud computing businesses.” #AI #AIgovernance #innovation #data #llmmodels #compute
Tech policy professor at Oxford and Aalto. How economics and geopolitics shape cloud and AI. Digital Economic Security Lab DIESL.
Where in the world is AI located? Training and deploying AI systems requires lots of computing power. This "AI compute" is typically supplied by cloud providers like Amazon AWS using #Nvidia GPUs. In a new paper with Bóxī Wú and Zoe Jay Hawkins, we set out to map the physical infrastructures behind this cloud AI compute. We found that countries fall into three categories: 1. "Compute North": predominantly rich countries hosting data centres with the latest GPUs for training frontier AI models; 2. "Compute South": predominantly mid-income countries hosting older GPUs relevant for deploying but not training frontier AI; 3. "Compute Desert": countries with no cloud AI compute at all. We also found that China is hosting the greatest number of cloud AI compute regions, while the United States is hosting the latest and most powerful GPUs models. But cloud AI compute can be accessed remotely over the Internet -- so why does its location matter? Two main answers: 1. AI governance: countries on whose territory AI physically resides are in a strong position to set the rules that govern it. 2. Tech geopolitics: countries could use their territorial jurisdiction to cut off rivals' access to AI compute resources located on their territory. TIME published a story on our study today -- I'll post a link to the story and to the the peer-reviewed study in the comments. Oxford Internet Institute, University of Oxford Aalto University Jesus College Oxford #AI #cloud #AWS #Azure #AIAct
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Technologist, ICT, Smart Cities, AI, Data Privacy, Cybersecurity, Ethical AI, Telecom, Real Estate, Mobile-Apps, Innovation.
𝗘𝘅𝗰𝗶𝘁𝗶𝗻𝗴 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗦𝘁𝗮𝘁𝗲 𝗼𝗳 𝗔𝗜 𝗥𝗲𝗽𝗼𝗿𝘁 𝗖𝗼𝗺𝗽𝘂𝘁𝗲 𝗜𝗻𝗱𝗲𝘅! This comprehensive index tracks the growth and utilization of public, private, and national HPC clusters, as well as the adoption of various AI chips in research papers. It serves as a vital indicator of the progress in AI systems.🔍 Key Highlights: • Public Cloud: Capacity rented from hyperscalers. • Private Cloud: Owned and operated by companies. • National HPC: Government-owned infrastructure. Understanding these trends helps us gauge the rapid advancements in AI technology. Dive deeper into the data to see how leading companies and startups are shaping the future of AI. #AI #HPC #Innovation #Technology #Research #ArtificialIntelligence #NVIDIA #Meta #Google https://lnkd.in/dh3ssbsG Thanks to Nathan Benaich Founder & General Partner Paula Pastor Castaño Advisor, Legal & Operations Alex Chalmers Platform
State of AI Report Compute Index
stateof.ai
