Sai kumar Reddy N’s Post

Groq has developed an AI chip designed to significantly speed up the performance of Large Language Models (LLMs), achieving up to 500 tokens per second, far surpassing the typical 30-50 tokens per second by existing models. This "GroqChip" utilizes a tensor streaming architecture for enhanced speed and efficiency, making it particularly suitable for real-time applications. Additionally, its design allows for scalability and reduced energy consumption, positioning it as a potential alternative to prevalent Nvidia chips, although it's initially focused on inference tasks rather than model training. #Groq #AIchip #LargeLanguageModels #LLMs #GroqChip #tensorstreamingarchitecture #realtimeapplications #Nvidia

It´s all about time to market. Check out how to deploy NVIDIA Inference Microservices (NIMs). The setup from scratch to interacting with an LLM took me below 2hrs... #NVIDIANIMs #NVIDIAWorkbench

NVIDIA's GTC 2024 marked a significant leap in the AI revolution. New Blackwell GPU series and DGX systems promise up to 30x performance increase for AI workloads. The event also showcased genAI-native software for diverse applications and strategic partnerships. NVIDIA's shift from GPU to AI solutions positions it as a formidable enterprise player.</div><div class="read-more"><a href="" class="more-link">Continue reading</a>https://lnkd.in/g9hUfXB4

Ever wished you could run CUDA code on Nvidia GPUs without proprietary constraints? 🤔 Enter LibreCUDA! Dive into open-source freedom and power up your AI projects like never before. Discover more here: https://lnkd.in/gUmthwGz 🚀 #OpenSource #AIRevolution

NVIDIA NIM takes all the complexity of deploying AI models in production environments so it can be done in just a few minutes. Check out how to deploy these optimized containers in under 5 minutes. #AIinference #NVIDIANIM #generativeAI

Enabling Nvidia GPU in Red Hat Openshift AI on OpenShift Cluster

Lesson's from Jensen Huang, Founder & CEO, NVIDIA for all of us!! 1️⃣ 𝐆𝐨 𝐬𝐨𝐥𝐯𝐞 𝐡𝐚𝐫𝐝 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬 - Most people go after easy problems. It takes a long time to solve a hard problem, but the rewards are going to be extremely high. 2️⃣ To solve a hard problem, 𝐭𝐡𝐢𝐧𝐤 𝐨𝐮𝐭𝐬𝐢𝐝𝐞 𝐭𝐡𝐞 𝐛𝐨𝐱. Existing systems or solutions might not be applicable. You might have to create a new paradigm shift in the way we operate to solve this problem. (like how nvidia developed not just the gpu - but cuda & helped AI revolution) #ai #gpu #nvidia #lessons #startup #investing https://lnkd.in/gciyjFRS

"Chat with RTX uses retrieval-augmented generation (#RAG), #NVIDIA TensorRT-LLM software and NVIDIA RTX acceleration to bring generative AI capabilities to local, GeForce-powered Windows PCs. Users can quickly, easily connect local files on a #PC as a dataset to an open-source large language model like #Mistral or #Llama 2, enabling queries for quick, cocontextually relevant answers." source: #nvidia https://lnkd.in/eke6ppec 💎💻 #Heruka_AI_Consulting offers its expertise in the transition to AI for corporate strategy and information for social and technological player

Today I successful got the Gemma LLM from Ollama downloaded successfully locally to my NVIDIA Jetson GPU. The goal is to create a complete AI Agent that can use the files on my gpu to execute tasks and scripts without the use of internet or APIs. This can be helpful in many use cases that you want a model to provide suggestions in a closed environment. #threatintelligence #aiagents

Here is the reason why AGI is arriving on a faster timetable.  Nvidia is outperforming Moore’s Law.  OpenAI’s first Nvidia GPU stack had .17 Petaflops.  We are going from fractions of a Petaflop to Exaflops.  1 Exaflop = 1,000 Petaflops Today, there are a handful of Exaflop supercomputers globally.  Now, Nvidia is selling Exaflop systems.  Two more years and those Exaflop systems will look obsolete.  The main constraint is having enough data to train these massive trillion parameter models.  Imagine an AI trained on all YouTube videos, the Library of Congress, and the World Wide Web.  Who wins that game? Long: Google