Arslan Ahmad’s Post

Some important applications of Llama 3.1 💥 Llama 3.1 has: 💨 Light-weight model with 8 billion parameters 💣 Highly performant model with 70 billion parameters 💥 Flagship foundation model with 405 billion parameters With Llama 3.1, you can perform the following: 1. Supervised fine-tuning 2. Model evaluation for different use cases. 3. Continual pre-training 4. Retrieval-Augmented Generation (RAG) 5. Function calling 6. Synthetic data generation (SDG) 7. Model batch inferencing; and lots more To use an enterprise-ready llama 3.1 model for synthetic data generation: 📌 Try Nvidia NIM API at https://build.nvidia.com To perform AI inferencing, go to groq @ https://groq.com/ Learn more about llama 3.1 at https://lnkd.in/dBwXUrr2 #ai #aiintegration #modelinferencing #aiupdate #aiopensource

Sure you have endlessly heard about NVIDIA in regard to the AI computational offering. I'm so surprised that there has been so little said about how that data is accessed for that computational processing. There is no other solution on the market like Pure Storage that meets the AI data requirements for: ease of use, speed, data access/sharing, cost effectiveness, and reliability. Two short videos that are very valuable on achieving those requirements for AI and reaching your desired outcome of building a modeling and latency of data delivery to your customers. Hear the scoop directly from NVIDIA's VP of Global Solutions. https://lnkd.in/ezH6GaJC Hear some of the details from Pure Storage's Global Practice Lead. https://lnkd.in/ezUhJQZD

NVIDIA's New Model Shows Fascinating Edge in Basic Tasks! Here's something that made me do a double-take today... While testing NVIDIA's new Llama-3.1-Nemotron-70B-Instruct model, I noticed something really interesting about how it handles basic tasks compared to GPT-4 and Claude 3.5 Sonnet. Take the word "strawberry" • GPT-4 and Claude: Count 2 'r's • NVIDIA's new model: Correctly counts 3 'r's Let me share some impressive benchmarks where NVIDIA's model shows outstanding performance: • 85.0 on Arena Hard • 57.6 on AlpacaEval 2 LC • 8.98 on GPT-4-Turbo MT-Bench These numbers showcase why this new model is making waves in the AI community! #AI #MachineLearning #TechInnovation #FutureOfTech  The model is not yet available for regular use but here you can test it's capabilities. 📂 https://lnkd.in/d3fEkVDB 📁 source: https://lnkd.in/dduy4k9k

🚀 Speeding Up Large Language Models: Inference Made Faster! Ever wondered how to make your LLMs run even faster? 🤔 We've got you covered! Our new method tackles the challenge of reducing model inference latency during distributed deployment. Here's what makes it special: ☑️ Optimized deployment scheme ☑️ Preserves data locality in GPU memory access ☑️ Leverages TP for reduced communication This approach delivers impressive results, achieving up to 1.81x speedup for Llama-70B and 1.78x speedup for IBM WatsonX's Granite-20B MLP layer on A100 and H100 NVIDIA DGX Systems! 🤯 Takeaway: Don't let latency slow you down! Our method empowers you to accelerate LLM inference, paving the way for more efficient and powerful AI applications. Check out the full paper here: https://lnkd.in/gz7xCZf8 #TP-AwareDequantization, #Quantization, #Dequantization, #DeepLearning, #MachineLearning, #AI, #ResearchPaper, #ComputerScience, #DataScience, #NeuralNetworks, #LTI, #ltimindtree, #genai, #generativeai, #aiml, #trends

What happens when the output of one LLM is used as training data for another LLM? It turns out that models begin to lose the sense of the original data distribution, and as this process is repeated (e.g. GPT-{n}, where n = n + 1), the model eventually collapses. Last week, we saw a significant study on Model Collapse, "AI models collapse when trained on recursively generated data" (https://lnkd.in/dRXJb2MA), which described the problem in depth. Around the same time (June 12th, 2024), NVIDIA published another fascinating piece of research (https://lnkd.in/dYNdcjST) on SDG (Synthetic Data Generation), demonstrating the ability to produce high-quality synthetic data. Now, I'm curious if we could prevent or delay model collapse by using high-quality synthetic data generated by the recently announced NVIDIA Nemtron-4 340B family of models, which includes a state-of-the-art reward model and an instruct model to aid in SDG. imo., this could be a great follow-up study! UPD: I reference the wrong link for the model collapse study 🤦♂️. It is now being corrected: https://lnkd.in/dRXJb2MA #ai #llms #genai #modelcollapse #syntheticdatageneration

🚀 **Nvidia's Llama-3.1-Nemotron-70B-Instruct is Outperforming GPT-4o!** 🚀 Nvidia's latest AI model has quietly launched on Hugging Face, making a huge impact by surpassing OpenAI's GPT-4o and Anthropic’s models on key performance benchmarks. This revolutionary model is setting a new standard for alignment and efficiency, proving to be a real game-changer in the AI landscape. 🔑 **Key Insights:** - Nvidia’s Llama-3.1-Nemotron-70B-Instruct beats GPT-4o across multiple benchmarks. - The model’s breakthrough in user alignment and task performance is reshaping the AI industry. - Nvidia is making strategic moves into AI software, directly challenging leading competitors. 📈 **Why This Matters:** Nvidia is pushing the boundaries of AI, leveraging their hardware expertise to create cutting-edge software that’s outperforming industry giants. This shift signals the need for faster innovation, bringing a new era of AI development with massive implications for businesses and developers alike. #AI #Nvidia #Innovation #MachineLearning #GPT4 #HuggingFace #ArtificialIntelligence #TechLeadership #FutureOfAI

Big things are happening—800 #4090GPUs are almost ready to power your next AI project! If you’re looking to push the boundaries of performance and take on more complex tasks, these GPUs are up to the challenge. But that’s not all—we also have access to a supply of #H100GPUs, ready to fuel your next big leap in AI and machine learning. These high-performance GPUs are designed for innovation, helping you achieve more in less time, whether you're working on deep learning, research, or any cutting-edge tech. We’re excited to help you take your AI capabilities to the next level with the latest in GPU technology! #AI #MachineLearning #TechInnovation #GPU #DeepLearning #AIInfrastructure #NextGenTech #HighPerformanceComputing #ArtificialIntelligence #AIResearch Conduit Network

Interesting approach with MInference, but how do we ensure it doesn't trade off too much in terms of accuracy? The balance between speed and precision is critical. Have you tested this method on real-world, production-level tasks or only on benchmarks? Also, how does it handle edge cases where traditional methods fail? A 10x reduction in latency is impressive, but without knowing the edge cases' performance, it's hard to fully embrace this solution. What about compatibility with future hardware developments? #LLMinference #AIefficiency #SparseComputation

🔎 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐢𝐧𝐠 𝐌𝐈𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐟𝐨𝐫 𝐋𝐨𝐧𝐠-𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐋𝐋𝐌𝐬 🔎 𝐓𝐡𝐞 𝐭𝐰𝐨 𝐦𝐨𝐬𝐭 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐜𝐨𝐧𝐬𝐢𝐝𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐀𝐈: 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 𝐚𝐧𝐝 𝐬𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲 In their groundbreaking new paper “MInference 1.0: Accelerating Pre-Filling for Long-Context LLMs via Dynamic Sparse Attention,” Microsoft and the University of Surrey address the most vital challenge in LLM inference acceleration—without sacrificing accuracy. This new technique decreases latency by as much as 10x on a single A100 GPU, helping to make sophisticated AI apps feasible and available to the masses. Read the full research here -> https://lnkd.in/egC2MD2y #ai #LLM #generativeai

Llama 3.1 is out! This massive 405b parameter LLM was trained on 16k H100’s and has been optimized for blazing fast inferencing using Nvidia TensorRT LLM.