Douglas Moore’s Post

How does the AI infrastructure for LLMs like GPT-4 and Llama-3 work under the hood? I decided to break it down for you when I came across the Series A fundraising announcement from Simplismart. While traditional API servers like FastAPI and Flask efficiently handle simpler tasks, deploying LLMs demands more advanced systems with custom-designed serving layers, such as Triton. This efficiently handles dynamic batching, ensemble modeling, and inference. The model-GPU interaction layer is where things get more exciting. Simplismart inference engine leverages optimised kernel operations, mapping matrix multiplications (MatMul) to the ideal GPU kernel for top-tier performance. Simplismart also stands out on the infrastructure front with impressive optimisations. Rapid scaling: Models are up and running on new nodes within 60 seconds—far quicker than the typical 5-6 minutes in traditional clouds. Sharding: Larger models (Llama3.1-70B and Llama3.1-405B) are deployed across multiple GPUs, effectively Sharding them to increase parallel processing. Please find the detailed link https://lnkd.in/gzUMcm62 Please let me if i missed anything in comments ?

In transit to NVIDIA #gtc2024 ! Come see how the VAST Data platform is accelerating AI/ML workloads. stop by booth #1424 for a demo and more!

🚨New Catalogue🚨The wait is over! 🎉 We present to you #Aetina’s brand-new annual catalog for 2024! Read it now! ⬇️ Learn how our comprehensive #EdgeAI products can take your business to the next level! Here is a little preview:  🟩 Edge AI #RealWorld Applications 🟩 AI Solutions 🟩 AI Software & Tools 🟩 AI Inference Devices 🟩 AI Inference & Training Servers 🟩 AI Accelerators & GPUs If you like what you saw, download now! ➡️ https://bit.ly/4cFbt0W #AiInnovation #EmbeddedComputing

Google has launched Gemma, a new family of lightweight open-weight models, including Gemma 2B and Gemma 7B, inspired by Gemini, for commercial and research use, with dense decoder-only architecture. While not open-source, they provide wide access for customization and fine-tuning, supported by ready-to-use Colab and Kaggle notebooks, and integrations with Hugging Face, MaxText, and Nvidia’s NeMo, catering to various AI applications. These models, offering improved generation quality, enable inferencing and tuning on local devices or cloud platforms, marking a significant advancement in AI development. #GoogleGemma #OpenWeightModels #AIInference #ModelFineTuning #AIResearch https://lnkd.in/d9es4b2t

Join VESSL AI x Pineconeat our SF Meetup “LLMs in Production” We'll talk about context-augmented LLMs, custom LLMs, serverless deployment, and more with top experts from Pinecone, VESSL AI, Snowflake, Koyeb and LlamaIndex Monday, September 9 6:00 - 9:00 PM San Francisco, CA Agenda: — "Building context-augmented LLMs with RAG & Vector DB" — "Custom LLMs—smarter, faster, and cheaper" — "High-Performance LLMs: Serverless Deployment across Accelerators, GPUs, and CPUs" — "Evaluating LLM Apps" — "Multi-agent systems in production" Speakers: Roie Schwaber-Cohen, Jaeman An, Yann Leger, Anupam Datta, and Laurie Voss Gain valuable insights and connect with industry experts on LLMs in production to advance your understanding and application of this technology. Apply to join: https://hubs.ly/Q02LChxX0

🔎 Curious about HEAVY.AI's strategies for redefining data analytics? 📚Learn how Vultr's advanced NVIDIA GPU infrastructure helps them tackle complex data sets quickly and efficiently. Explore the case study for insights! 🔍💪 #TechTrends #CloudComputing #DataInsights #BigData https://lnkd.in/ggerrU6N

In this webinar we will learn how to address the complexity of processing multimodal data, using NeMo Curator modules to solve these challenges; exploring various features such as deduplication, classifier models, and filters.

We're thrilled to announce the general availability of Ray Data! As data-centric AI explodes in popularity, organizations are facing challenges with scaling complex workloads, handling unstructured data, and managing diverse hardware requirements. Ray Data built on Ray and offers powerful solutions like: - Streaming execution: Process data in real-time for maximum efficiency. - Heterogeneous compute support: Leverage the best hardware for each task, from CPUs to GPUs. - ML ecosystem interoperability: Seamlessly integrate with your existing tools and workflows. With Ray Data, you can develop and deploy AI applications with massive performance and cost savings.

This week, we take a deeper look at how NVIDIA is scaling #clara its AI product suite and its partner ecosystems. NVIDIA Healthcare Artisight | Domino Data Lab | Flywheel.io | Rhino Health | Schrödinger | Vyasa Analytics | Weights & Biases Rinkle Dudhani

Shake off ‘#GPU Monday Blues’ 🔵🔵 - We’ve got the cure to your #data bottlenecks! 🙌 Check out the complete strategy💡in our whitepaper, ‘Designing a Strategy to Scale and Optimize Your GPUs' 👉https://hubs.ly/Q02D2sQf0 Discover how a modern data platform can turn your GPU investment into a high-performing asset for #AI workloads! #WinwithWEKA #gpus #generativeai #genai #datastorage