Gary Longsine’s Post

Very technical, but very thorough.

🌟Day 12 : Marvelous Groq ! 🌟 Meet Groq, a groundbreaking company founded in 2016 by Jonathan Ross, redefining the landscape of AI hardware with its innovative "Language Processing Units" (LPUs). These chips offer lightning-fast speeds and exceptional cost efficiency—reportedly one-tenth the cost of traditional AI hardware. Key Features of Groq's LPUs: Superior Performance: Groq's LPUs are designed to outperform traditional CPUs and GPUs in AI workloads, thanks to their innovative architecture optimized for natural language processing and inference tasks. Efficiency: With a focus on power efficiency, Groq's LPUs offer exceptional performance per watt, making them ideal for applications demanding both speed and sustainability. Scalability: The architecture of Groq's LPUs supports scalability, enabling seamless integration into diverse AI applications ranging from real-time analytics to deep learning models. Memory Hierarchy: Featuring a robust memory hierarchy including on-chip SRAM and off-chip memory, Groq's LPUs ensure high-bandwidth, low-latency data access critical for AI processing. Centralized Control Unit: The Centralized Control Unit coordinates operations across the LPU, optimizing workflow efficiency and enabling rapid execution of complex AI algorithms. Network-on-Chip (NoC): A high-bandwidth NoC facilitates efficient communication between different components of the LPU, enhancing overall system performance. Groq's API and Tools: Groq offers powerful API endpoints that facilitate seamless integration and programmable execution of AI operations. Developers can leverage these APIs to: Interpret Natural Language: Translate user queries into API requests for fetching real-time data or performing complex calculations. Automate Data Processing: Streamline workflows by calling specific functions through Groq's API, enabling automated data analysis and decision-making processes. Enhance Machine Learning Models: Integrate Groq's API into machine learning pipelines to enhance model training and inference capabilities. Groq's commitment to advancing AI hardware through efficient, scalable solutions underscores its pivotal role in shaping the future of intelligent computing. #Groq #AIHardware #LPUs #ArtificialIntelligence #Innovation #TechRevolution Explore practical implementations of Groq in AI through the provided link. Stay tuned for more insights into Groq's game-changing technologies and architecture! 🚀💡

NVIDIA Releases Nemotron-4 340B Model Family & Synthetic data generation pipeline 𝐌𝐨𝐝𝐞𝐥𝐬 𝐀𝐯𝐚𝐢𝐥𝐚𝐛𝐥𝐞: - Nemotron-4-340B-Base - Nemotron-4-340B-Instruct - Nemotron-4-340B-Reward 𝐋𝐢𝐜𝐞𝐧𝐬𝐞: - Under NVIDIA Open Model License Agreement 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: - Generating synthetic data to train smaller/domain specific language models - Research studies & Commercial applications 𝐎𝐩𝐞𝐧-𝐒𝐨𝐮𝐫𝐜𝐞𝐝: - Models - Pretraining code - Alignment strategies - Reward Model Training code - Synthetic data generation pipeline (Links in comments) 𝐊𝐞𝐲 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬: 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭: - Fits on a single DGX H100 with 8 GPUs in FP8 precision - All models available on #HuggingFace 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠: - 9 trillion tokens - Sequence length of 4096 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞: - Decoder-only Transformer - Rotary Position Embeddings (RoPE) - Grouped Query Attention (GQA) - Squared ReLU activation - No dropout & bias - Untied embeddings 𝐀𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭: - Supervised Fine-Tuning (SFT) - Preference Fine-Tuning (RLHF, DPO) 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: Nemotron-4-340B-Base: - Competitive with Llama-3 70B, Mixtral 8x22B, Qwen-2 72B - Excels in commonsense reasoning (ARC-Challenge, MMLU, BigBench Hard) Nemotron-4-340B-Instruct: - Surpasses Llama-3 70B, Mixtral 8x22B, Qwen-2 72B in instruction following and chat capabilities Nemotron-4-340B-Reward: - Top accuracy on RewardBench (Allen AI, 2024) - Surpasses proprietary models (GPT-4o-0513, Gemini 1.5 Pro-0514) https://lnkd.in/g4mQ5SJM #ai #artificialintelligence #research #llm #generativeai #nvidia #nlp #naturallanguageprocessing #nlproc

Another fun read from this weekend was the Bitnet based “The Era of 1 bit LLMs” - https://lnkd.in/gVhRWpW7 . The paper has an ambitious goal, to represent model weights in -1, 0, +1. It was trained on similar datasets as LLama to similar tokens and similar parameters. Needless to say, due to binary weights, the multiplications are completely eliminated and only additions remain - means latency improvements, throughput improvements, lower emergency consumption. Questionable thing is accuracy, but it seem that a 3.9 B bitnet model was giving LLama 3B run for its money. Promising direction, but quite a few experiments need to happen. I am sure the team is busy. This also reminds us that the number formats play the biggest role in making the models run viably in production. From FP32, to BF16 and now FP8’s in the Hopper GPUs paved a path to make Inference faster. The distribution of weights in a neural network is usually Gaussian, with the top of the bell curve around 0. Hence it makes sense to find interesting ways to represent the information in a neutral network in as less space as possible while maziming that range and minimizing the error. Quantization is another aspect were we attempt the manipulations to represent the information in the famous INT8 representation (or some even stretch it to INT4). Going forward, the giants Nvidia, Intel, Meta, Qualcomm, AMD and Microsoft are aligning behind micro scaling formats (MX) and hoping to see more of in their future chips - https://lnkd.in/gkChVStP . Another interesting startup in this area is Lemurian Labs, which is pushing it PAL format. Interesting time ahead. 20 years down the line, we may be talking about the Large Language Models similar to how we talk about mainframes now.

NVIDIA just released Nemotron-4, and it outperformed GPT-4. Key Highlights: ✅ The big unlock is the synthetic data generation, which mimicks real world data, and allows for training and fine tuning LLMs 🔓 Nemotron-4 democratizes LLM training, enabling organizations to rapidly develop custom language models tailored to their specific domains and use cases, without the high costs and challenges of acquiring extensive datasets. Read the announcement below #NVIDIA #SyntheticData #LLMs #GenerativeAI https://lnkd.in/eRVavfD7

🚀 The AI Revolution Just Got Cheaper 💸 Forget breaking the bank to train cutting-edge AI models. The eggheads at Ivy League schools have cooked up JetMoE, a foundational model that goes toe-to-toe with Meta's LLaMA2 – for a measly $100k in training costs. This is a game-changer, folks. 🤯 👉 AI is no longer the exclusive playground of tech giants with bottomless budgets. 👉 Foundational models are becoming more accessible by the day. 👉 Expect an explosion of AI innovation as the barriers to entry crumble. The future belongs to those who can harness AI's power without breaking the piggy bank. 🐷🔨 Are you ready for the democratization of AI? 🗳️ → #AIRevolution #JetMoE #AffordableAI #FutureOfTech

#NVIDIA & #OCI have a solution to help enterprises leverage their structured and unstructured data more effectively, enhancing both the quality and reliability of #generativeAI outputs using RAG (Retrieval-augmented generation) pipelines. The vast majority of the world’s data remains untapped, and enterprises are looking to generate value from this data by creating the next wave of generative AI applications that will make a transformative business impact. RAG pipelines are a key part of this, enabling users to have conversations with large corpuses of data and turning manuals, policy documents, and more into interactive generative AI applications. #clinicaldata #oracle https://lnkd.in/gvaCNTb2

Hey #devs, check out this technical blog for a new way to use NVIDIA's Nemotron-4 340B model for Synthetic Data Generation (SDG)! https://lnkd.in/gJaU-ivG #developer #learning #llm #gpt #mlops #syntheticdata #data #datascience #llmops #ai #ml #nemotron #nemo

Retrieval Augmented Generation (RAG) is a cutting-edge method allowing Large Language Models (LLMs) to access enterprise data. With the ability to overcome one of the pressing limitations of LLMs, domain specificity, RAG enables enterprise to deploy purpose-built virtual assistants, chatbots, and agents. This innovation has tremendous implications for knowledge workers in most industries, including those in the intelligence field. Check out their answers here:

Language Processing unit - the heart of LLM. I was having a discussion around Nvidia’s impact on AI 🤖 with my friend Dheeraj Lenka and discussed about the worth challenger GROQ Last month, a largely unknown company, Groq, demonstrated unprecedented speed running open-source LLMs such as Llama-2 (70 billion parameters) at more than 100 tokens per second, and Mixtral at nearly 500 tokens per second per user on Groq’s Language Processing Unit (LPU). Wohoo 🚀🙌 For the comparison: ✅ According to Groq, in similar tests, ChatGPT loads at 40–50 tokens per second, and Bard at 70 tokens per second on typical GPU-based computing systems. ✅ Context for 100 tokens per second per user — A user could generate a 4,000-word essay in just over a minute.” The LPU is a special kind of computer brain 🧠 designed to handle language tasks very quickly. Unlike other computer chips that do many things at once (parallel processing), the LPU works on tasks one after the other (sequential processing), which is perfect for understanding and generating language. What makes LPU special - Processing differently !! Imagine it like a relay race 🏃 where each runner (chip) passes the baton (data) to the next, making everything run super fast li⚡️The LPU is designed to overcome the two LLM bottlenecks: compute density and memory bandwidth. Groq took a novel approach right from the start, focusing on software and compiler development before even thinking about the hardware. They made sure the software could guide how the chips talk to each other, ensuring they work together seamlessly like a team in a factory. An LPU™ inference engine has the following characteristics: ✅ Exceptional sequential performance ✅ Single core architecture ✅ Synchronous networking that is maintained even for large scale deployments ✅ Ability to auto-compile >50B LLMs Instant memory access ✅ High accuracy that is maintained even at lower precision levels Now, don’t go tossing out your GPUs just yet! While the LPU is a beast when it comes to inference, making light work of applying trained models to new data, GPUs still reign supreme in the training arena. The LPU and GPU might become the dynamic duo of AI hardware, each excelling in their respective roles. More on Architecture- next time 🙏 #LLM #LPU #Groq #nvidia