Artificial Analysis’ Post

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Are you looking to unlock the power of lightning-fast inferencing speed at 1000+ tokens/sec on your own custom Llama3? Introducing SambaNova Fast API, available today with free token-based credits to make it easier to build AI apps like chatbots and more. Bring your own custom checkpoint for both Llama 8B and Llama 70B and avoid the cost of acquiring hundreds of chips to get started.  Relevance? The next phase of AI is Agentic AI; you’ll need lots of models, big and small, working together as one system. Development teams will require ultra-fast token generation, which we know cannot be achieved with GPUs. That is not all… you’ll need to host lots of models concurrently, with instantaneous switching between these models, which we know can’t be achieved with other architectures due to their inefficiency. You can’t get this speed, with a diversity of models, including your own custom model behind a simple API anywhere else!  SambaNova Fast API is available now: https://lnkd.in/g9W_Bnjv #FastAI #RDU #API 

👏 Excited to share we have launched SambaNova Fast API! It is available to apply for today with free token-based credits. With our Fast API, you are able to bring your own custom checkpoint for both Llama 8B and Llama 70B and speed up your ability to get started on inferencing at super fast speeds (1000 tokens/s+). As as we adopt more agentic systems: 👥 More variety of models will be needed. ⚡ Inference speed matters a lot more with chaining of models - you will need ultra-fast token generation. 🔃 You’ll need to host lots of models concurrently, with instantaneous switching between these models. Apply for access here: https://lnkd.in/g9W_Bnjv

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The Trillion-Dollar Trick: Reducing LLM Inference Costs by 100x of Magnitude LLMs always trigger the first response that inference costs will be high and big GPUs with 100GB+ vRAM like #H100, #A100 are a must. While that's true for quick use cases like #chatbots, LLMs are not limited to just chat. They will extend to workflow automation and many other applications. That's where we can see a trade-off between price and time. But is that even possible? A clever thought of #Layerwise inference is making this a reality, allowing large language models (LLMs) to run at a fraction of the cost by trading off execution time. Instead of loading all 80+ layers of a multi-billion parameter model onto expensive A100 GPUs, layerwise inference loads and executes just one layer at a time. This reduces the GPU memory requirement from over 100 GB down to just 4 GB. #AirLLM is an open source library that makes it easy to run efficient layerwise inference on models like the 70B parameter #Llama3. According to the creators, this reduces the GPU cost from over $20,000 to under $100. As more enterprises look to leverage top LLMs while keeping data on-premises, techniques like AirLLM's layerwise execution open up exciting possibilities. Bringing powerful models to the edge at low cost will enable a new wave of enterprise AI innovation. The future of practical and cost-effective LLM deployment is bright indeed. Libraries like AirLLM are paving the way by intelligently optimizing execution for different scenarios. Expect to see more such advancements that make LLMs accessible for a wide range of high-value enterprise use cases, beyond just chatbots. While trading off execution time for cost efficiency may not suit all applications, it's a game-changer for many. This trick is set to democratize access to powerful LLMs and unlock their potential across industries. #LLMInference #CostEfficiency #LayerwiseExecution #AirLLM #EnterpriseAI #EdgeComputing #GPUOptimization #ModelDeployment #AIAccessibility #InnovationUnleashed #BeyondChatbots #WorkflowAutomation #IntelligentOptimization #DataPrivacy #OnPremisesAI #DemocratizingAI #CuttingEdgeTechnology #FutureOfAI #GameChangingTrick #UnlockingLLMPotential

Unlock the power of AMD #MI300X #GPU with AMD #ROCm software for #OpenSource acceleration for #AI, #GenAI, and #HPC. Moving the GPU compute trifecta #upandtotheright. 🚀🚀🚀 https://lnkd.in/gXx4ApzA

Fine-tuning with Mosaic AI Model Training: Whether you're fine-tuning a model on a small dataset or pre-training a model from scratch (like DBRX) with trillions of tokens on 3,000+ GPUs, we provide an easy-to-use, managed API for model training, abstracting away the underlying infrastructure. We’re seeing our customers find success with fine-tuning smaller open source models for system components to reduce cost and latency while matching GPT-4 performance on enterprise tasks with proprietary data. Model Training empowers customers to fully own their models and their data, allowing them to iterate on quality Don't forget to register to the youtube channel: https://lnkd.in/e_Fs3ydK #Databricks #DataAISummit

Infrastructure for the AI age is big business, and a real and profitable business that will continue to grow over the coming decades.  It is not an easy business and many people trying to find their way will realize that it isn’t right for them or as easy as they hoped.  When the shakeout happens I expect we will see a handful of players who generate a ton of value, because they can do deals but more importantly can execute on the infrastructure and financing, and have a solid software platform to allow enterprises and developers to easily take advantage of the power of GPUs.  It is a great time to invest in the best AI infrastructure companies that are still small because once they start to scale, the best will not stop. #AI #GPU #GPUcloud #OriIndustries

The latest update for #ClearML includes "#OpenSource Fractional GPUs for Everyone, Now Available from ClearML" and "The State of #AI Infrastructure at Scale 2024". #MachineLearning https://lnkd.in/eenUzjUe

The latest update for #Iguazio includes "#GenerativeAI in Call Centers: How to Transform and Scale Superior #CustomerExperience" and "Why You Need GPU as a Service for #GenAI". #DataScience #MLOps #ML https://lnkd.in/ddMsxFRx