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The AI space is abuzz with large language models (LLMs), but using them locally is a challenge due to their enormous size. Organizations that want to use these models for applications such as question answering must either invest in expensive cloud infrastructure or use closed-source models. By using closed-source models, companies also give up their… Read More Run a Medical Chatbot on CPUs With Sparse LLMs and DeepSparse
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Since OpenAI's introduction of ChatGPT, developers worldwide have widely embraced the OpenAI API as the go-to solution for making API requests to their language models. However, in response to the growing demand within open-source communities for more accessible and cost-effective language model alternatives, users have started to explore the integration of DeepSparse with OpenAI's API.… Read More Integrating DeepSparse With OpenAI’s API for Fast Local LLMs
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LangChain is one of the most exciting tools in Generative AI, with many interesting design paradigms for building large language model (LLM) applications. However, developers who use LangChain have to choose between expensive APIs or cumbersome GPUs to power LLMs in their chains. With Neural Magic, developers can accelerate their model on CPU hardware, to… Read More Building Sparse LLM Applications on CPUs With LangChain and DeepSparse
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Neural Magic's DeepSparse Inference Runtime can now be deployed directly from the Google Cloud Marketplace. DeepSparse supports various machine types on Google Cloud, so you can quickly deploy the infrastructure that works best for your use case, based on cost and performance. In this blog post, we will illustrate how easy it is to get… Read More Neural Magic’s DeepSparse Inference Runtime Now Available in the Google Cloud Marketplace
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With conventional object detection models, it can be challenging to identify small objects due to the limited number of pixels they occupy in the overall image. To help with this issue, you can use a technique like, Slicing Aided Hyper Inference (SAHI), which works on top of object detection models to discover small objects without… Read More Detecting Small Objects on High-Resolution Images With SAHI and DeepSparse
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Training time is a well-known problem when training computer vision networks such as image classification models. The problem is aggravated by the fact that image data and models are large, therefore requiring a lot of computational resources. Traditionally, these problems have been solved using powerful GPUs to load the data faster. Unfortunately, these GPUs are… Read More Sparsify Image Classification Models Faster with SparseML and Deep Lake
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This is the second entry in our AWS-centric blog series leading up to the AWS Startup Showcase on Thursday, March 9th. We are excited to be a part of this event with other selected visionary AI startups to talk about the future of deploying AI into production at scale. Sign up here to register for this… Read More Build Scalable NLP and Computer Vision Pipelines With DeepSparse - Now Available From the AWS Marketplace (Part 2 of 3-Blog Series)
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Simplify Pre-processing Pipelines with Sequence Bucketing to Decrease Memory Utilization and Inference Time For Efficient ML DeepSparse is an inference runtime offering GPU-class performance on CPUs and APIs to integrate ML into your application. DeepSparse has built-in performance features, like sequence bucketing, to lower latency and increase the throughput of deep learning pipelines. These features… Read More Process Text Faster Through Sequence Bucketing and DeepSparse
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According to a recent poll from Ultralytics, the creators of YOLO object detection models, 22% of ML experts experience difficulty deploying their vision AI models. Getting into production successfully is hard, and scaling while in production is even harder. To improve this step in the ML pipeline, Ultralytics partnered with Neural Magic, whose DeepSparse runtime… Read More Accelerating Object Detection Deployments With Ultralytics & Neural Magic
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Object detection is a crucial task in computer vision. With applications in fields such as image and video analysis, robotics, and autonomous vehicles, object detection involves identifying and locating objects within an image or video. Traditionally, it has been tackled using various techniques, including edge and corner detection, template matching, and machine learning-based approaches. In… Read More Object Detection: Your Ultimate Guide to Easy Deployment and Fast Inferencing