Showing 4 open source projects for "server performance"

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  • 1
    Triton Inference Server

    Triton Inference Server

    The Triton Inference Server provides an optimized cloud

    ...Triton delivers optimized performance for many query types, including real-time, batched, ensembles, and audio/video streaming. Provides Backend API that allows adding custom backends and pre/post-processing operations. Model pipelines using Ensembling or Business Logic Scripting (BLS). HTTP/REST and GRPC inference protocols based on the community-developed KServe protocol.
    Downloads: 1 This Week
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  • 2
    OpenVINO Model Server

    OpenVINO Model Server

    A scalable inference server for models optimized with OpenVINO

    OpenVINO™ Model Server is a high-performance inference serving system designed to host and serve machine learning models that have been optimized with the OpenVINO toolkit. It’s implemented in C++ for scalability and efficiency, making it suitable for both edge and cloud deployments where inference workloads must be reliable and high throughput. The server exposes model inference via standard network protocols like REST and gRPC, allowing any client that speaks those protocols to request predictions remotely, abstracting away the complexity of where and how the model runs. ...
    Downloads: 0 This Week
    Last Update:
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  • 3
    Text Generation Inference

    Text Generation Inference

    Large Language Model Text Generation Inference

    Text Generation Inference is a high-performance inference server for text generation models, optimized for Hugging Face's Transformers. It is designed to serve large language models efficiently with optimizations for performance and scalability.
    Downloads: 0 This Week
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  • 4
    BrowserAI

    BrowserAI

    Run local LLMs like llama, deepseek, kokoro etc. inside your browser

    BrowserAI is a cutting-edge platform that allows users to run large language models (LLMs) directly in their web browser without the need for a server. It leverages WebGPU for accelerated performance and supports offline functionality, making it a highly efficient and privacy-conscious solution. The platform provides a developer-friendly SDK with pre-configured popular models, and it allows for seamless switching between MLC and Transformer engines. Additionally, it supports features such as speech recognition, text-to-speech, structured output generation, and Web Worker support for non-blocking UI performance.
    Downloads: 0 This Week
    Last Update:
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