Showing 4 open source projects for "high performance computing"

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  • 1
    uzu

    uzu

    A high-performance inference engine for AI models

    uzu is a high-performance inference engine designed to run artificial intelligence models efficiently on Apple Silicon hardware. Written primarily in Rust and leveraging Apple’s Metal framework, the project focuses on maximizing performance when executing large language models and other AI workloads on devices such as Mac computers with M-series chips.
    Downloads: 1 This Week
    Last Update:
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  • 2
    mistral.rs

    mistral.rs

    Fast, flexible LLM inference

    mistral.rs is a fast and flexible LLM inference engine implemented in Rust, designed to run and serve modern language models with an emphasis on performance and practical deployment. It provides multiple entry points for developers, including a CLI for running models locally and an HTTP server that exposes an OpenAI-compatible API surface for easy integration with existing clients. The project includes hardware-aware tooling that can benchmark a system and choose sensible quantization and...
    Downloads: 11 This Week
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  • 3
    webclaw

    webclaw

    Fast, local-first web content extraction for LLMs

    webclaw is a high-performance web content extraction tool designed specifically for AI agents and large language models, focusing on delivering clean, structured data instead of raw HTML. It is built in Rust and operates without a headless browser, using advanced techniques such as TLS fingerprinting to bypass common scraping barriers and mimic real browser behavior.
    Downloads: 0 This Week
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  • 4
    Korvus

    Korvus

    Korvus is a search SDK that unifies the entire RAG pipeline

    Korvus is an open-source retrieval-augmented generation (RAG) pipeline designed to run entirely inside PostgreSQL, allowing developers to build AI search and knowledge systems directly within a database environment. The project consolidates the typical steps of a RAG pipeline—including embedding generation, document retrieval, reranking, and text generation—into a single query executed within the Postgres ecosystem. By leveraging PostgresML and vector extensions such as pgvector, Korvus...
    Downloads: 0 This Week
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