Showing 3 open source projects for "dispatch"

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    Host LLMs in Production With On-Demand GPUs

    NVIDIA L4 GPUs. 5-second cold starts. Scale to zero when idle.

    Deploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
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    MongoDB Atlas runs apps anywhere

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  • 1
    OpenSquilla

    OpenSquilla

    Token-Efficient AI Agent with same budget, higher intelligence density

    OpenSquilla is a token-efficient microkernel AI agent runtime designed for CLI, web UI, and chat-based workflows. It routes each turn through a shared loop that can select lower-cost models when appropriate while preserving tool dispatch, retries, memory, and decision logging. The project supports multiple LLM providers through a pluggable provider layer, making it adaptable to different model ecosystems. It includes persistent memory, built-in web search, on-device embeddings, and sandboxing for safer execution. OpenSquilla is designed for users who want stronger agent capabilities without wasting tokens on every interaction. ...
    Downloads: 4 This Week
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  • 2
    fastai

    fastai

    Deep learning library

    fastai is a deep learning library which provides practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains, and provides researchers with low-level components that can be mixed and matched to build new approaches. It aims to do both things without substantial compromises in ease of use, flexibility, or performance. This is possible thanks to a carefully layered architecture, which expresses common underlying...
    Downloads: 12 This Week
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  • 3
    VibeTensor

    VibeTensor

    Our first fully AI generated deep learning system

    ...It implements a PyTorch-style eager tensor library with a modern C++20 core that supports both CPU and CUDA backends, giving it the ability to manage tensors, automatic differentiation (autograd), and complex computation flows similar to mainstream frameworks. What makes VibeTensor remarkable is that every major component, from core libraries and dispatch systems to CUDA runtime support, caching allocators, and language bindings, was created and validated by coding agents using automated builds and tests rather than manual line-by-line human coding. The system includes both a Python frontend via a torch-like API and an experimental Node.js/TypeScript interface.
    Downloads: 0 This Week
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