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Alternatives to model-deploy

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    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables AI agents to export PyTorch models to validated ONNX, quantize them to FP16 or INT8, measure fidelity drift and benchmark latency and memory against edge hardware budgets. It also autonomously searches export and precision configurations and persists JSON, Markdown and HTML reports.
      MIT
    • F
      license
      Not graded
      quality
      A
      maintenance
      Enables coding agents to coordinate local profilers, benchmark tools, trace processors, and direct targets, turning explicit artifacts or commands into bounded runtime evidence with provenance, coverage, and limitations. It exposes analysis and capture tools that help trace, profile, and burn down hotspots in application and native code, GPU kernels, and inference stacks without workspace setup or a hosted service.
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    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables local candidate review of ONNX or TFLite models by checking static evidence and externally measured results against a fixed policy, producing accept/reject/hold evidence ZIPs for AI agent workflows.
      Apache 2.0
    • A
      license
      Not graded
      quality
      D
      maintenance
      An MCP server that autonomously optimizes ONNX ML models for Arm64 deployment, providing tools to analyze models, apply real INT8 quantization, benchmark performance, and generate Arm64-optimized Docker deployment packages.
      Apache 2.0