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khanhnd61-vr/README.md

Khanh D. Nguyen

AI software engineer working on inference engineering, model optimization, and edge AI for robotics: making capable models run fast on real hardware.

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  • vla.cpp: Vision-Language-Action models on local hardware, in the spirit of llama.cpp.
  • vla.simd: Efficient CPU inference for language-conditioned manipulation - SIMD micro-kernels (AVX2 / NEON) running 30+ actions per second on a Raspberry Pi 5.
  • ggml-compiler: Compiles PyTorch and ONNX models into ggml programs for CPU and CUDA inference without Python.
  • XPU Arch: Architecture diagrams of CPUs, GPUs, NPUs, and SoCs, from core clusters and caches down to register files.
  • VLA Arch: Architecture diagrams of Vision-Language-Action policies supported by vla.cpp and vla.simd, drawn from their C++ forward passes.
  • VLA Hub: Explore measured latency and memory usage of Vision-Language-Action models across hardware using vla.cpp and vla.simd.

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  1. VinRobotics/vla.cpp VinRobotics/vla.cpp Public

    A unified inference runtime for VLA models.

    C++ 254 49

  2. modelopt101 modelopt101 Public

    Learn model optimization for edge deployment

    HTML 16 27

  3. cair-vinuni/vla.simd cair-vinuni/vla.simd Public

    A pure C++ inference engine for VLA policies on CPUs, with no GPU, CUDA, or ggml dependency. Built with its own tensors, operators, and SIMD kernels, the engine keeps kernels easy to inspect, tune,…

    C++ 35 1

  4. ggml-compiler ggml-compiler Public

    Compile PyTorch and ONNX models into ggml programs

    Python 4