GGML vs GPTQ in Simple Words

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AI Summary

Summary: GGML vs GPTQ

• GGML:
  • Best for CPU or weak GPU.
  • Tensor library written in C.
  • Enables large models and high performance on commodity hardware.
  • Used by Lama.cpp and Whisper.cpp.
  • Supports 16-bit float and integer quantization.
  • Features automatic differentiation and built-in optimization algorithms (e.g., Adam, L-BFGS).
  • Optimized for Apple silicon, no third-party dependencies.
  • Zero memory allocations at runtime for improved performance.
  • Includes guided language output support.
• GPTQ:
  • Suitable for systems where the model fits entirely on the GPU.
  • One-shot weight quantization method using approximate second-order information.
  • Efficiently compresses large GPT models (e.g., 175 billion parameters) while preserving accuracy.
  • Reduces model size but maintains accuracy.
  • Increases inference speeds over FP16.
• Usage Recommendations:
  • Use GGML if you have a CPU or weak GPU.
  • Use GPTQ if you have a GPU that can fit the entire model.