The Hunyuan-A13B is available in two major quantized versions for different hardware environments and computing needs:
FP8 quantized version:
- Stores model weights and activation values using 8-bit floating point format
- Better suited for low to mid-range GPU devices
- A better balance between computational efficiency and model accuracy
- Recommended for scenarios where some inference speed is required but top-notch hardware is not available
GPTQ-Int4 quantized version:
- Using 4-bit integer quantization technology
- Significantly reduced model memory footprint (only Int4 storage required)
- Suitable for severely resource-constrained environments (e.g. GPUs with less than 10GB VRAM)
- Need to optimize inference speed using a backend such as TensorRT-LLM
Users can choose the appropriate quantization version according to their hardware conditions and performance requirements. the FP8 version is suitable for situations where better model accuracy needs to be maintained, while the Int4 version is more suitable for scenarios where resources are extremely limited but a certain loss of accuracy is acceptable.
This answer comes from the articleHunyuan-A13B: Efficient Open Source Large Language Modeling with Ultra-Long Context and Intelligent Reasoning SupportThe
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