The key differences between FP16 (16-bit floating point) and INT8 (8-bit integer) precision for DeepSeek models primarily revolve around accuracy, dynamic range, and computational efficiency.
Accuracy
FP16 generally provides higher accuracy compared to INT8. This is particularly important for models requiring precise calculations, as FP16 can represent a wider range of values and finer distinctions between them due to its floating-point nature. In contrast, INT8's lower precision can lead to greater quantization errors, especially in scenarios where model parameters or activations have values that exceed the limited range of signed 8-bit integers, which spans from -128 to 127[1][4].Dynamic Range
The dynamic range of FP16 is significantly broader than that of INT8. FP16 can express values across a much larger spectrum because it allocates bits for both the exponent and the mantissa, allowing it to handle very small and very large numbers effectively. In comparison, INT8's fixed range limits its applicability, particularly in complex models where the output values may vary widely[2][3]. For example, while INT8 has a dynamic range of $$2^8$$, FP16 can represent values in a range that effectively allows for much more nuanced data representation[2].Computational Efficiency
INT8 is optimized for computational efficiency, particularly in inference tasks on edge devices. The use of integer arithmetic typically requires less power and can lead to faster computations compared to floating-point operations. This efficiency is crucial for deploying models in real-time applications where latency and energy consumption are critical factors[1][4]. Moreover, INT8 quantization allows for significant reductions in memory usage, which can enhance performance on devices with limited resources[3][5].In summary, while FP16 offers superior accuracy and dynamic range suitable for training and inference of complex models, INT8 excels in operational efficiency and speed, making it preferable for deployment in resource-constrained environments. The choice between these precisions often depends on the specific requirements of the application, such as the need for accuracy versus the need for speed and efficiency.
Citations:[1] https://arxiv.org/pdf/2303.17951.pdf
[2] https://www.baseten.co/blog/fp8-efficient-model-inference-with-8-bit-floating-point-numbers/
[3] https://itsabout.ai/understanding-data-types-in-ai-and-hpc-int8-fp8-fp16-bf16-bf32-fp32-tf32-fp64-and-hardware-accelerators/
[4] https://massedcompute.com/faq-answers/?question=What+are+the+differences+between+INT8+and+FP16+precision+in+terms+of+accuracy+and+speed%3F
[5] https://www.baseten.co/blog/introduction-to-quantizing-ml-models/
[6] https://www.reddit.com/r/MachineLearning/comments/114fgo8/d_is_fp16_used_in_deep_learning_or_fp32/
[7] https://forums.developer.nvidia.com/t/same-inference-speed-for-int8-and-fp16/66971
[8] https://forums.developer.nvidia.com/t/int8-vs-fp16-results/158139