With the rapid increase of digital content like images or videos nowadays, compression technology contributes more to saving storage or transferring time with large-scale data. While some existing methods already achieved a great compression ratio, they are not applicable to certain live applications under low efficiency. In this work, we use massive parallelization to speed up the SOTA baseline FLIF, including bitwise-equivalent speedup and learning-based speedup. Our method achieves 38.7x throughputs for encoding and 2.45x throughputs for decoding, compared to the baseline FLIF.