Explanation of INT8 ConvRot (FP8 is no longer needed)

SYSTEM NOTICE Auto translation by AI. Be sure, accuracy, nuances and authorial intent may not be fully reflected. Show original Explanation of INT8 ConvRot (FP8 is no longer needed) 43 ひろろひ🐈‍⬛Forge Neo推し 2026年7月4日 18:16 The modeling and quantization method called INT8 ConvRot, which was natively supported in ComfyUI v0.27.0 released on July 1, 2026, is a hot topic.It is particularly beneficial for the GeForce RTX 20/30 series, but it has also been reported to provide performance exceeding the previously standard FP8 and FP8 Scaled formats on the GeForce RTX 40/50 series as well.Because of this, it is said that INT8 ConvRot will become the standard for all 8-bit quantized models, and support is actually being advanced by Comfy-Org.Since information has not yet spread widely and many people are misunderstanding or confusing it, I will organize and explain it in this article.2026/7/7: Added that Forge Neo now supports INT8 ConvRot2026/7/6: Added information regarding Kimama-san's INT8 ConvRot evaluation results2026/7/5: Added information about GGUF in "Classification of Modeling Methods and Formats"2026/7/5: Added information about Triton and PyTorch in "How to use INT8 ConvRot"AI Model Storage FormatsBefore explaining INT8 ConvRot, I will explain the basics of AI model storage formats.Modeling and QuantizationAI models are modeled in FP32, a 32-bit FLOAT type, for maximum precision in a general range, but since even SDXL results in a large file size of 12GB, they are converted to reduce the bit count to 16-bit, 8-bit, or in some cases 4-bit.The smaller the bit count, the smaller the file size, making it easier to handle and faster to process. However, because the number of representable values decreases and precision drops, methods and formats to minimize the impact on precision and results are used as much as possible.Classification of Modeling Methods and FormatsThe table below roughly classifies the modeling methods and formats of AI models.$$\begin{array}{l|l|l}\bf{階層} & \bf{例} & \bf{役割} \\\hline\text{ファイル形式} & \text{safetensors, GGUF,} & \text{保存するコンテナ} \\& \text{ONNX} & \\\hline\text{符号化方式} & \text{FP16, BF16,} & \text{数値をbit列で表現する方法} \\& \text{INT8, FP8, FP4} & \\\hline\text{スケーリング} & \text{なし, Tensor-wise,} & \text{値の範囲を調整する方法} \\\text{方式} & \text{Row-wise} & \\\hline\text{量子化方式} & \text{RTN, AWQ,} & \text{低精度へ変換するアルゴリズム} \\& \text{GPTQ, ConvRot} & \\\end{array}$$I searched for such a classification table on the Web but could not find one, so I created it while Q&A-ing with ChatGPT for my own understanding. It might be rough or have inappropriate terminology, but since I had it corrected many times, I believe the general classification is correct.2026/7/5 update: GGUF is not just a file format but also specifies a storage method for quantized data such as Q4_K_M, so it cannot be organized in the classification table above.Since this article does not touch on GGUF, I will stick to the classification in the table above so as not to complicate the organization.By the way, in a broad sense, modeling with these methods and formats is sometimes collectively called "quantization," but in this article, I will call it "modeling" and refer to the algorithm for converting to low precision as a "quantization method."Note that there are countless sites that explain individual methods and formats in detail, so I will not explain them in this article. If necessary, please look them up elsewhere or ask an AI.Regarding FLOAT types, Kimama-san's article is detailed. Representative Model Formats and INT8 ConvRotBased on the classification table mentioned above, representative model formats including INT8 ConvRot are organized as follows.16-bit models$$\begin{array}{l|l|l}\bf{階層} & \bf{FP16} & \bf{BF16} \\\hline\text{符号化方式} & \text{FP16} & \text{BF16} \\& & \text{(Brain FP16)} \\\hline\text{スケーリング} & \text{な