Unverified50% confidenceSolutionExact time
开发者采取混合精度策略，将对质量敏感的层保留在BF16，仅对能从FP8获益的部分做转换
1
Sources
50%
Confidence
Long-term
Relevance
7/12/2026
First Seen
Sources
LingBot-Video 1.3B Selective FP8 Quantization Benchmark: 22% Faster Sampling
redditr/comfyui7/11/2026
Related Claims
Unverified此次工作采用FP8 rollout搭配BF16 actor的混合精度方案79% similarUnverified工程实践中建议将路由器层、注意力投影矩阵及门控网络保留在 BF16/FP16 精度，仅对专家 FFN 权重进行激进量化76% similarUnverifiedThis paper adopts an INT8+bf16 mixed-precision strategy: the original model weights use INT8 quantization (via the LLM.int8() method from the bitsandbytes library), while the LoRA components use bf16 with fp32 master weights.75% similarUnverifiedMixed-precision training was jointly proposed by NVIDIA and Baidu in 2018; its core idea is to use FP16 in forward and backward propagation to reduce VRAM usage and accelerate computation, while retaining an FP32 master weight copy for parameter updates.72% similarUnverifiedIn mixed-precision training, the forward pass uses FP16 to leverage Tensor Core acceleration while parameter updates retain FP32 to maintain numerical stability, which has become standard practice for large model training.69% similar
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