Unverified50% confidenceSolutionExact time
The solution uses BF16 precision uniformly for the KV cache, and MXFP4 quantization for the model weights.
1
Sources
50%
Confidence
Long-term
Relevance
10/11/2026
First Seen
Sources
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Related Claims
Unverified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78% similarUnverified此次工作采用FP8 rollout搭配BF16 actor的混合精度方案75% similarVerifiedINT4 quantization can theoretically compress a model's VRAM footprint to one-quarter of FP16, with commonly used tools including GPTQ, AWQ, and GGUF.73% similarUnverifiedLlama.cpp can compress model weights from Float16 precision to int8 or even int4 through quantization technology.72% similarUnverified本地部署常采用INT4或INT8量化压缩模型体积，而云端可能运行FP16或BF16完整精度版本，量化会引入精度损失并影响生成质量72% similar
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