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新内核通过将量化/反量化操作融合进注意力计算的 CUDA Kernel 中，使 KV 缓存量化不再造成推理减速，某些带宽受限场景下甚至能进一步提升吞吐

1

Sources

50%

Confidence

Long-term

Relevance

7/16/2026

First Seen

Sources

ExLlamaV3 v1.0.0正式发布：推理提速、KV量化无损、依赖全面精简

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UnverifiedDSpark 对 MLA 上投影计算路径进行了 CUDA 内核融合优化，将还原计算与注意力计算合并为单个 kernel 执行71% similar Unverified推理框架运行时开销（CUDA上下文初始化、算子融合缓存、显存碎片等）通常额外消耗 0.5–2 GB69% similar UnverifiedTensorRT能针对CUDA和GPU优化张量运算，进一步提升性能69% similar Unverified推理优化中的KV Cache管理涉及操作系统的内存分页概念，量化技术依赖对浮点数表示的理解，FlashAttention需要CUDA编程知识68% similar UnverifiedUnsloth 的核心技术原理是通过手动编写 CUDA 内核来优化反向传播过程中的内存分配，重写注意力机制的前向和反向传播过程68% similar

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