Moonshot AI Open-Sources FlashKDA for Kimi Delta Attention, Reporting Up to 2.22× H20 Prefill Speedup
Moonshot AI has released FlashKDA, a CUTLASS-based implementation of Kimi Delta Attention kernels. The company reports 1.72×–2.22× faster prefill than the flash-linear-attention baseline on NVIDIA H20 GPUs.
Moonshot AI has open-sourced FlashKDA, a high-performance implementation of Kimi Delta Attention (KDA) kernels built with CUTLASS. The project is designed to integrate with the flash-linear-attention ecosystem and can serve as a drop-in backend for compatible workloads.
According to Moonshot AI's published benchmark, FlashKDA delivers a 1.72×–2.22× prefill speedup over the flash-linear-attention baseline on NVIDIA H20 GPUs. Secondary coverage also describes support for variable-length batching and an MIT license.
The implementation is available on GitHub with its integration details and benchmarks. As with any kernel benchmark, results may vary with sequence length, batch size, model configuration, and the surrounding software stack, so the reported gains should not be generalized to every deployment environment.
Source evidence
AI Research Digest - 2026-05-01 - Noah Technoah-code.com · supportingCompiled on May 1, 2026 ## Key Highlights The week’s most significant technical advancement is the open-sourcing of FlashKDA by Moonshot AI. This implementation offers high-performance kernels for Kimi Delta Attention that plug directly into the flash-linear-attention ecosystem, specifically leveraging the CUTLASS framework. By introducing variable-length batching and benchmarking against H20 hardware, Moonshot is addressing a critical need for inference efficiency. This move suggests that while model scaling has been a priority, optimizing the underlying attention mechanisms for specific hardware is now a key driver for competitive advantage in the open-source community. [...] ## Papers to Read ### 1. Moonshot AI Open-Sources FlashKDA: CUTLASS Kernels for Kimi Delta Attention with Variable-Length Batching and H20 Benchmarks Source: MarkTechPost Moonshot AI releases FlashKDA, a high-performance implementation of Kimi Delta Attention that plugs directly into the flash-linear-attent
FlashKDA: High-Performance Attention Kernels Open-Sourced | Kimi (Moonshot AI) posted on the topic | LinkedInlinkedin.com · supportingAgree & Join LinkedIn By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy. # FlashKDA: High-Performance Attention Kernels Open-Sourced View organization page for Kimi (Moonshot AI) 46,353 followers We've open-sourced FlashKDA, our high-performance CUTLASS-based implementation of Kimi Delta Attention kernels. It delivers 1.72×–2.22× prefill speedup over the flash-linear-attention baseline on H20, and works as a drop-in backend for flash-linear-attention. Explore on GitHub: GitHub - MoonshotAI/FlashKDA: FlashKDA: high-performance Kimi Delta Attention kernels Chris Qin, graphic [...] Chris Qin, graphic Impressive numbers, especially with a drop-in backend. Kernel improvements like this directly expand the practical context and throughput envelope. Curious how performance scales across sequence lengths and batch sizes on H20. Simon Jakobs, graphic Wow thanks! Will use it when serving Kimi K3 fully EU hosted on meliou
Moonshot AI Open-Sources FlashKDA: CUTLASS Kernels for Kimi Delta Attention with Variable-Length Batching and H20 Benchmarks - MarkTechPostmarktechpost.com · supportingBy Asif Razzaq - The team behind Kimi.ai (Moonshot AI) just made a significant contribution to the open-source AI infrastructure space. The research team has made a significant contribution to the open-source AI infrastructure space. They released FlashKDA (Flash Kimi Delta Attention), a high-performance CUTLASS-based kernel implementation of the Kimi Delta Attention (KDA) mechanism. The FlashKDA library is available on GitHub under an MIT license. It delivers prefill speedups of 1.72× to 2.22× over the `flash-linear-attention` baseline on NVIDIA H20 GPUs, and works as a drop-in backend for the popular `flash-linear-attention` library. ### What Is Kimi Delta Attention, and Why Does It Matter? [...] FlashKDA is Moonshot AI’s open-source CUTLASS-based CUDA kernel for Kimi Delta Attention (KDA), delivering 1.72×–2.22× prefill speedup over the `flash-linear-attention` baseline on NVIDIA H20 GPUs. KDA extends Gated DeltaNet with fine-grained, channel-wise gating — it’s the core attenti
Moonshot AI Open-Sources FlashKDA: CUTLASS Kernels for Kimi Delta Attention with Variable-Length Batching and H20 Benchmarks - MarkTechPostmarktechpost.com · supportingBy Asif Razzaq - The team behind Kimi.ai (Moonshot AI) just made a significant contribution to the open-source AI infrastructure space. The research team has made a significant contribution to the open-source AI infrastructure space. They released FlashKDA (Flash Kimi Delta Attention), a high-performance CUTLASS-based kernel implementation of the Kimi Delta Attention (KDA) mechanism. The FlashKDA library is available on GitHub under an MIT license. It delivers prefill speedups of 1.72× to 2.22× over the `flash-linear-attention` baseline on NVIDIA H20 GPUs, and works as a drop-in backend for the popular `flash-linear-attention` library. ### What Is Kimi Delta Attention, and Why Does It Matter? [...] FlashKDA is Moonshot AI’s open-source CUTLASS-based CUDA kernel for Kimi Delta Attention (KDA), delivering 1.72×–2.22× prefill speedup over the `flash-linear-attention` baseline on NVIDIA H20 GPUs. KDA extends Gated DeltaNet with fine-grained, channel-wise gating — it’s the core attenti
Moonshot AI's FlashKDAthreads.com · supporting# Thread 595 views sung.kim.mw's profile picture sung.kim.mw Moonshot AI's FlashKDA A high-performance CUTLASS-based implementation of Kimi Delta Attention kernels. Achieves 1.72×–2.22× prefill speedup over the flash-linear-attention baseline on H20, and works as a drop-in backend for flash-linear-attention. github.com/Moons… GitHub - MoonshotAI/FlashKDA: FlashKDA: high-performance Kimi Delta Attention kernels github.com GitHub - MoonshotAI/FlashKDA: FlashKDA: high-performance Kimi Delta Attention kernels 15 1 1 1 Log in or sign up for ThreadsSee what people are talking about and join the conversation. Log in with username instead
Kimi.ai on X: "We've open-sourced FlashKDA, our high-performance CUTLASS-based implementation of Kimi Delta Attention kernels. It delivers 1.72×–2.22× prefill speedup over the flash-linear-attention baseline on H20, and works as a drop-in backend for flash-linear-attention. Explore on GitHub: https://t.co/QScjWsJqSy" / Xx.com · supportingLog inSign up ## Post user avatar Kimi.ai @Kimi\_Moonshot We've open-sourced FlashKDA, our high-performance CUTLASS-based implementation of Kimi Delta Attention kernels. It delivers 1.72×–2.22× prefill speedup over the flash-linear-attention baseline on H20, and works as a drop-in backend for flash-linear-attention. Explore on GitHub: github.com/MoonshotAI/Fla… GitHub - MoonshotAI/FlashKDA: FlashKDA: high-performance Kimi Delta Attention kernelsFrom github.com 3:25 PM · Jul 27, 2026168.8KViews user avatar Louis(没 Fable 5 用我就要发狂了) @0x\_Yao Jul 27 Tell me, who's the real hero? GIF 5.4K user avatar Kirolos Basalos @KeroBasalos Jul 27 [...] Kirolos Basalos @KeroBasalos Jul 27 I FUCKING love you guys. Ong, fuck “open” AI, definitely fuck anthropic, kinda fuck meta. My only hope is in Gemini, Kimi and Qwen. Y’all are beautiful goddamnit. 3.1K user avatar Bing Xu @bingxu\_ Jul 27 We’ve open-sourced PTX KDA, our fu