Zihao Ye @ye_combinator
Proud to be an engineer. I'm building flashinfer (https://t.co/PabCM3ksjN) at @NVIDIA Opinions are my own. homes.cs.washington.edu/~zhye/ Seattle Joined October 2017-
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@JingyuanLiu123 This is the advantage of large nvlink domains or TPUs topology - the main reason to do PP is that you are bottlenecked on your DP comms and cannot scale TP further. But if you have high enough bandwidth across a large enough domain (like TPUs or NVL72), you don't need to do PP…
🚀 Presenting LiteASR: a method that halves the compute cost of speech encoders by 2x, leveraging low-rank approximation of activations. LiteASR is accepted to #EMNLP2025 (main) @emnlpmeeting
Sub-10-microsecond Haskell Sudoku solver implemented in hardware. unsafeperform.io/papers/2025-hs…
Tilelang now supports SM120 — give it a try if you have RTX 5090 🚀😎
🎉 Excited to share: We’ve open-sourced Triton-distributed MegaKernel! A fresh, powerful take on MegaKernel for LLMs—built entirely on our Triton-distributed framework. github.com/ByteDance-Seed… Why it’s awesome? 🧩 Super programmable ⚡ Blazing performance 📊 Rock-solid precision
One nice thing you can do with an interactive world model, look down and see your footwear ... and if the model understands what puddles are. Genie 3 creation.
🚀 Excited to announce day-0 support from @NVIDIAAIDev for @OpenAI's gpt-oss model in flashinfer v0.2.10! github.com/flashinfer-ai/… ✅ Speed-of-light Blackwell mxfp4/mxfp8 MoE kernels + attention-sink from trtllm-gen ✅ FA2/FA3 template-based attention-sink support for earlier…
🚀 Excited to announce day-0 support from @NVIDIAAIDev for @OpenAI's gpt-oss model in flashinfer v0.2.10! github.com/flashinfer-ai/… ✅ Speed-of-light Blackwell mxfp4/mxfp8 MoE kernels + attention-sink from trtllm-gen ✅ FA2/FA3 template-based attention-sink support for earlier…
Like SGLang? Want speed of light decode perf? Checkout: github.com/sgl-project/sg…
Powered by TensorRT-LLM Gen kernels. Available via flashinfer and TRT-LLM. 🚀
Great to see StepFun acknowledges the idea of Attention-FFN disaggregation from our Megascale-infer work and take to the next level 🚀🚀🚀 arxiv.org/abs/2504.02263
Great to see StepFun acknowledges the idea of Attention-FFN disaggregation from our Megascale-infer work and take to the next level 🚀🚀🚀 arxiv.org/abs/2504.02263
I’ve been starting to collaborate with the folks who are building FlashInfer: nice project and pretty amazing set of people! @ye_combinator @tqchenml and everyone.
I’ve been starting to collaborate with the folks who are building FlashInfer: nice project and pretty amazing set of people! @ye_combinator @tqchenml and everyone.
SGLang is an early user of FlashInfer and witnessed its rise as the de facto LLM inference kernel library. It won best paper at MLSys 2025, and Zihao now leads its development @NVIDIAAIDev. SGLang’s GB200 NVL72 optimizations were made possible with strong support from the…
SGLang is an early user of FlashInfer and witnessed its rise as the de facto LLM inference kernel library. It won best paper at MLSys 2025, and Zihao now leads its development @NVIDIAAIDev. SGLang’s GB200 NVL72 optimizations were made possible with strong support from the…
🔍 Our Deep Dive Blog Covering our Winning MLSys Paper on FlashInfer Is now live ➡️ nvda.ws/3ZA1Hca Accelerate LLM inference with FlashInfer—NVIDIA’s high-performance, JIT-compiled library built for ultra-efficient transformer inference on GPUs. Go under the hood with…
🔥 We introduce Multiverse, a new generative modeling framework for adaptive and lossless parallel generation. 🚀 Multiverse is the first open-source non-AR model to achieve AIME24 and AIME25 scores of 54% and 46% 🌐 Website: multiverse4fm.github.io 🧵 1/n
Been excited about this talk for a while, @SonglinYang4 on efficient architecture! Just started! youtube.com/watch?v=j4zJbr…
Another 🔥 blog about CUTLASS from @colfaxintl, this time focusing on the gory details of block-scaled MXFP and NVFP data types and Blackwell kernels for them. research.colfax-intl.com/cutlass-tutori…
We know Attention and its linear-time variants, such as linear attention and State Space Models. But what lies in between? Introducing Log-Linear Attention with: - Log-linear time training - Log-time inference (in both time and memory) - Hardware-efficient Triton kernels
🚀 Fast-dLLM: 27.6× Faster Diffusion LLMs with KV Cache & Parallel Decoding 💥 Key Features🌟 - Block-Wise KV Cache Reuses 90%+ attention activations via bidirectional caching (prefix/suffix), enabling 8.1×–27.6× throughput gains with <2% accuracy loss 🔄 -…
🎉CUTLASS 4.0 is here-bringing native #Python support for device-side kernel design, for ops like GEMM, Flash Attention, and more, powered by the new CuTe DSL. For the first time, you can write high-performance GPU kernels in Python with the same abstractions, APIs, and…
🚨🔥 CUTLASS 4.0 is released 🔥🚨 pip install nvidia-cutlass-dsl 4.0 marks a major shift for CUTLASS: towards native GPU programming in Python slidehelloworld.png docs.nvidia.com/cutlass/media/…

Tianqi Chen @tqchenml
18K Followers 1K Following AssistProf @CarnegieMellon. Distinguished Eng @NVIDIA. Creator of @XGBoostProject, @ApacheTVM. Member https://t.co/QYyfjQNp4p, @TheASF. Views are on my own
Kiv @kivdaychen
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Ce Gao @gaocegege
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Beidi Chen @BeidiChen
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Infinity Professional... @services843691
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gokul @gokulp01
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Tianqi Chen @tqchenml
18K Followers 1K Following AssistProf @CarnegieMellon. Distinguished Eng @NVIDIA. Creator of @XGBoostProject, @ApacheTVM. Member https://t.co/QYyfjQNp4p, @TheASF. Views are on my own
Kiv @kivdaychen
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Horace He @cHHillee
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Beidi Chen @BeidiChen
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Shriram Krishnamurthi... @ShriramKMurthi
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Ligeng Zhu @LigengZhu
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