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Zihao Ye
263 posts
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Zihao Ye
@ye_combinator
往前看 别回头
Seattle
expye.com/about
Joined October 2017
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  • user avatar
    Zihao Ye
    @ye_combinator
    Feb 5, 2024
    (1/4) Announcing FlashInfer, a kernel library that provides state-of-the-art kernel implementations for LLM Inference/Serving. FlashInfer's unique features include: - Comprehensive Attention Kernels: covering prefill/decode/append attention for various KV-Cache formats (Page
    58K
  • user avatar
    Zihao Ye
    @ye_combinator
    May 13, 2025
    We’re thrilled that FlashInfer won a Best Paper Award at MLSys 2025! 🎉 This wouldn’t have been possible without the community — huge thanks to @lmsysorg’s sglang for deep co-design (which is crtical for inference kernel evolution) and stress-testing over the years, and to
    user avatar
    NVIDIA AI Developer
    NVIDIA
    @NVIDIAAIDev
    May 13, 2025
    🎉 Congratulations to the FlashInfer team – their technical paper, "FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving," just won best paper at #MLSys2025. 🏆 🙌 We are excited to share that we are now backing FlashInfer – a supporter and
    39K
  • user avatar
    Zihao Ye
    @ye_combinator
    Dec 19, 2024
    We are excite to announce FlashInfer v0.2! Core contributions of this release include: - Block/Vector Sparse (Paged) Attention on FlashAttention-3 - JIT compilation for customized attention variants - Fused Multi-head Latent Attention (MLA) decoding kernel - Lots of bugfix and
    34K
  • user avatar
    Zihao Ye
    @ye_combinator
    Mar 6, 2025
    Check out the intra-kernel profiler in flashinfer to visualize the timeline of each SM/warpgroup in the lifecycle of a CUDA persistent kernel: github.com/flashinfer-ai/… You can clearly understand how tensor/cuda cores overlapping, variable length load-balancing and fusion works.
    8.8K
  • user avatar
    Zihao Ye
    @ye_combinator
    Feb 5, 2024
    (1/3) Memory Bandwidth Efficient Shared Prefix Batch Decoding, brought to you by FlashInfer: blog: flashinfer.ai/2024/02/02/cas… Trying out our APIs: docs.flashinfer.ai/api/python/cas…
    28K
  • user avatar
    Zihao Ye
    @ye_combinator
    Aug 5, 2025
    🚀 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
    user avatar
    OpenAI
    @OpenAI
    Aug 5, 2025
    Our open models are here. Both of them. openai.com/open-models
    6.3K
  • user avatar
    Zihao Ye
    @ye_combinator
    Mar 27, 2023
    Check our #ASPLOS23 presentation tomorrow at 11 in session 4C at Grand D! We’ll be discussing our paper “SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning” - a compiler for sparse DL workloads built on @ApacheTVM (1/2)
    9.7K
  • user avatar
    Zihao Ye
    @ye_combinator
    Mar 11, 2025
    LLM is not all about tensor cores. categorical sampling under filters (top-p/top-k/min-p) are critical operators in llms as vocabulary size grows, flashinfer uses sorting-free rejection sampling algorithm for efficient sampling. checkout this great blog post written by @0xsling0
    user avatar
    Shanli Xing
    @shanli_xing
    Mar 11, 2025
    🚀Meet flashinfer.sampling—our sorting-free GPU kernels for lightning-fast #LLM sampling. Our implementation achieves over 50% reduction in sampling time. Blog post: flashinfer.ai/2025/03/10/sam…
    4.8K
  • user avatar
    Zihao Ye
    @ye_combinator
    Jul 20, 2023
    MLC-LLM now supports deploying Llama-2-70B-chat locally (needs an Apple Silicon Mac w/ 50GB VRAM to run).🦙💬🔥 The decoding speed can achieve ~10.0 tokens/s on an M2 Ultra! Try it out at: mlc.ai/mlc-llm/docs/g… and join our discord server: discord.gg/9Xpy2HGBuD
    GIF
    00:25
    user avatar
    Junru Shao
    @junrushao
    Jul 20, 2023
    (1/2) 🦙 Buckle up and ready for a wild llama ride with 70B Llama-2 on a single MacBook 💻 🤯 Now 70B Llama-2 can be run smoothly on an 64G M2 max with 4bit quantization. 👉 Here is a step-by-step guide: mlc.ai/mlc-llm/docs/g… 🚀 How about the performance? It's
    6.5K
  • user avatar
    Zihao Ye
    @ye_combinator
    Jan 28, 2023
    It's our first step toward Compiler for Sparsity in Deep Learning, and there are many intriguing research opportunities in this field. Check out our (experimental) repository at github.com/uwsampl/sparse…, more examples and tutorials are coming. typo: Ruihang Lai instead of Lao :)
    user avatar
    Luis Ceze
    @luisceze
    Jan 27, 2023
    Excited about our upcoming ASPLOS'23 paper on optimizing sparse tensor code using composable abstractions on top of @ApacheTVM by @ye_combinator @tqchenml @junrushao and Ruihang Lao. Great collaboration between @uwcse @CSDatCMU and @octoml! Pre-print: arxiv.org/abs/2207.04606
    5.7K
  • user avatar
    Zihao Ye
    @ye_combinator
    Jul 1, 2022
    I enjoy using copilot until I realize it's trying to predict my experiment result when I type <tab>.😅
  • user avatar
    Zihao Ye
    @ye_combinator
    Feb 18, 2023
    Retrospective on preparing SparseTIR artifact (for ASPLOS 2023):
    gist.github.com
    Retrospective on SparseTIR artifact
    Retrospective on SparseTIR artifact. GitHub Gist: instantly share code, notes, and snippets.
    2.3K
  • user avatar
    Zihao Ye
    @ye_combinator
    Feb 5, 2024
    Replying to @ye_combinator
    (4/4) More interesting results and analysis can be found in our blog post. FlashInfer has been adopted by MLC-LLM from @ApacheTVM , Punica from @abcdabcd987 , and the SGLang project from @lmsysorg (we are looking forward to seeing more !!). The AMD and Mac GPU implementations
    2K
  • user avatar
    Zihao Ye
    @ye_combinator
    Feb 5, 2024
    Replying to @ye_combinator
    (2/4) We analyzed attention kernels' performance bottleneck for different use cases, for example, grouped query attention has high operational intensity than original multi-head attention, and GQA's performance will be bounded by low CUDA Cores performance and we propose to use
    2.4K