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    $300 Free Credits to Build on Google Cloud

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    Put your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
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    FlashKDA

    FlashKDA

    High-performance Kimi Delta Attention kernels

    ...It supports recurrent state input and output, variable-length batches, internal gating, query-key normalization, and beta activation. Builds can target the detected GPU architecture or multiple supported architectures for wheels and CI pipelines. The repository also provides correctness tests, benchmark material, a direct Python kernel API, and development helpers for CUDA and C++ tooling.
    Downloads: 0 This Week
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    BlockSparse

    BlockSparse

    Efficient GPU kernels for block-sparse matrix multiplication

    The blocksparse repository provides efficient GPU kernels (TensorFlow custom ops) for block-sparse matrix multiplication and convolution operations. The idea is to exploit block-level sparsity — i.e. treat matrices or weight tensors as composed of blocks, many of which may be zero or unused — to save compute and memory when sparsity patterns are structured. This is particularly useful in models like Sparse Transformers, where attention matrices or intermediate layers may adopt block-sparse patterns to scale better. ...
    Downloads: 0 This Week
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