Standard Kernel Co.

AI progress increasingly depends on how efficiently workloads run on hardware. @Standard_Kernel is tackling this challenge at the kernel level, unlocking more performance from modern GPUs. We're proud to lead their seed with @generalcatalyst, @CoreWeave, @felicis, & @ericsson

It’s rare to find founders so perfectly and uniquely suited to solve a problem, let alone a problem of this magnitude and importance. Proud to lead @Standard_Kernel’s seed round.

Anne is killing it. Here's my quote from the press release Kernel generation is key for improving performance and efficiency of AI hardware. As fleet sizes for users of AI hardware get larger, and more hardware diversity is introduced, Standard Kernel becomes key to deployment.”

Excited to share @Standard_Kernel's seed round and some reflections on what we’ve learned about kernel generation and what we believe is next. Grateful to our amazing team, supporters, and the broader community pushing this space forward.

The choice of timing method (CUDA events, CUDA graphs, Nsight Compute, PyTorch profiler, etc.) can result in different measured GPU performance, and the effect depends on the workload. For microsecond-scale kernels, true execution time is often indistinguishable from measurement overhead and system variance. (7/9)

Warm caches can make GPU kernels appear much faster, and sensitivity to cold vs. warm cache varies by workload. Be explicit about which condition you measure, as the appropriate choice depends on the behavior you want to evaluate. (6/9)

GPU timing is deceptively hard. Power limits, thermal state, clock behavior, caching, and measurement method all affect results in subtle ways. We explored sources of timing variation to obtain more reliable results for kernel benchmarking. (1/9)

Getting identical hardware from cloud providers is not guaranteed. Across three cloud providers all listing “A100 80GB,” we received different variants with differing clock limits, power caps, and driver environments. When benchmarking an identical GEMM, the runtime distributions formed distinct clusters for each provider. (9/9)

Compilation flags can change GPU performance on the same kernel + hardware. Sensitivity varies by workload, so uncontrolled flags can look like real algorithmic gains when they're just compiler effects. (8/9)

Excited to share what friends and I have been working on at @Standard_Kernel We've raised from General Catalyst (@generalcatalyst), Felicis (@felicis), and a group of exceptional angels. We have some great H100 BF16 kernels in pure CUDA+PTX, featuring: - Matmul 102%-105% perf of cuBLAs in 100 lines of code - Attention 104% perf of FlashAttention3 in 500 lines - Fused Llama3 FFN 120% perf of PyTorch (gpt-fast) Reach out if you want to work on AI kernel gen with us!