Dylan Lim

(1/5) We’ve never enjoyed watching people chop Llamas into tiny pieces. So, we’re excited to be releasing our Low-Latency-Llama Megakernel! We run the whole forward pass in single kernel. Megakernels are faster & more humane. Here’s how to treat your Llamas ethically: (Joint with @jordanjuravsky, @stuart_sul, @OwenDugan, @dylan__lim, @realDanFu, @simran_s_arora, and @HazyResearch)

(1/6) We’re happy to share that ThunderKittens now supports writing multi-GPU kernels, with the same programming model and full compatibility with PyTorch + torchrun. We’re also releasing collective ops and fused multi-GPU GEMM kernels, up to 2.6x faster than PyTorch + NCCL. (Joint with @dylan__lim, @bfspector, and @HazyResearch. Generously supported by @cursor_ai)

(1/5) We’ve never enjoyed watching people chop Llamas into tiny pieces. So, we’re excited to be releasing our Low-Latency-Llama Megakernel! We run the whole forward pass in single kernel. Megakernels are faster & more humane. Here’s how to treat your Llamas ethically: (Joint with @jordanjuravsky, @stuart_sul, @OwenDugan, @dylan__lim, @realDanFu, @simran_s_arora, and @HazyResearch)

Spatial reasoning is a major challenge for the foundation models today, even in simple tasks like arranging objects in 3D space. #CVPR2025 Introducing LayoutVLM, a differentiable optimization framework that uses VLM to spatially reason about diverse scene layouts from unlabeled assets and open-ended language instructions 1/n

(1/5) @CKT_Conner, @dill_pkl, @emilyzsh, and I are excited to introduce Shard - a proof-of-concept for an infinitely scalable distributed system composed of consumer hardware for training and running ML models! Features: - Data + Pipeline Parallel for handling arbitrarily large models - Algorithmic load balancing for throughput optimization - Fault tolerance for unreliable machines