#pytorch

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🔮 A refreshing functional take on deep learning, compatible with your favorite libraries

library to quickly export and deploy entire inference pipelines

Train and serve MoE models that do not fit in VRAM: fused 4-bit experts, QLoRA, CPU/NVMe offload, and fast inference on consumer NVIDIA GPUs.

Tools for computational pathology

Kubernetes-native remote dev environments for AI/ML engineers. Code locally, run on cluster GPUs — live file sync, SSH/tmux into pods, multi-pod PyTorchJob support, and detached job control.

A pytorch library for graph and hypergraph computation.

A convenient CLI to streamline LiteRT related development workflows, including converting, quantizing, compiling, managing, running, benchmarking and visualizing LiteRT (TFLite) models on various hardwares (CPU / GPU / NPU) across platforms (desktop, mobile or cloud).

Triton kernels for 4-bit MoE inference: grouped NF4/MXFP4 GEMM, INT4 GEMV, FP8 paged attention, and CPU/NVMe expert streaming.