transmissions11
Follow
Stars
Open-source framework for the research and development of foundation models.
Named Tensors for Legible Deep Learning in JAX
A simple, performant, and scalable Jax LLM!
JaxPP is a library for JAX that enables flexible MPMD pipeline parallelism for large-scale LLM training
SpecTrax is a JAX-native library for neural networks and graph learning, built for performance, composability and modularity.
torchax is a PyTorch frontend for JAX. It gives JAX the ability to author JAX programs using familiar PyTorch syntax. It also provides JAX-Pytorch interoperability, meaning, one can mix JAX & Pytor…
A Python DSL to write Nvidia PTX for Hopper and Blackwell in JAX and PyTorch
Experimentation using the xla compiler from rust
Powering AWS purpose-built machine learning chips. Blazing fast and cost effective, natively integrated into PyTorch and TensorFlow and integrated with your favorite AWS services
JetStream is a throughput and memory optimized engine for LLM inference on XLA devices, starting with TPUs (and GPUs in future -- PRs welcome).
ATLAS Autoformalized Textbook Library At Scale
ThunderKittens LCF forward non-causal attention kernel benchmarked against FlashAttention-2 and FlashAttention-3 on Hopper.
This module defines a type system for distributed training code, based off of JAX's sharding in types, but adapted for the PyTorch ecosystem.
FoundationDB - the open source, distributed, transactional key-value store
Seastar boilerplate project with cmake
FlashMLA: Efficient Multi-head Latent Attention Kernels
DeepGEMM: clean and efficient BLAS kernel library on GPU
DeepEP: an efficient expert-parallel communication library
NoSQL data store using the SEASTAR framework, compatible with Redis
mTCP: A Highly Scalable User-level TCP Stack for Multicore Systems
Algorithm powering the For You feed on X