5 posts
The pure-Rust NVIDIA CUDA Toolkit replacement adds nine new GPU deep-learning crates — generative diffusion, graph neural nets, Mamba SSMs, vision transformers, audio/speech, time-series, Bayesian DL, federated learning, and NAS — growing to ~320K lines across 37 crates with 9,568 passing tests. No CUDA SDK, no nvcc.
ToRSh is a pure-Rust, PyTorch-compatible deep-learning framework with native tensor sharding. 0.1.2 lands real AVX2/NEON SIMD for f32 ops and activations, a true zero-copy buffer pool (100% heap-block reduction on hot loops), and SIMD + parallel enabled by default.
ToRSh is a PyTorch-compatible deep-learning framework in pure Rust with native tensor sharding. The 0.1.1 release hardens the 33-crate workspace onto consistent, published crates.io dependencies and adds the new torsh-convert model-converter CLI.
SciRS2 is a pure-Rust SciPy/NumPy/scikit-learn replacement, and 0.3.0 is the biggest release yet — transformers, GNNs, diffusion, MoE/RLHF, Gaussian processes, MCMC, survival analysis, radar/compressed sensing, LOBPCG/AMG, plus new Julia and Python bindings. 19,644 tests, ~2.59M lines of Rust. No C, no Fortran.
Drop-in PyTorch replacement in pure Rust. Full SciRS2 integration (18 crates), SIMD CPU backend, autograd, and native sharding support. 2—3× faster inference, 50% less memory, single-binary deployment — no Python, no CUDA required.