Chimera 3.0: Runs deep learning on OpenGL (github.com)

Chimera v3.0 is a bold, open‑source AI stack that claims to run large‑language‑model style workloads entirely on OpenGL shaders — no PyTorch, TensorFlow or CUDA required. The project asserts it maps tensors to GPU textures and implements matrix multiply, self‑attention and nonlinearities as fragment/compute shaders, uses cellular‑automata “physics” for state evolution, and stores/hunts concepts in a holographic texture memory. The result, Chimera says, is one‑pass generation (not token‑by‑token), no backpropagation (learning by “imprinting”/correlation), minimal dependencies (~10 MB), and universal GPU support from integrated Intel/AMD to Apple Silicon and Raspberry Pi. Technically, Chimera reports dramatic speed and footprint gains in its benchmarks (e.g., 2048×2048 matmul 1.84 ms vs 80.03 ms, self‑attention 1.8 ms vs 45.2 ms, end‑to‑end generation ~15 ms vs ~500 ms) and a ~9× smaller run‑time memory profile (~510 MB vs 4.5+ GB). The repo includes conversion tools (one‑time PyTorch→OpenGL), demos, docs and claims production readiness under an MIT license. If reproducible, the approach could shift many inference and edge‑AI workloads toward truly local, cross‑vendor GPUs and enable ultra‑low‑latency, energy‑efficient language models — though the community will need to validate the novel “holographic/imprinting” training paradigm and cross‑platform robustness.