Rust+Vulkan training backend runs 143 Transformer architectures without CUDA or PyTorch

PhysicsDisastrous462 · reddit · 2026-09-17

A developer released Hierarchos-Native, a native Rust + Vulkan training and inference backend covering 143 canonical Transformer architectures (224 model-type spellings once Hugging Face modeltype aliases are counted), with no CUDA or PyTorch dependency.

Implemented capabilities include:

The architecture registry spans Llama, Qwen, Gemma, Mistral/Mixtral, DeepSeek, Phi, OLMo, Granite, Cohere, BERT-family, T5/BART-family and more, with architecture-specific Vulkan kernels for newer designs rather than one generic approximation.

The author explicitly does not claim this is a CUDA/PyTorch replacement, nor that supporting a model type means every configuration and multimodal component is validated: the registry describes native text-graph implementations, and vision/audio towers in multimodal packages are outside the native text backend. Real-world compatibility still needs broader testing across models and hardware.

The interesting part is the direction: Vulkan runs across NVIDIA, AMD, Intel and much integrated/mobile hardware, so a maturing native training stack could loosen ML training infrastructure's dependence on a single vendor ecosystem. Development and testing happened on AMD RDNA 3 hardware, including a ROG Ally Z1 Extreme, which surfaces assumptions hidden when ML software is built only around high-end CUDA systems. The repo ships Rust source, Vulkan compute shaders/SPIR-V, a CLI, the Hierarchos runtime, architecture docs and validation utilities.

Related event: Rust+Vulkan Training Backend Supports 143 Transformer Architectures(2 posts)→

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