Dynamic Library Loading With Multi Backend Support Cuda Rocm Cpu

via “gpu acceleration with cuda and rocm support”

Single-file executable LLMs — bundle model + inference, runs on any OS with zero install.

Unique: Automatically detects and routes tensor operations to CUDA or ROCm kernels at runtime, with build-time selection of GPU backend, enabling single binary to leverage GPU acceleration without code changes

vs others: Faster inference than CPU-only execution (5-20x speedup on modern GPUs) because matrix multiplications run on GPU cores, versus CPU alternatives limited by single-thread performance

via “dynamic library loading with multi-backend support (cuda/rocm/cpu)”

8-bit and 4-bit quantization enabling QLoRA fine-tuning.

Unique: Uses a five-layer architecture where Layer 4 abstracts backend selection through dynamic library loading and operator registration, allowing Layer 1 (user API) to remain completely backend-agnostic. Implements fallback chains (CUDA → ROCm → CPU) with automatic detection of available hardware capabilities.

vs others: Provides cleaner abstraction than manual backend selection, and enables single-codebase deployment across NVIDIA/AMD/Intel GPUs without conditional imports or environment variables.