GPU computing and LLMs

Bottom line

Nim can use GPUs, but it lacks the ecosystem required to make modern LLM training Nim-centric. CUDA, cuBLAS, cuDNN, and libtorch are reachable through C/C++ interoperability; the missing layer is the mature training stack above them.

Workload	Practicality
CUDA API calls	Possible
Custom GPU kernels	Possible, with substantial manual work
Small GPU tensor/NN experiments	Practical for experimentation
LLM inference control plane	A good fit
Production LoRA/QLoRA	Use Python/PyTorch
Large distributed pre-training	Not realistic

Community bindings such as nimcuda can expose CUDA APIs. Arraymancer supports tensor and numerical work but is not a PyTorch-equivalent LLM stack. libtorch bindings deserve careful maturity and ABI evaluation.

Nim is well suited to preprocessing, parsers, tokenizers, dataset streaming, native extensions, and the batching/queue/CLI/HTTP layer around established inference runtimes:

Nim application → C ABI → llama.cpp / ONNX Runtime / TensorRT / libtorch → GPU

Use Python/PyTorch for training, Rust for engine and resource-management infrastructure, and CUDA/C++ or Triton for specialized kernels. Nim is most compelling here for building data engines or GPU DSLs, not for recreating the LLM-training ecosystem.