Lightning AI Studios vs tinygrad

Lightning AI Studios is a cloud workspace for training models, running inference, building agents and AI apps, and coding collaboratively. Its persistent environments save installed packages, custom Python versions, and downloaded files. Studios offer browser-accessible VS Code and JupyterLab, plus AI copilots for AI and machine-learning work, including CUDA and PyTorch debugging. Teams can collaborate in real time on private projects, use a full-stack runtime, and deploy full-stack applications. Users can connect S3 buckets and EFS volumes through Lightning Drive, import models from Hugging Face and GitHub, or connect local IDEs such as VS Code, Cursor, and PyCharm over SSH. An SDK supports workflow automation. The Free plan is free and includes one active Studio, 50 GB persistent storage, and up to two concurrent GPUs; that Studio requires a restart every four hours. Pro and Teams plans have higher storage and GPU limits. Enterprise offers deployment in a customer VPC using AWS or GCP cloud credits.

tinygrad is a deep learning framework for neural network training, bringing together a tensor library, compiler, JIT, and training tools. It supports forward and backward automatic differentiation, and tensor operations run lazily when a tensor is realized. Neural network classes, optimizers, and state loading and saving are included. TinyJit captures and replays kernels in a decorated function, and tensors can be distributed across multiple GPUs. Listed accelerators include OpenCL, CPU, Metal, CUDA, AMD, NV, Qualcomm, and WebGPU. The project describes use in openpilot to run a driving model on a Snapdragon 845 GPU. It supports Python and local training, with multiple deployment targets and safetensors or PyTorch weights through model-specific loaders. Installation instructions cover pip, though the project recommends installing from source. tinygrad is free under the MIT license. Its API is described as stable for a while, though the project is not yet version 1.0.