DeepSpeed vs tinygrad
DeepSpeed
deepspeed.aitinygrad
tinygrad.orgSide by side
| Figure | DeepSpeed | tinygrad |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | — | Yes |
| Windows | — | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · Deep Learning Software | 8.0 (#12) | 7.5 (#23) |
| Accelerators | The getting-started guide names AMD ROCm, Intel Xeon CPU, Intel Data Center Max Series XPU, Intel Gaudi HPU and Huawei Ascend NPU support. | Listed accelerators include OpenCL, CPU, Metal, CUDA, AMD, NV, Qualcomm, and WebGPU. |
| Intended users | The project describes its audience as deep learning researchers and practitioners working on large-scale training and inference. | The project describes tinygrad as a framework for deep learning and neural network training. |
| License | The GitHub repository identifies DeepSpeed as an open-source project under the Apache-2.0 license. | The GitHub repository identifies the project license as MIT. |
| Purpose | DeepSpeed is a deep learning optimization library for distributed model training and inference. | tinygrad is an end-to-end deep learning stack with a tensor library, compiler, JIT, and tools for training. |
Plans
In their own words
tinygrad
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.