MiniTensor: A Lightweight, High-Performance Tensor Operations Library
Fuente:
arXiv
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866915764139720704 |
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| author | Sarkar, Soumyadip |
| author_facet | Sarkar, Soumyadip |
| contents | We present MiniTensor, an open source tensor operations library that focuses on minimalism, correctness, and performance. MiniTensor exposes a familiar PyTorch-like Python API while it executes performance critical code in a Rust engine. The core supports dense $n$ dimensional tensors, broadcasting, reductions, matrix multiplication, reverse mode automatic differentiation, a compact set of neural network layers, and standard optimizers. In this paper, we describe the design of MiniTensor's architecture, including its efficient memory management, dynamic computation graph for gradients, and integration with Python via PyO3. We also compare the install footprint with PyTorch and TensorFlow to demonstrate that MiniTensor achieves a package size of only a few megabytes, several orders of magnitude smaller than mainstream frameworks, while preserving the essentials needed for research and development on CPUs. The repository can be found at https://github.com/neuralsorcerer/minitensor |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_00125 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MiniTensor: A Lightweight, High-Performance Tensor Operations Library Sarkar, Soumyadip Machine Learning Artificial Intelligence Mathematical Software We present MiniTensor, an open source tensor operations library that focuses on minimalism, correctness, and performance. MiniTensor exposes a familiar PyTorch-like Python API while it executes performance critical code in a Rust engine. The core supports dense $n$ dimensional tensors, broadcasting, reductions, matrix multiplication, reverse mode automatic differentiation, a compact set of neural network layers, and standard optimizers. In this paper, we describe the design of MiniTensor's architecture, including its efficient memory management, dynamic computation graph for gradients, and integration with Python via PyO3. We also compare the install footprint with PyTorch and TensorFlow to demonstrate that MiniTensor achieves a package size of only a few megabytes, several orders of magnitude smaller than mainstream frameworks, while preserving the essentials needed for research and development on CPUs. The repository can be found at https://github.com/neuralsorcerer/minitensor |
| title | MiniTensor: A Lightweight, High-Performance Tensor Operations Library |
| topic | Machine Learning Artificial Intelligence Mathematical Software |
| url | https://arxiv.org/abs/2602.00125 |