Tools · SPBoucher
Menu

PyTorch

by PyTorch Foundation (Linux Foundation) · Framework · also known as torch, LibTorch

Open-source deep-learning framework with eager tensors, autograd, torch.compile and distributed training, governed by the PyTorch Foundation and the base of most LLM tooling

BSD-3-Clause licenseOpen sourceFreeLocalSelf-hostedCloudSelf-hostable
Visit official site Compare

Checked Nov 2, 2026

Overview

PyTorch is the BSD-3-Clause deep-learning framework originally developed at Facebook AI Research and governed since 2022 by the PyTorch Foundation under the Linux Foundation. It provides GPU-accelerated tensors with automatic differentiation, an eager execution model that behaves like ordinary Python, torch.compile for graph capture and kernel fusion, torch.distributed for data, tensor and pipeline parallelism, and a LibTorch C++ distribution. Backends include CUDA, ROCm, MPS on Apple Silicon and CPU, and the stable release requires Python 3.10 or later.

Most of the modern LLM stack is built on it: Hugging Face Transformers defines models in PyTorch, vLLM, Unsloth and Axolotl run on it, and Whisper and Sentence Transformers depend on it. The ecosystem adds torchvision, torchaudio, PyTorch Lightning, TorchServe, ExecuTorch for on-device deployment and a landscape of foundation-hosted projects. For researchers it is the default choice because papers and reference implementations target it. Caveats: wheels are large and tied to specific CUDA versions, MPS on Macs lags CUDA in operator coverage and performance, and major releases occasionally deprecate APIs that downstream libraries depend on.

Capabilities

  • GPU tensors with automatic differentiation
  • Eager execution plus torch.compile
  • Distributed training (DDP, FSDP, tensor and pipeline parallel)
  • CUDA, ROCm, MPS (Apple Silicon) and CPU backends
  • LibTorch C++ API and ExecuTorch for edge
  • Large ecosystem: torchvision, torchaudio, Lightning, TorchServe

Typical workflows

  1. Prototype a model in a notebook and scale it with FSDP
  2. Run Transformers models on an Apple Silicon Mac via MPS
  3. Export a trained model for serving or on-device inference

Strengths

  • Default framework for research code and LLM libraries
  • Vendor-neutral foundation governance
  • Permissive BSD license

Limitations

  • Large, CUDA-version-specific wheels
  • MPS backend less complete than CUDA
  • API deprecations across major versions

Prerequisites

  • Python 3.10+; CUDA or ROCm drivers for GPU use

Who it is for

  • ML researchers and students
  • Engineers building training and inference systems

Alternatives & comparison

  • Axolotl

    YAML-configured open-source framework for fine-tuning and post-training LLMs: full, LoRA and QLoRA training, preference and RL methods, multimodal models and multi-GPU or multi-node runs

    Open sourceOpen source
  • Core ML

    Apple's on-device machine-learning runtime for iOS and macOS apps, with the open-source coremltools package for converting PyTorch and other models to its format

    MixedFree
  • Create ML

    Apple's app and Swift framework for training image, text, sound, tabular and activity models on a Mac without writing training code, exporting directly to Core ML

    ProprietaryFree
  • JAX

    Python library for NumPy-style array computing with composable transformations (jit, grad, vmap, shard_map) compiled through XLA to CPU, GPU and TPU

    Open sourceOpen source
  • Metal

    Apple's low-level GPU programming framework for graphics and parallel compute on iOS, macOS and visionOS, the layer beneath MLX, Core ML and most Apple Silicon ML runtimes

    ProprietaryFree
  • MLX

    Apple's open-source array framework for machine learning on Apple Silicon, with NumPy-like Python, C++ and Swift APIs and unified CPU/GPU memory

    Open sourceOpen source
  • NumPy

    Foundational Python library for n-dimensional arrays, vectorized math, linear algebra, random numbers and the array API used by the scientific stack

    Open sourceOpen source
  • scikit-learn

    BSD-licensed Python machine-learning library for classification, regression, clustering, dimensionality reduction, preprocessing and model selection on NumPy and SciPy

    Open sourceFree
  • Transformers

    Hugging Face's model-definition library for text, vision, audio, video and multimodal models, with Pipeline, Trainer and generate APIs used as the reference by training and inference engines

    Open sourceOpen source
  • Unsloth

    Open-source framework for fine-tuning and running LLMs with reduced memory, covering LoRA, QLoRA, full fine-tuning and RL, GGUF export, Dynamic quantizations and a local Studio/Desktop UI

    Open sourceOpen source
Compare PyTorch with its alternatives →

Sources & verification

Primary sources consulted for this record. Claims are tied to the fields each source supports; the automated link checker re-verifies every URL on a schedule.

Confidence
High
Reviewed
Nov 1, 2026
Links
Broken · Nov 2, 2026

Spotted an error?

Broken link, outdated license, wrong category, or a tool that no longer exists? Tell us; reports are reviewed by the editor.

MLX LM

Apple Inc. · Apple Silicon ML & MLX

In-depth entry

Python package and CLI for running, serving, quantizing and fine-tuning large language models on Apple Silicon with MLX

MITOpen sourceFreeLocalNov 2, 2026

Ray

Ray project (Anyscale and contributors) · Distributed & parallel computing

In-depth entry

Open-source Python framework for distributing tasks, actors and ML workloads across a cluster, with libraries for data processing, training, tuning, serving and reinforcement learning

Apache-2.0Open sourceFreeLocalSelf-hostedBroken

Hugging Face Hub

Hugging Face · Training & fine-tuning

In-depth entry

Hosting platform for open models, datasets and Spaces demo apps built on git repositories and Xet storage, with Inference Providers, Endpoints, Jobs and an open-source Python client and CLI

MixedFreemiumPaidLocalNov 2, 2026

scikit-learn

scikit-learn developers · Mathematical & statistical computing

BSD-licensed Python machine-learning library for classification, regression, clustering, dimensionality reduction, preprocessing and model selection on NumPy and SciPy

BSD-3-ClauseFreeOpen sourceLocalNov 2, 2026

Sentence Transformers

Hugging Face · Embeddings, reranking & retrieval

Python library for computing and training embedding, cross-encoder reranker, sparse and multi-vector retrieval models, maintained by Hugging Face with thousands of pretrained models on the Hub

Apache-2.0Open sourceFreeLocalSelf-hostedNov 2, 2026

Unsloth

Unsloth AI · Training & fine-tuning

Open-source framework for fine-tuning and running LLMs with reduced memory, covering LoRA, QLoRA, full fine-tuning and RL, GGUF export, Dynamic quantizations and a local Studio/Desktop UI

Apache-2.0Open sourceFreeLocalSelf-hostedNov 2, 2026