ML Frameworks
9 open source tools compared. Sorted by stars. Scroll down for our analysis.
By Erik Loyd, SaaS CEO and former COO/CFO of an AWS Premier Partner.
| Tool | Stars | Velocity | Score |
|---|---|---|---|
 Transformers Model framework for state-of-the-art ML | 163.3k | +255/wk | 91 |
 pytorch Tensors and Dynamic neural networks in Python with strong GPU acceleration | 102.1k | +149/wk | 85 |
 MLX Array framework for Apple silicon | 27.8k | +81/wk | 88 |
 Paddle PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署） | 24.0k | +4/wk | 92 |
torchtitan A PyTorch native platform for training generative AI models | 5.6k | +13/wk | 82 |
 xla A machine learning compiler for GPUs, CPUs, and ML accelerators | 4.4k | +26/wk | 80 |
 torchrec Pytorch domain library for recommendation systems | 2.6k | +4/wk | 78 |
 coreai-models Model export recipes, Python primitives, and Swift runtime utilities for on-device AI | 1.5k | +10/wk | 74 |
 UniRL UniRL is a Framework for Unified Multimodal Model Reinforcement Learning | 869 | +10/wk | 59 |
Stay ahead of the category
New tools and momentum shifts, every Wednesday.
Our Analysis
Hugging Face Transformers gives you access to 400,000+ pre-trained models with a consistent Python API for text generation, translation, summarization, image classification, and more. Load a model in three lines of code, run inference, done. Apache 2.0. This is the standard library for working with transformer models. PyTorch, TensorFlow, and JAX backends. The `pipeline` API lets you go from zero to a working model in literally one line: `pipeline('sentiment-analysis')('I love this')`. For fine-tuning, the Trainer API handles the training loop, checkpointing, and evaluation. The library is free. Hugging Face Hub (the model hosting platform) has a free tier with unlimited public models and 25GB private storage. Pro ($9/mo) adds more private storage. Enterprise plans exist for organizations needing governance and deployment at scale. The catch: running large models requires serious GPU hardware. A 7B parameter model needs ~14GB VRAM just for inference. The library is free but the compute is not. Hugging Face Inference Endpoints (managed deployment) starts at $0.06/hr for CPU, $0.60/hr for GPU. Also, the library is enormous; it pulls in PyTorch (~2GB) and the ecosystem of dependencies is heavy. For production inference specifically, look at vLLM or llama.cpp for better performance.
PyTorch is the framework most modern AI is built on. If a model made headlines in the last few years, there's a good chance it was trained in PyTorch. It's a Python library for building and training neural networks, with the heavy math running on your GPU instead of your CPU. Free, BSD-licensed, backed by the Linux Foundation. What made it win over TensorFlow was feeling like normal Python. You write code that runs line by line, you can drop a debugger in the middle, and it behaves the way you'd expect. The ecosystem is enormous: pretrained models, training libraries, deployment tools, and tutorials for nearly everything. Running it yourself is mostly about hardware. The install is trivial. Matching CUDA versions to your GPU drivers is the part that eats an afternoon. Solo learners can run it on a laptop or a free cloud notebook. Teams doing real training rent GPUs, and that bill, not the software, is the cost. PyTorch itself never charges you. The catch: PyTorch gives you the engine, not the car. It won't manage your experiments, your data pipelines, or your deployment. For those you'll reach for other tools like Lightning, Hugging Face, or MLflow. And if you want maximum inference speed on non-NVIDIA hardware, expect rough edges. NVIDIA is still where PyTorch runs best.
MLX is Apple's machine learning framework built specifically for Apple Silicon. It uses the unified memory architecture (where CPU and GPU share the same RAM) so there's no copying data back and forth like you'd do with CUDA on NVIDIA GPUs. This matters because most ML frameworks were built for NVIDIA hardware. Running PyTorch on a Mac works but doesn't fully exploit what Apple Silicon can do. MLX does. The API is intentionally similar to NumPy and PyTorch, so the learning curve is gentle if you know those. MIT, backed by Apple's ML research team. Growing fast and gaining serious traction. The catch: Mac-only. If your production environment is Linux with NVIDIA GPUs, MLX doesn't help you there. It's best for local development, experimentation, and running models on Mac hardware. The ecosystem of pre-built models and integrations is growing but still much smaller than PyTorch/CUDA.
PaddlePaddle is Baidu's deep learning framework, in the same category as PyTorch and TensorFlow. It is a full platform for building, training, and deploying models, with unified dynamic and static graphs, automatic parallelism for large-model training, and deployment tooling across a wide range of hardware. Open sourced since 2016, Apache-2.0, free. Installing it is the standard framework routine: pip install the build that matches your hardware and CUDA version, then write Python. It is written mostly in C++ and Python, and it carries a large ecosystem of model libraries and end-to-end development kits on top of the core. The current line is version 3.x, actively released. For a developer or team already committed to a framework, Paddle is worth knowing but rarely worth switching to unless you have a specific reason. Its center of gravity is China: the biggest community, the deepest tooling, and the best documentation are there, and much of it is Chinese-first. Solo researchers and teams alike will find PyTorch has the larger global ecosystem and more tutorials in English. The catch is exactly that ecosystem gravity. Paddle is a serious, industrial-grade framework with real strengths in large-model parallelism and hardware breadth, but outside its home ecosystem you will hit more friction: fewer English answers, smaller third-party support, harder hiring for it. Pick it when you have a concrete pull toward Paddle-specific tooling or hardware, not as a default.
TorchTitan is the PyTorch team's framework for training large language models at scale. It combines PyTorch's distributed training primitives into a working system: data parallelism, tensor parallelism, pipeline parallelism, and activation checkpointing. Fully free, BSD-licensed, no cloud requirement. This is not a weekend project. You need multi-GPU clusters (H100 or equivalent) and familiarity with SLURM or cloud HPC to orchestrate across nodes. The project supports multiple architectures including Llama 3 and 4, DeepSeek V3, Qwen3, and Flux for image generation. It ships with configuration examples but expects you to already understand distributed training before you start. ML researchers and infrastructure teams training large-scale models from scratch have the cleanest PyTorch-native starting point available. Solo developers building on top of existing models have no use for this. It is infrastructure for teams running their own training clusters who want to stay in the PyTorch ecosystem. The catch: TorchTitan is a reference implementation, not a hardened production system. The APIs are bleeding-edge and change frequently.
XLA is the machine learning compiler that takes models from JAX, PyTorch, or TensorFlow and optimizes them for GPUs, CPUs, TPUs, and other accelerators. If you use any of those frameworks you're already running XLA, you just don't see it. The OpenXLA repo is where the compiler itself lives, and it's open source under Apache 2.0. Self-hosting in the normal sense doesn't apply. XLA is infrastructure that ML frameworks call into. End developers interact with it through `jax.jit`, `torch.compile` (with the OpenXLA backend), or TensorFlow's XLA flags. Building from source is a serious commitment that involves Bazel, platform-specific dependencies, and several days of patience. Most teams should consume the pre-built versions their framework ships. Use this directly if you're doing ML infrastructure work: writing a new framework, adding hardware support, or debugging compilation issues. Solo developers and small teams: you don't interact with XLA directly, you benefit from it. Large teams doing custom silicon or model serving optimization: this is the layer you're working in. The catch: governance still effectively sits with Google despite the OpenXLA branding. The Code of Conduct points back to TensorFlow governance, and Google contributes the majority of the changes. Apache 2.0 protects against the worst outcomes, but "open governance" reads aspirationally here.
TorchRec is Meta's PyTorch library for training recommendation systems at scale. It is the engine behind the kind of "you might also like" model that runs on a feed of billions of items, packaged as a library you can use too. Free, BSD-3 licensed, used in production at Meta, Twitter, and Databricks. Running this means GPU clusters, sharded embedding tables, and pipelined training. It gives you the parallelism primitives (model sharding, communication kernels, a planner that figures out the layout) but you bring the recommender model and the data pipeline. Single-GPU experiments work fine, the value shows up when your embedding table no longer fits in memory. Solo: skip unless you are researching recsys at scale. Small teams: probably overkill, simpler libraries cover most product needs. Large teams shipping recommendations against tens of millions of items: this is what Meta built for itself, which is the strongest signal you will find. The catch is that this is infrastructure, not a recommender. You still need to know what model you are training, how you are sharding it, and how to feed it. TorchRec makes the hard parts possible, not easy.
Apple's coreai-models is the toolkit for getting AI models running on Apple hardware, on the device, not in the cloud. It gives you export recipes that convert popular open-source models into Apple's Core AI format, Python tools for authoring custom models in PyTorch, and a Swift package with the runtime pieces to actually run them on macOS and iOS. This is how you ship an app that does inference locally instead of calling out to an API. There's nothing to host, it's a library and a set of recipes you build into your own app. The work is real but well-scoped: you convert a model, wire up the Swift runtime, and test it on-device. For developers already in Apple's ecosystem, this is the sanctioned path rather than fighting the conversion tooling yourself. App developers who want on-device AI, for privacy, offline capability, or to skip per-call API costs, are exactly who this is for. If you're not shipping to Apple platforms, it's irrelevant, look at GGUF and llama.cpp or ONNX instead. The payoff here is models that run on the Neural Engine with no network round trip and no data leaving the phone. The catch: this is Apple-only by design, so it ties you to their platforms and their format. On-device inference also means you're bounded by what the hardware can run, you're not loading a giant frontier model onto a phone. It's BSD-licensed and free, but it's a developer toolkit, there's nothing here unless you're building something.
UniRL is a training framework for teaching AI models new behavior through reinforcement learning, across every major model type at once. Most RL training code is built for one kind of model. UniRL, from Tencent's Hunyuan team, runs a single RL loop over image and video diffusion models, large language models, vision-language models, and unified multimodal models. It ships real algorithms (Flow-DPPO, DRPO, CPPO) each backed by a paper, alongside standard baselines like GRPO. There is nothing hosted here, and the ops burden is heavy. This is distributed training infrastructure: Ray for orchestration, FSDP for sharding models across GPUs, Hydra for configuration. You need a real GPU cluster and the experience to drive it. Getting a run going is a project, not an afternoon. ML engineers and researchers doing RL post-training on multimodal models are the only audience, and for them the value is having one framework instead of stitching together separate pipelines per model family. If you're not training models at this level, this is not for you. There is no lighter on-ramp; the complexity is inherent to the work. The catch: it's research-grade infrastructure, Apache-licensed and free, but free in the way a jet engine is free if someone hands you one. The cost is the cluster, the GPU hours, and the expertise. Expect rough edges, sparse docs, and the assumption that you already know how RL training works.