Dev tools · alternatives · 2026

24 alternatives to Fast,Compiled deep-learning based modules for inferencing on CPUs

Hi HN,I am Anubhav from RamanLabs.We have been developing dedicated modules based on deep-learning for purposes like face-detection,object-detection,pose-estimation etc. We hope to make it easy for developers,hobbyists to integrate such functionalities into their existing app/pipeline at the cost of a few milliseconds.All our modules run end to end in super-realtime even on consumer-grade CPUs[0]. For now we provide only Python based API. We provide Demo for each of the modules to allow testing for your desired data distribution.We also have a blog[1] where we hope to add more technical…

Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Fast,Compiled deep-learning based modules for inferencing on CPUs launched in 2022; newer entries below may have overtaken it.

1
Inferless▲749
Deploy any machine learning models in minutes
2025 · inferless.com · its alternatives →
2
TensorFlow 1.0▲823
Open-source machine learning library by Google
2017 · tensorflow.org · its alternatives →
3
Netmind Power▲272
Hi HN, We just opened public access to Qwen3‑Coder‑480B‑A35B‑Instruct via a REST/SDK API. Why it might be interesting: - Fully open-source (Apache‑2.0): https://github.com/QwenLM/Qwen3-Coder - 480B params (35B active MoE), long context up to 1M tokens with YaRN - Matches Claude Sonnet/GPT‑4o on SWE-Bench Verified (81.6%) - Free tier: 100K tokens/month
2023 · netmind.ai · its alternatives →
4
Integrate Computer-vision in your app with just 2 lines of code▲5
Hi HN, I am Anubhav from RamanLabs. We have been developing end to end Computer-vision modules to make it easy for developers, hobbyists to integrate such functionality into their applications with minimal amount of code. All modules are developed to run real-time on consumer-grade CPUs[0]. For now we are releasing only Python-language SDK. Demos are provided to allow users to test performance on desired data-distribution. Framework powering these modules in completely written in Nim language, which under the hood wraps some Operation's implementation provided by libraries like…
2022 · ramanlabs.in · its alternatives →
5
DeepFace – A lightweight deep face recognition library for Python▲257
DeepFace is a leading open-source library for facial recognition and facial attribute analysis, and the de facto standard in Python. It wraps multiple state-of-the-art models that have reached — and even surpassed — human-level accuracy in recognizing faces. By the numbers (as of early 2025): 15,000+ stars on GitHub; ~4 million installations via pip; 800+ citations in academic papers Whether you're building a cutting-edge AI project or simply exploring facial recognition, DeepFace makes advanced capabilities accessible with just a few lines of code.
2025 · github.com · its alternatives →
6
I built a tensor library from scratch in C++/CUDA▲119
Hi HN, Over the past few months, I've been building `dsc`, a tensor library from scratch in C++/CUDA. My main focus has been on getting the basics right, prioritizing a clean API, simplicity, and clear observability for running small LLMs locally. The key features are: - C++ core with CUDA support written from scratch. - A familiar, PyTorch-like Python API. - Runs real models: it's complete enough to load a model like Qwen from HuggingFace and run inference on both CUDA and CPU with a single line change[1]. - Simple, built-in observability for both Python and C++. Next on the roadmap is…
2025 · github.com · its alternatives →
7
We made our own inference engine for Apple Silicon▲186
We wrote our inference engine on Rust, it is faster than llama cpp in all of the use cases. Your feedback is very welcomed. Written from scratch with idea that you can add support of any kernel and platform.
2025 · github.com · its alternatives →
8
Richard – A CNN written in C++ and Vulkan (no ML or math libs)▲184
This started out as a personal effort to learn more about machine learning. It's currently a CLI app where you give it a JSON file specifying your network architecture and hyperparameters and point it to your training data, then invoke it again in 'eval' mode with some data it's not seen before and it will try to classify each sample. I don't see many other people using Vulkan for GPGPU, and there may be many good reasons for that, but I wanted to try something a bit different. I've made every attempt to make the code very clean and readable and I've written up the math in…
2024 · github.com · its alternatives →
9
Weights & Biases▲110
Developer tools for deep learning & machine learning
2019 · wandb.ai · its alternatives →
10PO
PlaidML, open source deep learning for any GPU▲52
Our company Vertex.AI has been working on this for a while but this is the first public release. We're starting with using PlaidML to bring OpenCL support to Keras and more frameworks, platforms, etc are coming. Yes, this means you can use use your AMD GPU for deep learning dev. Sorry, no Mac or Windows support yet although the brave can try building from source (it should work). http://vertex.ai/blog/announcing-plaidml https://github.com/plaidml/plaidml
2017 · its alternatives →
11
I wrote a minimal deep learning library from scratch in Python▲25
A minimalist deep learning library written from scratch in Python - sradc/SmallPebble
2021 · github.com · its alternatives →
12
End-to-end deep learning experimentation platform for Tensorflow▲151
AI Infra / AI Orchestration / AI Control Plane. Contribute to polyaxon/polyaxon development by creating an account on GitHub.
2017 · github.com · its alternatives →
13
Pip install inference, open source computer vision deployment▲71
Deploying vision models is time consuming and tedious. Setting up dependencies. Fixing conflicts. Configuring TRT acceleration. Flashing (and re-flashing) NVIDIA Jetsons. A streamlined, developer-friendly solution for inference is needed. We, the Roboflow team, have been hard at work open sourcing Inference, an open source vision deployment solution. Our solution is designed with developers in mind, offering a HTTP-based interface. Run models on your hardware without having to write architecture-specific inference code. Here's a demo showing how to go from a model to GPU inference on a video…
2023 · github.com · its alternatives →
14
I built a deep learning engine from scratch in Python▲30
I’ve spent the last few months building a deep learning engine completely from scratch in Python (using only math and random). What started as a basic linear algebra calculator project grew into a symbolic tensor system with autodiff, custom matrix ops, attention mechanisms, LayerNorm, GELU, and even a text generation demo trained on the Brown corpus. I'm still an undergrad, so my main goal is to deeply understand how deep learning actually works under the hood - gradients, attention, backpropagation, optimizers - by building it step-by-step with full visibility into everything, and without…
2025 · github.com · its alternatives →
15
DeepSwarm – Optimising CNNs Using Swarm Intelligence▲73
Neural Architecture Search Powered by Swarm Intelligence 🐜 - Pattio/DeepSwarm
2019 · github.com · its alternatives →
16
TensorPort▲143
Help teams apply machine learning to real-world applications
2017 · its alternatives →
17HP
High-performance ahead-of-time compiler for Machine Learning▲83
2018 · scss.tcd.ie · its alternatives →
18
Deepkit▲69
The collaborative and analytical AI training suite
2020 · deepkit.ai · its alternatives →
19
Neuropod – Uber ATG's open source deep learning inference engine▲80
A uniform interface to run deep learning models from multiple frameworks - uber/neuropod
2020 · github.com · its alternatives →
20
TensorFlow 1.0▲80
Open-source machine learning library by Google
2018 · tensorflow.org · its alternatives →
21
Compile ML models into dependency-free source code for easy deployment▲37
I'm a machine learning engineer who always found it annoying to integrate ML models into phone apps, smartwatch apps, microcontroller firmware etc... Why do we need all these libraries and runtimes with all the overhead, compatibility issues and other headaches, when it's just some math to be executed? So I made a compiler that simply converts the model into plain source code with no dependencies, and it actually solved all my deployment problems. Now I'm curious if it can help anyone else too. Through the link you can submit your model file (Keras h5, onnx soon to be supported), and I'll…
2023 · waveworks.dk · its alternatives →
22
Fast.ai Camera App for CNNs▲34
Camera web app that connects to a (fast.ai) CNN. Contribute to TylerNoblett/fastai-cnn-camera-app development by creating an account on GitHub.
2020 · github.com · its alternatives →
23
Deep-ML▲13
We just launched Deep-ML Labs, a new section on Deep-ML Each Lab is a hands-on challenge where you implement parts of real models, activations, layers, optimizers, normalization, even small transformers — directly in PyTorch (and sometimes NumPy). You can test your implementations on actual datasets, see how your results compare to others, and understand what’s really happening under the hood of deep learning frameworks. let me know what you guys think and if we should work on making more of these questions
2025 · deep-ml.com · its alternatives →
24PL
Python library to scan ML models for vulnerabilities▲20
Hi! I’ve been working on this automatic scanner for ML models to detect issues like underperforming data slices, overconfidence in predictions, robustness problems, and others. It supports all main Python ML frameworks (sklearn, torch, xgboost, …) and integrates with the quality assurance solution we are building at Giskard AI (https://giskard.ai) to systematically test models before putting them in production. It is still a beta and I would love to hear your feedback if you have the time to try it out. We have quite a few tutorials in the docs with ready-made colab notebooks to…
2023 · docs.giskard.ai · its alternatives →

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