Rapid-MLX
The fastest local AI engine for Apple Silicon. 4.2x faster than Ollama, 0.08s cached TTFT, 100% tool calling. 17 tool parsers, prompt cache, reasoning separation, cloud routing. Drop-in OpenAI replacement. Works with Claude Code, Cursor, Aider. (by raullenchai)
PaddlePaddle
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） (by PaddlePaddle)
| Rapid-MLX | PaddlePaddle | |
|---|---|---|
| 6 | 9 | |
| 2,756 | 23,951 | |
| 90.1% | 0.4% | |
| 9.8 | 10.0 | |
| 4 days ago | 3 days ago | |
| Python | C++ | |
| Apache License 2.0 | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Rapid-MLX
Posts with mentions or reviews of Rapid-MLX.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2026-05-05.
-
Chrome's Gemini Nano Prompt API: A Step-by-Step Guide
💡 💡 Make the fallback cheap to operate. The whole point of using Nano on the supported path is reduced cost. If your fallback is GPT-5.5 at $5/M tokens, you've moved the bill, not deleted it. Two patterns work well: (1) route the fallback to a smaller hosted model (Haiku, Gemini Flash, Mistral Small) that matches Nano's "short summarization" sweet spot; (2) for Mac users specifically, run Rapid-MLX as your /api/llm endpoint — Apple Silicon owners get on-device performance via your server's Mac, not theirs. Same thesis as our DeepClaude guide: the harness is one product, the model is another, and you can swap them.
-
Anthropic is allowing the Claude CLI to run OpenClaw again
> Large-context requests auto-route to a cloud LLM (GPT-5, Claude, etc.) when local prefill would be slow. Routing based on new tokens after cache hit. --cloud-model openai/gpt-5 --cloud-threshold 20000
https://github.com/raullenchai/Rapid-MLX
- Show HN: Rapid-MLX – Run local LLMs on Mac, 2-3x faster than alternatives
-
Gemma 4 on Apple Silicon: 85 tok/s with a pip install
I've verified this end-to-end with structured output (output_type=BaseModel), streaming, multi-turn conversations, and multi-tool workflows. Test suite here.
-
vLLM-mlx – 65 tok/s LLM inference on Mac with tool calling and prompt caching
pip install git+https://github.com/raullenchai/vllm-mlx.git
PaddlePaddle
Posts with mentions or reviews of PaddlePaddle.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2025-02-16.
-
GPT 4.5 level for 1% of the price
PaddlePaddle (so good they named it twice) predates Ray and supports both data parallel and model-parallel training. It is still being developed.
https://github.com/PaddlePaddle/Paddle
They have pedigry.
-
Ask HN: What is the best method for turning a scanned book as a PDF into text?
I have tried a bunch of things. This is what worked best for me: Surya [0]. It can run fully local on your laptop. I also tried EasyOCR [1], which is also quite good. I haven't tried this myself, but I will look at Paddle [2] if the previous two don't float your boat.
All of these are OSS, and you don't need to pay a dime to anyone.
[0]: https://github.com/VikParuchuri/surya
[1]: https://github.com/JaidedAI/EasyOCR
[2]: https://github.com/PaddlePaddle/Paddle
-
Fixing bugs in your AI: let's analyze bugs in OpenVINO
It's hard to define what exactly the correct code should look like in this case. However, let's take a guess. The code is in the OpenVINO Paddle Frontend module, which parses the model generated by the PaddlePaddle framework. If we search for the 'pad3d' name in the project, we can find the following description:
-
List of AI-Models
Click to Learn more...
-
Ask HN: Are there any notable Chinese FLOSS projects?
PaddlePaddle?
https://github.com/PaddlePaddle/Paddle
Also, Baidu have quite a few OSS projects out there in general.
https://github.com/baidu
-
Volcano vs Yunikorn vs Knative
Volcano is a batch scheduler on top of Kube-batch targetting spark-operator, plain old MPI, chinesium paddlepaddle, and Kromwell HPC.
-
Baidu AI Researchers Introduce SE-MoE That Proposes Elastic MoE Training With 2D Prefetch And Fusion Communication Over Hierarchical Storage
Continue reading | Check out the paper, and Github
- I have issue with only __habs for half datatype? Please help!
- Alternatives to google collab?
What are some alternatives?
When comparing Rapid-MLX and PaddlePaddle you can also consider the following projects:
Sacred - Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
tensorflow - An Open Source Machine Learning Framework for Everyone
MindsDB - General-purpose AI designed for knowledge workers — creators, strategists, and operators — and individuals seeking AI systems they can truly control to help them get work done, with full flexibility to extend and deploy anywhere (VPC, on-prem, or cloud).
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
gym - A toolkit for developing and comparing reinforcement learning algorithms.
python-recsys - A python library for implementing a recommender system