Mojo – The Programming Language for AI That Is Up To 35000x Faster Than Python. Although Python’s simplicity and versatility made it the language of choice in fields such as data science, machine learning, and AI. It has tons of packages that are very useful for anyone working with data, but for libraries that require great performance.
Python only acts as a glue layer and low-level bindings to C, C++, and other languages with better performance. But the overview of Mojo’s official docs says that –
Our high-performance serving library provides an OpenAI-compatible REST endpoint out of the box, so you can easily migrate from OpenAI services or other libraries like vLLM and SGLang with minimal code changes. We handle the complete request lifecycle with built-in support for function calling that enables agentic workflows, structured output for predictable JSON responses, and advanced performance optimizations like prefix caching and speculative decoding.
For scenarios where you don’t need a REST endpoint, you can also use our Python API for offline inference to run models directly in your applications for batch processing or embedded use cases.
This enabled the development of libraries such as NumPy and TensorFlow. However, this comes with a drawback: building these libraries is very complicated, it requires a low-level understanding of the internals of CPython, requires knowledge of C/C++, etc.
According to the Mojo doc, the issues brought by Python go deeper and particularly impact the AI field.
Python alone isn’t able to address all the issues that applied AI systems need and that’s how Mojo was born.
Read More: What is Competitive Programming? Why Competitive Programming?
ডাটা স্ট্রাকচার ও এলগরিদম এর বেসিক টু এডভান্স গাইডলাইন।
Mojo is a programming language that combines the usability of Python with the performance of C.
1. It is designed as a superset of Python
2. Strong type checking
3. Memory ownership and borrowing checker
4. Auto-tuning
5. It is leverages MLIR (Multi-Level Intermediate Representation)
Using AI coding assistants
You can use large language models (LLMs) to accelerate your development with Modular by providing structured context about Modular Platform’s docs and code to your projects. We provide two mechanisms:
llms.txtfiles for broad documentation access..cursorulesfiles for specific coding guidelines.
Supply documentation to LLMs with llms.txt
Modular supports the llms.txt proposed standard, enabling LLMs to access our documentation at inference time. This allows LLMs the most up-to-date documentation providing more accurate and context-aware responses.
Modular provides the following llms.txt files:
- llms.txt: Contains an index of links with brief content descriptions for LLMs to navigate to detailed information.
- llms-full.txt: Provides all detailed content in a single file, removing the need for navigation.
- llms-mojo.txt: Includes documentation for the Mojo standard library, MAX AI Kernels, and MAX library.
- llms-python.txt: Contains MAX Python APIs documentation.
What are the system requirements?
For Mac –
- macOS Ventura (13) or later
- Apple silicon (M1/M2/M3/M4 processor)
- Python 3.9 – 3.13
- Xcode or Xcode Command Line Tools
- We currently don’t support Mac GPUs
For Linux –
- Ubuntu 22.04 LTS
- x86-64 CPU (with SSE4.2 or newer) or AWS Graviton2/3 CPU
- Minimum 8 GiB RAM (or much more, depending on the model you run)
- Python 3.9 – 3.13
- g++ or clang++ C++ compiler
- To use GPUs, see the GPU requirements
For Windows –
Windows is not officially supported at this time.
In the meantime, you can try MAX on Windows with WSL, using a compatible version of Ubuntu (see our requirements for Linux).
When a new programming language launch in the market, developer and user are eagerly waiting to see and dive into that programming language. If you want to deep dive into this new programming language, here are the official documentations for you. Kindly visit their official website from here –
Mojo Programming Language Docs: [https://docs.modular.com/mojo/](https://docs.modular.com/mojo/)
