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MAX Platform
Mojo
MOJO 🔥 — THE PROGRAMMING LANGUAGE FOR ALL AI DEVELOPERS
Mojo combines the usability of Python with the performance of C, unlocking
unparalleled programmability of AI hardware and extensibility of AI models.
Available on Mac 🍎, Linux, and Windows (WSL)
Install Mojo🔥
Read the docs
JOIN THE MODVERSE
175K+ Developers
50K+ Enterprises
22K+ Members
17K+ stars
9K+ subscribers
Over 300 open-source projects created by the community
LLAMA2.MOJO
Inference forLlama2 models in a single file of Mojo 🔥
Github
VOODOO
A machine learning framework in pure Mojo 🔥
Github
LIGHTBUG HTTP
Simple and fast HTTP framework for Mojo 🔥
Github
More community awesomeness
MOJO OUT-PERFORMS
01
Usability & Programmability
WRITE EVERYTHING IN ONE LANGUAGE
Write Python or scale all the way down to the metal. Program the multitude of
low-level AI hardware. No C++ or CUDA required.
Take a tour of Mojo
FILE_NAME.🔥
def sort(v: ArraySlice[Int]):
for i in range(len(v)):
for j in range(len(v) - i - 1):
if v[j] > v[j + 1]:
swap(v[j], v[j + 1])
FILE_NAME.🔥
struct MyPair:
var first: Int
var second: F32
def __init__(self, first: Int, second: F32):
self.first = first
self.second = second
FILE_NAME.🔥
def reorder_and_process(owned x: HugeArray):
sort(x) # Update in place
give_away(x^) # Transfer ownership
print(x[0]) # Error: ‘x’ moved away!
FILE_NAME.🔥
def exp[dt: DType, elts: Int]
(x: SIMD[dt, elts]) -> SIMD[dt, elts]:
x = clamp(x, -88.3762626647, 88.37626266)
k = floor(x * INV_LN2 + 0.5)
r = k * NEG_LN2 + x
return ldexp(_exp_taylor(r), k)
FILE_NAME.🔥
def exp_buffer[dt: DType](data: ArraySlice[dt]):
# Search for the best vector length
alias vector_len = autotune(1, 4, 8, 16, 32)
# Use it as the vectorization length
vectorize[exp[dt, vector_len]](data)
Features include:
PROGRESSIVE TYPES
Leverage types for better performance and error checking.
ZERO COST ABSTRACTIONS
Take control of storage by inline-allocating values into structures.
OWNERSHIP + BORROW CHECKER
Take advantage of memory safety without the rough edges.
PORTABLE PARAMETRIC ALGORITHMS
Leverage compile-time meta-programming to write hardware-agnostic algorithms and
reduce boilerplate.
LANGUAGE INTEGRATED AUTO-TUNING
Automatically find the best values for your parameters to take advantage of
target hardware.
As well as:
The full power of MLIR
Parallel heterogenous runtime
Fast compile times
FILE_NAME.🔥
def sort(v: ArraySlice[Int]):
for i in range(len(v)):
for j in range(len(v) - i - 1):
if v[j] > v[j + 1]:
swap(v[j], v[j + 1])
Features include:
PROGRESSIVE TYPES
Leverage types for better performance and error checking.
FILE_NAME.🔥
struct MyPair:
var first: Int
var second: F32
def __init__(self, first: Int, second: F32):
self.first = first
self.second = second
Features include:
ZERO COST ABSTRACTIONS
Take control of storage by inline-allocating values into structures.
FILE_NAME.🔥
def reorder_and_process(owned x: HugeArray):
sort(x) # Update in place
give_away(x^) # Transfer ownership
print(x[0]) # Error: ‘x’ moved away!
Features include:
OWNERSHIP + BORROW CHECKER
Take advantage of memory safety without the rough edges.
FILE_NAME.🔥
def exp[dt: DType, elts: Int]
(x: SIMD[dt, elts]) -> SIMD[dt, elts]:
x = clamp(x, -88.3762626647, 88.37626266)
k = floor(x * INV_LN2 + 0.5)
r = k * NEG_LN2 + x
return ldexp(_exp_taylor(r), k)
Features include:
PORTABLE PARAMETRIC ALGORITHMS
Leverage compile-time meta-programming to write hardware-agnostic algorithms and
reduce boilerplate.
FILE_NAME.🔥
def exp_buffer[dt: DType](data: ArraySlice[dt]):
# Search for the best vector length
alias vector_len = autotune(1, 4, 8, 16, 32)
# Use it as the vectorization length
vectorize[exp[dt, vector_len]](data)
Features include:
LANGUAGE INTEGRATED AUTO-TUNING
Automatically find the best values for your parameters to take advantage of
target hardware.
02
Performance
UNLOCK PYTHON PERFORMANCE
Utilize the full power of the hardware, including multiple cores, vector units,
and exotic accelerator units, with the world's most advanced compiler and
heterogenous runtime. Achieve performance on par with C++ and CUDA without the
complexity.
Play with Mojo
PARALLELIZATION
Mojo leverages MLIR, which enables Mojo developers to take advantage of vectors,
threads, and AI hardware units.
PYTHON
Single-threaded execution
MOJO 🔥
Parallel processing across multiple cores
Languages
Time (S) *
Speedup vs Python
Python 3.10.9
970 s
1x
Numpy
171 s
6x
Scalar C++
0.11 s
9000x
Mojo 🔥
0.0142 s
68000x
* Algorithm
Mandelbrot
Instance
AWS h3-standard-88
Intel Xeon
MoreRead the 68,000x Blog
03
Interoperability
ACCESS THE ENTIRE PYTHON ECOSYSTEM
Experience true interoperability with the Python ecosystem. Seamlessly intermix
arbitrary libraries like Numpy and Matplotlib and your custom code with Mojo.
Read the programming manual
MAKE_PLOT.🔥
def make_plot(m: Matrix):
plt = Python.import_module("matplotlib.pyplot")
fig = plt.figure(1, [10, 10 * yn // xn], 64)
ax = fig.add_axes([0.0, 0.0, 1.0, 1.0], False, 1)
plt.imshow(image)
plt.show()
make_plot(compute_mandelbrot())
Mojo 🔥
04
Extensibility
UPGRADE YOUR MODELS AND THE MODULAR STACK
Easily extend your models with pre and post-processing operations, or replace
operations with custom ones. Take advantage of kernel fusion, graph rewrites,
shape functions, and more.
Model extensibility
Mojo can upgrade the existing operations in your model.
Input layer
Hidden layers
Output layer
max platform
MOJO 🔥 WORKS WITH
ALL THE REST OF THE SUITE
Modular MAX Engine can be used in combination with our integrations via MAX
Serving services and it is powered by Mojo 🔥 the fastest and most portable
programming language for your AI applications.
Our engine integrates with the rest of our suite of MAX products, while being
usable on its own.
Explore all the MAX Platform
05
GET STARTED NOW
DOWNLOAD MOJO 🔥 AND CONTRIBUTE TO IT NOW
Mojo is still a work in progress, but it's available to try today via our Mojo
SDK. Run through tutorials and write your own Mojo code.
Download now
Mojo 🔥
01.
EASY TO GET STARTED
Get the Mojo 🔥 SDK today and get started with our example code on GitHub.
02.
Unleash your mojo
Our docs will help you quickly discover why Mojo is such a powerful extension to
Python, and the future of AI programming.
03.
JOIN the mojo COMMUNITY
Come and chat with us on our Discord, and help shape the future of the language
as we continue to develop it.
READY TO PLAY WITH MOJO?
Sign up & download the Mojo SDK right now.
Get started
API Reference, Tutorials, & More
Read the Mojo docs
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