The expressiveness of Python,  with the performance of C

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Mojo Developers
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Community members

Mojo has powerful & easy to use features

Leverage types for better performance and error checking.

awesome.🔥

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])

struct MyPair:
  var first: Int
  var second: F32
  
  def __init__(self, first: Int, second: F32):
    self.first = first
    self.second = second

def reorder_and_process(owned x: HugeArray):
  sort(x)	# Update in place
  
  give_away(x^)	# Transfer ownership
  
  print(x[0])	# Error: ‘x’ moved away!

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)

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)

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])

struct MyPair:
  var first: Int
  var second: F32
  
  def __init__(self, first: Int, second: F32):
    self.first = first
    self.second = second

def reorder_and_process(owned x: HugeArray):
  sort(x)	# Update in place
  
  give_away(x^)	# Transfer ownership
  
  print(x[0])	# Error: ‘x’ moved away!

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)

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)

Progressive types
Zero cost abstractions
Ownership + borrow checker
Portable parametric algorithms
Language integrated auto-tuning

Mojo fEATURES:

Progressive types

Zero cost abstractions

Ownership + borrow checker

Portable parametric algorithms

Language integrated auto-tuning

Extend Python or scale all the way down to the metal. Program the multitude of low-level AI hardware. No C++ or CUDA required.

Using Mojo with MAX unlocks the future of accelerated compute for the world

Development tools for accelerated compute on GPUs and CPUs, built from the ground up

What is MAX?

Get started with Mojo🔥 and MAX

Mojo🔥 + MAX unlock incredible NVIDIA GPU performance

Mojo with MAX enables state of the art latency and throughput without writing low-level CUDA code.

Get early GPU access

Deploy MAX on GPUs

Get early NVIDIA GPU access

See what AI developers are building with Mojo🔥

LLM.mojo

A implementation of Andrej Karpathy's llm.c to Mojo 🔥

View Github

Basalt

A machine learning framework in pure Mojo 🔥

View Github

Lightbug HTTP

A HTTP web framework written in pure Mojo 🔥

View Github

Endia

A dynamic Array library for Scientific Computing🔥

View Github

+300

more open source projects

Developers love Mojo 🔥

“I'm excited, you're excited, everyone is excited to see what's new in Mojo and MAX and the amazing achievements of the team at Modular.”

Eprahim

“Max installation on Mac M2 and running llama3 in (q6_k and q4_k) was a breeze! Thank you Modular team!”

“Mojo destroys Python in speed. 12x faster without even trying. The future is bright!”

svpino

“Tired of the two language problem. I have one foot in the ML world and one foot in the geospatial world, and both struggle with the "two-language" problem. Having Mojo - as one language all the way through is be awesome.”

fnands

"C is known for being as fast as assembly, but when we implemented the same logic on Mojo and used some of the out-of-the-box features, it showed a huge increase in performance... It was amazing."

Aydyn

“It’s fast which is awesome. And it’s easy. It’s not CUDA programming...easy to optimize.”

dorjeduck

“I'm excited, you're excited, everyone is excited to see what's new in Mojo and MAX and the amazing achievements of the team at Modular.”

Eprahim

“Max installation on Mac M2 and running llama3 in (q6_k and q4_k) was a breeze! Thank you Modular team!”

“Mojo destroys Python in speed. 12x faster without even trying. The future is bright!”

svpino

fnands

"C is known for being as fast as assembly, but when we implemented the same logic on Mojo and used some of the out-of-the-box features, it showed a huge increase in performance... It was amazing."

Aydyn

“It’s fast which is awesome. And it’s easy. It’s not CUDA programming...easy to optimize.”

dorjeduck

“I'm excited, you're excited, everyone is excited to see what's new in Mojo and MAX and the amazing achievements of the team at Modular.”

Eprahim

“Max installation on Mac M2 and running llama3 in (q6_k and q4_k) was a breeze! Thank you Modular team!”

“Mojo destroys Python in speed. 12x faster without even trying. The future is bright!”

svpino

fnands

"C is known for being as fast as assembly, but when we implemented the same logic on Mojo and used some of the out-of-the-box features, it showed a huge increase in performance... It was amazing."

Aydyn

“It’s fast which is awesome. And it’s easy. It’s not CUDA programming...easy to optimize.”

dorjeduck

“I'm excited, you're excited, everyone is excited to see what's new in Mojo and MAX and the amazing achievements of the team at Modular.”

Eprahim

“Max installation on Mac M2 and running llama3 in (q6_k and q4_k) was a breeze! Thank you Modular team!”

“Mojo destroys Python in speed. 12x faster without even trying. The future is bright!”

svpino

fnands

"C is known for being as fast as assembly, but when we implemented the same logic on Mojo and used some of the out-of-the-box features, it showed a huge increase in performance... It was amazing."

Aydyn

“It’s fast which is awesome. And it’s easy. It’s not CUDA programming...easy to optimize.”

dorjeduck

Mojo destroys Python in speed. 12x faster without even trying. The future is bright!

mytechnotalent

“The Community is incredible and so supportive. It’s awesome to be part of.”

benny.n

“A few weeks ago, I started learning Mojo 🔥 and MAX. Mojo has the potential to take over AI development. It's Python++. Simple to learn, and extremely fast.”

svpino

“What @modular is doing with Mojo and the MaxPlatform is a completely different ballgame.”

scrumtuous

"Mojo gives me the feeling of superpowers. I did not expect it to outperform a well-known solution like llama.cpp."

Aydyn

“Mojo and the MAX Graph API are the surest bet for longterm multi-arch future-substrate NN compilation”

“I am focusing my time to help advance @Modular. I may be starting from scratch but I feel it’s what I need to do to contribute to #AI for the next generation.”

mytechnotalent

“What @modular is doing with Mojo and the MaxPlatform is a completely different ballgame.”

scrumtuous

“Mojo and the MAX Graph API are the surest bet for longterm multi-arch future-substrate NN compilation”

pagilgukey

“I'm very excited to see this coming together and what it represents, not just for MAX, but my hope for what it could also mean for the broader ecosystem that mojo could interact with.”

strangemonad

“I tried MAX builds last night, impressive indeed. I couldn't believe what I was seeing... performance is insane.”

drdude81

“The more I benchmark, the more impressed I am with the MAX Engine.”

justin_76273

“I am focusing my time to help advance @Modular. I may be starting from scratch but I feel it’s what I need to do to contribute to #AI for the next generation.”

mytechnotalent

“What @modular is doing with Mojo and the MaxPlatform is a completely different ballgame.”

scrumtuous

“Mojo and the MAX Graph API are the surest bet for longterm multi-arch future-substrate NN compilation”

pagilgukey

“I'm very excited to see this coming together and what it represents, not just for MAX, but my hope for what it could also mean for the broader ecosystem that mojo could interact with.”

strangemonad

“I tried MAX builds last night, impressive indeed. I couldn't believe what I was seeing... performance is insane.”

drdude81

“The more I benchmark, the more impressed I am with the MAX Engine.”

justin_76273

“I am focusing my time to help advance @Modular. I may be starting from scratch but I feel it’s what I need to do to contribute to #AI for the next generation.”

mytechnotalent

“What @modular is doing with Mojo and the MaxPlatform is a completely different ballgame.”

scrumtuous

“Mojo and the MAX Graph API are the surest bet for longterm multi-arch future-substrate NN compilation”

pagilgukey

“I'm very excited to see this coming together and what it represents, not just for MAX, but my hope for what it could also mean for the broader ecosystem that mojo could interact with.”

strangemonad

“I tried MAX builds last night, impressive indeed. I couldn't believe what I was seeing... performance is insane.”

drdude81

“The more I benchmark, the more impressed I am with the MAX Engine.”

justin_76273

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🔥

Python
Single Thread Distribution
Mojo🔥
Parallel processing across multiple cores

Mojo enables systems programming features so you can process information exponentially faster.

Languages	Time (s)*	Speedup Vs Python
Python 3.10.9	1027s	1X
PYPY	46.1s	22x
Scalar C++	0.20s	5,000x
Mojo🔥	0.03s	68,000x

* Algorithm

Mandelbrot

Instance

AWS C1.xlarge

Intel Xeon

Read our 68,000x blog post

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

Mojo 🔥

Python

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())

Upgrade your models to full pipelines with MAX

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.

Read the programming manual

Input layer

Hidden layers

Output layer