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Awni Hannun
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<section id="mlx">
<h1>MLX<a class="headerlink" href="#mlx" title="Permalink to this heading">#</a></h1>
<p>MLX is a NumPy-like array framework designed for efficient and flexible
machine learning on Apple silicon.</p>
<p>MLX is a NumPy-like array framework designed for efficient and flexible machine
learning on Apple silicon, brought to you by Apple machine learning research.</p>
<p>The Python API closely follows NumPy with a few exceptions. MLX also has a
fully featured C++ API which closely follows the Python API.</p>
<p>The main differences between MLX and NumPy are:</p>
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GPU, …)</p></li>
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</div></blockquote>
<p>The design of MLX is strongly inspired by frameworks like <a class="reference external" href="https://pytorch.org/">PyTorch</a>, <a class="reference external" href="https://github.com/google/jax">Jax</a>, and
<p>The design of MLX is inspired by frameworks like <a class="reference external" href="https://pytorch.org/">PyTorch</a>, <a class="reference external" href="https://github.com/google/jax">Jax</a>, and
<a class="reference external" href="https://arrayfire.org/">ArrayFire</a>. A noteable difference from these
frameworks and MLX is the <em>unified memory model</em>. Arrays in MLX live in shared
memory. Operations on MLX arrays can be performed on any of the supported