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Signed-off-by: Josh Soref <2119212+jsoref@users.noreply.github.com>
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Josh Soref 2024-01-01 22:38:20 -05:00
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@ -19,7 +19,7 @@ The main differences between MLX and NumPy are:
The design of MLX is inspired by frameworks like `PyTorch
<https://pytorch.org/>`_, `Jax <https://github.com/google/jax>`_, and
`ArrayFire <https://arrayfire.org/>`_. A noteable difference from these
`ArrayFire <https://arrayfire.org/>`_. A notable difference from these
frameworks and MLX is the *unified memory model*. Arrays in MLX live in shared
memory. Operations on MLX arrays can be performed on any of the supported
device types without performing data copies. Currently supported device types