diff --git a/doc/USING.rst b/doc/USING.rst
index db3013d8..2d874ba1 100644
--- a/doc/USING.rst
+++ b/doc/USING.rst
@@ -40,6 +40,20 @@ If you want to use the pre-trained English model for performing speech-to-text,
    wget https://github.com/mozilla/DeepSpeech/releases/download/v0.7.4/deepspeech-0.7.4-models.pbmm
    wget https://github.com/mozilla/DeepSpeech/releases/download/v0.7.4/deepspeech-0.7.4-models.scorer
 
+There are several pre-trained model files available in official releases. Files ending in ``.pbmm`` are compatible with clients and language bindings built against the standard TensorFlow runtime. Usually these packages are simply called ``deepspeech``. These files are also compatible with CUDA enabled clients and language bindings. These packages are usually called ``deepspeech-gpu``. Files ending in ``.tflite`` are compatible with clients and language bindings built against the `TensorFlow Lite runtime <https://www.tensorflow.org/lite/>`_. These models are optimized for size and performance in low power devices. On desktop platforms, the compatible packages are called ``deepspeech-tflite``. On Android and Raspberry Pi, we only publish TensorFlow Lite enabled packages, and they are simply called ``deepspeech``. You can see a full list of supported platforms and which TensorFlow runtime is supported at :ref:`supported-platforms-inference`.
+
++--------------------+---------------------+---------------------+
+| Package/Model type | .pbmm               | .tflite             |
++====================+=====================+=====================+
+| deepspeech         | Depends on platform | Depends on platform |
++--------------------+---------------------+---------------------+
+| deepspeech-gpu     | ✅                  | ❌                  |
++--------------------+---------------------+---------------------+
+| deepspeech-tflite  | ❌                  | ✅                  |
++--------------------+---------------------+---------------------+
+
+Finally, the pre-trained model files also include files ending in ``.scorer``. These are external scorers (language models) that are used at inference time in conjunction with an acoustic model (``.pbmm`` or ``.tflite`` file) to produce transcriptions. We also provide further documentation on :ref:`the decoding process <decoder-docs>` and :ref:`how language models are generated <scorer-scripts>`.
+
 Model compatibility
 ^^^^^^^^^^^^^^^^^^^