diff --git a/deepspeed/runtime/engine.py b/deepspeed/runtime/engine.py
index 6d8040fe8ec2..5081adb0b021 100755
--- a/deepspeed/runtime/engine.py
+++ b/deepspeed/runtime/engine.py
@@ -1715,6 +1715,8 @@ def _zero3_consolidated_fp16_state_dict(self):
 
         Get a full non-partitioned state_dict with fp16 weights on cpu.
 
+        Important: this function must be called on all ranks and not just rank 0.
+
         This is similar to nn.Module.state_dict (modelled after _save_to_state_dict), but:
 
         1. consolidates the weights from different partitions on gpu0
diff --git a/docs/_pages/config-json.md b/docs/_pages/config-json.md
index 9a9554cbd75f..81c7d5bd62b9 100755
--- a/docs/_pages/config-json.md
+++ b/docs/_pages/config-json.md
@@ -256,7 +256,8 @@ Enabling and configuring ZeRO memory optimizations
     "stage3_prefetch_bucket_size" : 5e8,
     "stage3_param_persistence_threshold" : 1e6,
     "sub_group_size" : 1e12,
-    "elastic_checkpoint" : [true|false]
+    "elastic_checkpoint" : [true|false],
+    "stage3_gather_fp16_weights_on_model_save": [true|false]
     }
 ```
 
@@ -351,6 +352,11 @@ Enabling and configuring ZeRO memory optimizations
 | Do not partition parameters smaller than this threshold. Smaller values use less memory, but can greatly increase communication (especially latency-bound messages). | `1e6`   |
 
 
+***stage3_gather_fp16_weights_on_model_save***: [boolean]
+| Description                                                                                                                                                          | Default |
+| -------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------- |
+| Consolidate the weights before saving the model by `save_fp16_model()`. Since the weights are partitioned across GPUs, they aren't part of `state_dict`, so this function automatically gather the weights when this option is enabled and then saves the fp16 model weights. | `False` |
+
 ### Logging
 
 ***steps\_per\_print***: [integer]
diff --git a/docs/_tutorials/advanced-install.md b/docs/_tutorials/advanced-install.md
index 18f60e864039..4ff02bf7ec84 100644
--- a/docs/_tutorials/advanced-install.md
+++ b/docs/_tutorials/advanced-install.md
@@ -73,6 +73,18 @@ DS_BUILD_OPS=1 pip install deepspeed --global-option="build_ext" --global-option
 
 This should complete the full build 2-3 times faster. You can adjust `-j` to specify how many cpu-cores are to be used during the build. In the example it is set to 8 cores.
 
+You can also build a binary wheel and install it on multiple machines that have the same type of GPUs and the same software environment (CUDA toolkit, pytorch, python, etc.)
+
+```bash
+DS_BUILD_OPS=1 python setup.py build_ext -j8 bdist_wheel
+```
+
+This will create a pypi binary wheel under `dist`, e.g., ``dist/deepspeed-0.3.13+8cd046f-cp38-cp38-linux_x86_64.whl`` and then you can install it directly on multiple machines, in our example:
+
+```bash
+pip install dist/deepspeed-0.3.13+8cd046f-cp38-cp38-linux_x86_64.whl
+```
+
 
 ## Install DeepSpeed from source
 
diff --git a/docs/_tutorials/zero.md b/docs/_tutorials/zero.md
index 1e9f97b98a11..8f506d25babe 100644
--- a/docs/_tutorials/zero.md
+++ b/docs/_tutorials/zero.md
@@ -260,5 +260,43 @@ for more details.
         self.init_method(self.position_embeddings.weight)
     ```
 
+## Extracting weights
+
+If you need to take the pretrained weights out of Deepspeed here is what you can do for getting fp16 weights:
+
+- under ZeRO-2 `state_dict` contains the fp16 model weights and these can be saved normally with `torch.save`.
+- under ZeRO-3 `state_dict` contains just the placeholders since the model weights are partitioned across multiple GPUs. If you want to get to these weights enable:
+
+```
+    "zero_optimization": {
+        "stage3_gather_fp16_weights_on_model_save": true
+    },
+```
+And then save the model using:
+
+```
+            if self.deepspeed:
+                self.deepspeed.save_fp16_model(output_dir, output_file)
+```
+
+Because it requires consolidation of the weights on one GPU it can be slow and memory demanding, so only use this feature when needed.
+
+Note that if `stage3_gather_fp16_weights_on_model_save` is `False`, no weights will be saved (again, because `state_dict` doesn't have them.
+You can use this method to save ZeRO-2 weights as well.
+
+If you'd like to get the fp32 weights, we supply a special script that can do offline consolidation. It requires no configuration files or GPUs. Here is an example of its usage:
+
+```
+$ cd /path/to/checkpoints_dir
+$ ./zero_to_fp32.py global_step1 pytorch_model.bin
+Processing zero checkpoint at global_step1
+Detected checkpoint of type zero stage 3, world_size: 2
+Saving fp32 state dict to pytorch_model.bin (total_numel=60506624)
+```
+
+The `zero_to_fp32.py` gets created automatically when you save a checkpoint.
+
+Note: currently this script uses 2x memory (general RAM) of the size of the final checkpoint.
+
 
 Congratulations! You have completed the ZeRO tutorial.
diff --git a/docs/code-docs/source/training.rst b/docs/code-docs/source/training.rst
index d88d755f39cb..52e124fc3b40 100644
--- a/docs/code-docs/source/training.rst
+++ b/docs/code-docs/source/training.rst
@@ -31,3 +31,11 @@ Optimizer Step
 Gradient Accumulation
 ---------------------
 .. autofunction:: deepspeed.DeepSpeedEngine.is_gradient_accumulation_boundary
+
+
+Model Saving
+------------
+.. autofunction:: deepspeed.DeepSpeedEngine.save_fp16_model
+
+
+Additionally when a DeepSpeed checkpoint is created, a script ``zero_to_fp32.py`` is added there which can be used to reconstruct fp32 master weights into a single pytorch ``state_dict`` file.