fix docs performance section (pytorch#1409)

Co-authored-by: Andrew Ho <[email protected]>

docs/source/what_is_torchdata_nodes.rst

@@ -55,7 +55,7 @@ hoops with a special sampler.
 
 ``torchdata.nodes`` follows a streaming data model, where operators are
 Iterators that can be combined together to define a dataloading and
-pre-proc pipeline. Samplers are still supported (see example above) and
+pre-proc pipeline. Samplers are still supported (see :ref:`migrate-to-nodes-from-utils`) and
 can be combined with a Mapper to produce an Iterator
 
 Multi-Datasets do not fit well with the current implementation in ``torch.utils.data``
@@ -102,12 +102,14 @@ where we showed that:
 
 * With GIL python, torchdata.nodes with multi-threading performs better than
   multi-processing in some scenarios, but makes features like GPU pre-proc
-  easier to perform which can boost
-
-We ran a benchmark loading the Imagenet dataset from disk,
-and manage to saturate main-memory bandwidth with Free-Threaded Python (3.13t)
-at a significantly lower CPU utilization than with multi-process workers
-(blogpost expected eary 2025). See ``examples/nodes/imagenet_benchmark.py``.
+  easier to perform, which can boost throughput for many use cases.
+
+* With No-GIL / Free-Threaded python (3.13t), we ran a benchmark loading the
+  Imagenet dataset from disk, and manage to saturate main-memory bandwidth
+  at a significantly lower CPU utilization than with multi-process workers
+  (blogpost expected eary 2025).  See
+  `imagenet_benchmark.py <https://github.com/pytorch/data/blob/main/examples/nodes/imagenet_benchmark.py>`_
+  to try on your own hardware.
 
 
 Design choices