diff --git a/xbatcher/prefetch_generators.py b/xbatcher/prefetch_generators.py
new file mode 100644
index 0000000..8bc46ef
--- /dev/null
+++ b/xbatcher/prefetch_generators.py
@@ -0,0 +1,158 @@
+import time
+from typing import List, Union, Tuple
+
+import numpy as np
+import dask
+from distributed import get_client, Future, as_completed, Client
+import dask.array as da
+import xarray as xr
+
+
+class BatchIDX:
+    """Deterministic batch idx with [] syntax dropping remainder."""
+
+    def __init__(self, array, batch_size: int):
+        self.batch_size = batch_size
+        self.array = array
+
+    def __getitem__(self, i: int):
+        start = i * self.batch_size
+        end = start + self.batch_size
+        return slice(start, end)
+
+    def __len__(self):
+        return int(np.floor(len(self.array) / self.batch_size))
+
+
+class PrefetchBatchGenerator:
+    """
+    Using deterministic index generation over arrays that are chunked only over
+    the first dimension, submit tasks to workers to load a batch per task and
+    accumulate prefetch number of these futures. Block until the current buffer
+    of futures is available (calling gather on a batch of batches), while a
+    separate prefetch buffer of tasks has already been submitted.
+
+    The idea is to submit tasks to dask in order to start loading that batch
+    data into memory while actively transfering ready batches to the process
+    where this class has been instantiated.
+
+    Considerations:
+    - does getting results as they complete slows things down compared to gather?
+        - use .batches()! Still, is this the right approach?
+    - Can this be pickled (if necessary) to be used in tf.data.Dataset.from_generator()?
+        - does it have to be pickled? Can I set num_processes to 0 or 1 to avoid the
+        need to pickle it if it does cause issues?
+    - Does get_client slow things down?
+    - How to set prefetch?
+    - should we warn that 2xprefetch amount of data is too large for relevant workers?
+    - should we make it a high priority to support heterogeneous worker types?
+        - we might want to isolate worker types between data and modeling e.g.
+        use gpu for modeling, but cpu workers for prefetching.
+    """
+
+    def __init__(self, array: da.Array, batch_size: int, prefetch: int):
+        if not array.chunksize[1:] == array.shape[1:]:
+            raise ValueError(
+                "we require chunking only over the first, single, major, batch axis"
+            )
+        self.array = array
+        self.batch_size = batch_size
+        self._prefetch = prefetch
+        # use register_worker_plugin here instead
+        self._gen_idx = BatchIDX(array=array, batch_size=batch_size)
+        self.total_batches = len(self._gen_idx)
+        self._future_buffer: List[Future] = []
+        self._prefetch_buffer: List[Future] = []
+        self._init_prefetch = True
+        self._idx_to_be_submitted = 0
+
+    @property
+    def prefetch_range(self) -> Tuple[int, int]:
+        """Return the number of elements to submit."""
+        remaining = self.total_batches - self._idx_to_be_submitted
+
+        prefetch_count = min(remaining, self._prefetch)
+        return self._idx_to_be_submitted, self._idx_to_be_submitted + prefetch_count
+
+    def _submit_prefetch_batches(self):
+        """..."""
+        client = get_client()
+        if self._init_prefetch:
+            for i in range(*self.prefetch_range):
+                idx_slice = self._gen_idx[i]
+                future = client.submit(
+                    _make_batch, array=self.array, idx=idx_slice, pure=False
+                )
+                self._future_buffer.append(future)
+                self._idx_to_be_submitted = i + 1
+            self._init_prefetch = False
+
+        for i in range(*self.prefetch_range):
+            idx_slice = self._gen_idx[i]
+            future = client.submit(
+                _make_batch, array=self.array, idx=idx_slice, pure=False
+            )
+            self._prefetch_buffer.append(future)
+            self._idx_to_be_submitted = i + 1
+
+    def __len__(self):
+        return self.total_batches
+
+    def __iter__(self):
+        """..."""
+        client = get_client()
+        n_submissions = max(int(np.ceil(self.total_batches / self._prefetch)), 1)
+        for _ in range(n_submissions):
+            self._submit_prefetch_batches()
+            for batch_of_batches_futures in as_completed(self._future_buffer).batches():
+                batch_of_batches = client.gather(batch_of_batches_futures)
+                for batch in batch_of_batches:
+                    yield batch
+            self._future_buffer = self._prefetch_buffer
+            self._prefetch_buffer = []
+
+
+def _make_batch(
+    array: Union[da.Array, xr.DataArray], idx: slice, scheduler: str = "single-threaded"
+) -> np.ndarray:
+    """..."""
+
+    with dask.config.set(scheduler=scheduler):
+        batch = np.asarray(array[idx])
+    return batch
+
+
+if __name__ == "__main__":
+
+    import os
+
+    # set env or edit to suit your needs!
+    address = os.getenv("DASK_ADDRESS")
+    remote_path = os.getenv("REMOTE_PATH")
+    client = Client(address)
+
+    # write out an example zarr dataset
+    target_array = da.random.random((50000, 9, 100, 100), chunks=[1024, 9, 100, 100])
+    target_array.to_zarr(remote_path, overwrite=True)
+
+    def do_ml():
+        # load array for your data problem
+        array = da.from_zarr(remote_path)
+        batch_gen = PrefetchBatchGenerator(
+            array=array[:50000], batch_size=1024, prefetch=25
+        )
+        nbytes = 0
+        shapes = []
+        t0 = time.time()
+        for b in batch_gen:
+            nbytes += b.nbytes
+            shapes.append(b.shape)
+        elapsed = time.time() - t0
+        gbps = (nbytes / elapsed) / 1024**3
+        n = sum([s[0] for s in shapes])
+
+        return gbps, n
+
+    f = client.submit(do_ml, pure=False)
+    gbps, n = f.result()
+    print(f"We achieved about {gbps} gbps over {n} total examples")