diff --git a/test/test_dtype.py b/test/test_dtype.py
index 9254b4ac07..6311b4046e 100644
--- a/test/test_dtype.py
+++ b/test/test_dtype.py
@@ -249,7 +249,7 @@ class TestUint8Dtype(TestDType):
 @unittest.skipIf(Device.DEFAULT == "WEBGL", "No bitcast on WebGL")
 class TestBitCast(unittest.TestCase):
   def test_shape_change_bitcast(self):
-    with self.assertRaises(AssertionError):
+    with self.assertRaises(RuntimeError):
       _test_bitcast(Tensor([100000], dtype=dtypes.float32), dtypes.uint8, [100000])
 
   def test_bitcast_float_to_int32(self):
diff --git a/test/unit/test_disk_tensor.py b/test/unit/test_disk_tensor.py
index 1929296a57..643a7593f2 100644
--- a/test/unit/test_disk_tensor.py
+++ b/test/unit/test_disk_tensor.py
@@ -1,6 +1,7 @@
-import pathlib, unittest
+import pathlib, tempfile, unittest
 import numpy as np
 from tinygrad import Tensor, Device, dtypes
+from tinygrad.dtype import DType
 from tinygrad.nn.state import safe_load, safe_save, get_state_dict, torch_load
 from tinygrad.helpers import Timing, fetch, temp
 from test.helpers import is_dtype_supported
@@ -36,14 +37,50 @@ test_fn = pathlib.Path(__file__).parents[2] / "weights/LLaMA/7B/consolidated.00.
 #test_size = test_fn.stat().st_size
 test_size = 1024*1024*1024*2
 
+def _test_bitcasted(t: Tensor, dt: DType, expected):
+  np.testing.assert_allclose(t.bitcast(dt).numpy(), expected)
+
 # sudo su -c 'sync; echo 1 > /proc/sys/vm/drop_caches' && python3 test/unit/test_disk_tensor.py TestRawDiskBuffer.test_readinto_read_speed
-@unittest.skipIf(not test_fn.exists(), "download LLaMA weights for read in speed tests")
 class TestRawDiskBuffer(unittest.TestCase):
+  @unittest.skipIf(not test_fn.exists(), "download LLaMA weights for read in speed tests")
   def test_readinto_read_speed(self):
     tst = np.empty(test_size, np.uint8)
     with open(test_fn, "rb") as f:
       with Timing("copy in ", lambda et_ns: f" {test_size/et_ns:.2f} GB/s"):
         f.readinto(tst)
+  def test_bitcasts_on_disk(self):
+    tmp = tempfile.mktemp()
+    # ground truth = https://evanw.github.io/float-toy/
+    t = Tensor.empty((128, 128), dtype=dtypes.uint8, device=f"disk:{tmp}") # uint8
+    # all zeroes
+    _test_bitcasted(t, dtypes.float16, 0.0)
+    _test_bitcasted(t, dtypes.uint16, 0)
+    _test_bitcasted(t, dtypes.float32, 0.0)
+    _test_bitcasted(t, dtypes.uint32, 0)
+    # pi in float16 stored via int16
+    t.bitcast(dtypes.uint16).assign(Tensor.full((128, 64), 0x4248, dtype=dtypes.uint16)).realize()
+    _test_bitcasted(t, dtypes.float16, 3.141)
+    _test_bitcasted(t, dtypes.float32, 50.064727)
+    _test_bitcasted(t, dtypes.uint16, 0x4248)
+    _test_bitcasted(t, dtypes.uint32, 0x42484248)
+    # pi in float32 stored via float32
+    t.bitcast(dtypes.float32).assign(Tensor.full((128, 32), 3.1415927, dtype=dtypes.float32)).realize()
+    _test_bitcasted(t, dtypes.float32, 3.1415927)
+    _test_bitcasted(t, dtypes.uint32, 0x40490FDB)
+    # doesn't suport normal cast
+    with self.assertRaises(RuntimeError):
+      Tensor.empty((4,), dtype=dtypes.int16, device=f"disk:{tmp}").cast(dtypes.float16)
+
+    # Those two should be moved to test_dtype.py:test_shape_change_bitcast after bitcast works on non-disk
+    with self.assertRaises(RuntimeError):
+      # should fail because 3 int8 is 3 bytes but float16 is two and 3 isn't a multiple of 2
+      Tensor.empty((3,), dtype=dtypes.int8, device=f"DISK:{tmp}").bitcast(dtypes.float16)
+
+    with self.assertRaises(RuntimeError):
+      # should fail because backprop through bitcast is undefined
+      Tensor.empty((4,), dtype=dtypes.int8, requires_grad=True, device=f"DISK:{tmp}").bitcast(dtypes.float16)
+
+    pathlib.Path(tmp).unlink()
 
 @unittest.skipIf(Device.DEFAULT == "WEBGPU", "webgpu doesn't support uint8 datatype")
 class TestSafetensors(unittest.TestCase):
diff --git a/tinygrad/lazy.py b/tinygrad/lazy.py
index 31c2655be2..f76971a1a3 100644
--- a/tinygrad/lazy.py
+++ b/tinygrad/lazy.py
@@ -79,10 +79,18 @@ class LazyBuffer:
 
   def cast(self, dtype:DType, bitcast:bool=False):
     if self.dtype == dtype: return self
+    if self.device.startswith("DISK") and not bitcast: raise RuntimeError("attempted to cast disk buffer (bitcast only)")
     # TODO: applying this makes gpt2 slower
     if getenv("CAST_BEFORE_VIEW", 1) and dtype.itemsize <= self.dtype.itemsize and self != self.base:
       return self.base.cast(dtype, bitcast)._view(self.st)
-    return create_lazybuffer(self.device, ShapeTracker.from_shape(self.shape), dtype, UnaryOps.CAST, (dtype, bitcast), (self,))
+    new_shape = self.shape
+    if bitcast and self.dtype.itemsize != dtype.itemsize:
+      if not self.device.startswith("DISK"): raise RuntimeError("shape changing bitcast only supported on DISK right now")
+      if not all_int(new_shape): raise RuntimeError("shape changing bitcast with symbolic shape isn't supported yet")
+      # https://pytorch.org/docs/stable/generated/torch.Tensor.view.html
+      if not (new_shape[-1]*self.dtype.itemsize) % dtype.itemsize == 0: raise RuntimeError("unsupported size in bitcast")
+      new_shape = new_shape[:-1] + ((new_shape[-1]*self.dtype.itemsize) // dtype.itemsize,)
+    return create_lazybuffer(self.device, ShapeTracker.from_shape(new_shape), dtype, UnaryOps.CAST, (dtype, bitcast), (self,))
 
   def is_unrealized_const(self): return not self.base.realized and self.base.op is LoadOps.CONST
   def is_unrealized_contiguous_const(self): return self.base == self and not self.base.realized and self.op is LoadOps.CONST
diff --git a/tinygrad/nn/state.py b/tinygrad/nn/state.py
index eb98b586f2..38a9713996 100644
--- a/tinygrad/nn/state.py
+++ b/tinygrad/nn/state.py
@@ -14,7 +14,7 @@ inverse_safe_dtypes = {v:k for k,v in safe_dtypes.items()}
 
 def safe_load_metadata(fn:Union[Tensor,str]) -> Tuple[Tensor, int, Any]:
   t = fn if isinstance(fn, Tensor) else Tensor.empty(os.stat(fn).st_size, dtype=dtypes.uint8, device=f"disk:{fn}")
-  json_len = t[0:1].cast(dtypes.int64).item()
+  json_len = t[0:8].bitcast(dtypes.int64).item()
   return t, json_len, json.loads(t[8:8+json_len].numpy().tobytes())
 
 def safe_load(fn:Union[Tensor,str]) -> Dict[str, Tensor]:
@@ -23,8 +23,8 @@ def safe_load(fn:Union[Tensor,str]) -> Dict[str, Tensor]:
   for k,v in metadata.items():
     if k == "__metadata__": continue
     dtype = safe_dtypes[v['dtype']]
-    sz = (v['data_offsets'][1]-v['data_offsets'][0])//dtype.itemsize
-    ret[k] = t[8+json_len+v['data_offsets'][0]:8+json_len+v['data_offsets'][0]+sz].cast(dtype).reshape(v['shape'])
+    sz = (v['data_offsets'][1]-v['data_offsets'][0])
+    ret[k] = t[8+json_len+v['data_offsets'][0]:8+json_len+v['data_offsets'][0]+sz].bitcast(dtype).reshape(v['shape'])
   return ret
 
 def safe_save(tensors:Dict[str, Tensor], fn:str, metadata:Optional[Dict[str, Any]]=None):
@@ -37,7 +37,7 @@ def safe_save(tensors:Dict[str, Tensor], fn:str, metadata:Optional[Dict[str, Any
   j += "\x20"*((8-len(j)%8)%8)
   pathlib.Path(fn).unlink(missing_ok=True)
   t = Tensor.empty(8+len(j)+offset, dtype=dtypes.uint8, device=f"disk:{fn}")
-  t[0:1].cast(dtypes.int64).assign([len(j)])
+  t[0:8].bitcast(dtypes.int64).assign([len(j)])
   t[8:8+len(j)].assign(list(j.encode('utf-8')))
   for k,v in safe_load(t).items(): v.assign(tensors[k])
 
@@ -83,7 +83,7 @@ def torch_load(fn:str) -> Dict[str, Tensor]:
     lens[storage[2]] = storage[4] * storage[1].itemsize
     if storage[2] not in offsets: return None
     byte_offset = offsets[storage[2]]+storage_offset*storage[1].itemsize
-    ret = t[byte_offset:byte_offset+prod(size)].cast(storage[1])
+    ret = t[byte_offset:byte_offset+prod(size)*storage[1].itemsize].bitcast(storage[1])
 
     # 7 lines to deal with permuted tensors. NOTE: this currently requires reading off the disk
     shape_strides = [(s, st) for s,st in zip(size, stride) if s != 1]
diff --git a/tinygrad/runtime/ops_disk.py b/tinygrad/runtime/ops_disk.py
index 3903666a3a..9691aba5a3 100644
--- a/tinygrad/runtime/ops_disk.py
+++ b/tinygrad/runtime/ops_disk.py
@@ -53,7 +53,7 @@ class DiskRunner(JITRunner):
     # TODO: there shouldn't actually be casts here, bitcasts should fold into the load
     if ast.src[0].op == UnaryOps.CAST:
       top_src = ast.src[0].src[0]
-      # TODO: assert that this is bitcast
+      assert ast.src[0].arg[1], "disk only supports bitcasts, not normal casts"
       self.new_dtype = ast.src[0].arg[0]
     else:
       top_src = ast.src[0]
diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index 8c1e71f95f..e0844fec63 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -1033,7 +1033,7 @@ class Tensor:
     return self.to("LLVM").bitcast(dtypes.uint16).cast(dtypes.uint32).mul(1<<16).bitcast(dtypes.float32).cast(dtype)
   def cast(self, dtype:DType) -> Tensor: return self if self.dtype == dtype else mlops.Cast.apply(self, dtype=dtype)
   def bitcast(self, dtype:DType) -> Tensor:
-    assert self.dtype.itemsize == dtype.itemsize, "can't bitcast mismatched dtype itemsizes"
+    if self.requires_grad: raise RuntimeError("can't backprop through bitcast")
     return mlops.Cast.apply(self, dtype=dtype, bitcast=True) if self.dtype != dtype else self
   def float(self) -> Tensor: return self.cast(dtypes.float32)
   def half(self) -> Tensor: return self.cast(dtypes.float16)