diff --git a/extra/models/unet.py b/extra/models/unet.py
index 92d4496320..94d2e95410 100644
--- a/extra/models/unet.py
+++ b/extra/models/unet.py
@@ -1,6 +1,6 @@
 from tinygrad import Tensor, dtypes
 from tinygrad.nn import Linear, Conv2d, GroupNorm, LayerNorm
-
+from tinygrad.device import is_dtype_supported
 from typing import Optional, Union, List, Any, Tuple
 import math
 
@@ -9,7 +9,8 @@ def timestep_embedding(timesteps:Tensor, dim:int, max_period=10000):
   half = dim // 2
   freqs = (-math.log(max_period) * Tensor.arange(half, device=timesteps.device) / half).exp()
   args = timesteps.unsqueeze(1) * freqs.unsqueeze(0)
-  return Tensor.cat(args.cos(), args.sin(), dim=-1).cast(dtypes.float16)
+  out = Tensor.cat(args.cos(), args.sin(), dim=-1)
+  return out.cast(dtypes.float16) if is_dtype_supported(dtypes.float16) else out
 
 class ResBlock:
   def __init__(self, channels:int, emb_channels:int, out_channels:int):
@@ -222,16 +223,17 @@ class UNetModel:
     ]
 
   def __call__(self, x:Tensor, tms:Tensor, ctx:Tensor, y:Optional[Tensor]=None) -> Tensor:
-    t_emb = timestep_embedding(tms, self.model_ch).cast(dtypes.float16)
+    t_emb = timestep_embedding(tms, self.model_ch)
     emb   = t_emb.sequential(self.time_embed)
 
     if y is not None:
       assert y.shape[0] == x.shape[0]
       emb = emb + y.sequential(self.label_emb[0])
 
-    emb = emb.cast(dtypes.float16)
-    ctx = ctx.cast(dtypes.float16)
-    x   = x  .cast(dtypes.float16)
+    if is_dtype_supported(dtypes.float16):
+      emb = emb.cast(dtypes.float16)
+      ctx = ctx.cast(dtypes.float16)
+      x   = x  .cast(dtypes.float16)
 
     def run(x:Tensor, bb) -> Tensor:
       if isinstance(bb, ResBlock): x = bb(x, emb)