diff --git a/tinygrad/llm/gguf.py b/tinygrad/llm/gguf.py
index 39e56ade38..de1482515e 100644
--- a/tinygrad/llm/gguf.py
+++ b/tinygrad/llm/gguf.py
@@ -38,7 +38,7 @@ def ggml_data_to_tensor(t: Tensor, n: int, ggml_type: int) -> Tensor:
   def q_to_uint8(t: Tensor, b: int) -> Tensor:
     # TODO: rewrite with arange?
     shift_tensor, bitmask = Tensor.const(t.dtype, tuple(2**(i*b) for i in range(8//b))), 0xff >> (8 - b)
-    return t.unsqueeze(-1).expand((*t.shape,8//b)).div(shift_tensor, rounding_mode="trunc").bitwise_and(bitmask).transpose(-1, -2).flatten(-2)
+    return t.unsqueeze(-1).div(shift_tensor, rounding_mode="trunc").bitwise_and(bitmask).transpose(-1, -2).flatten(-2)
 
   if (nelements_nbytes := _GGML_QUANT.get(ggml_type)) is not None:
     from tinygrad.runtime.autogen import ggml_common as _ggml
@@ -68,7 +68,7 @@ def ggml_data_to_tensor(t: Tensor, n: int, ggml_type: int) -> Tensor:
     if ggml_type == 14:
       xl, xh = q_to_uint8(blocks[:,:128].reshape((-1, 2, 64)), 4), q_to_uint8(blocks[:,128:192].reshape((-1, 2, 32)), 2).lshift(4)
       scales = blocks[:,192:208].bitcast(dtypes.int8).unsqueeze(-1).expand((-1, 16, 16)).reshape((-1, 256))
-      d = blocks[:,-2:].bitcast(dtypes.float16).cast(dtypes.float32).expand((-1, 256))
+      d = blocks[:,-2:].bitcast(dtypes.float16).cast(dtypes.float32)
       return d * (xl.bitwise_or(xh).bitcast(dtypes.int8) - 32).flatten(-2) * scales
     if ggml_type == 18:
       d = blocks[:, :2].bitcast(dtypes.float16).cast(dtypes.float32).reshape((-1, 1, 1, 1))
diff --git a/tinygrad/mixin/op.py b/tinygrad/mixin/op.py
index f523215d99..7474debbc5 100644
--- a/tinygrad/mixin/op.py
+++ b/tinygrad/mixin/op.py
@@ -440,7 +440,7 @@ class OpMixin(ElementwiseMixin, ReduceMixin):
     sz = merge_dicts([dict(zip(s, x.shape)) for s, x in zip(inputs, xs)])
     alpha = sorted(sz)
     # align all tensors to alphabet, multiply, sum non-output, permute to output order
-    xs = [x.permute(*[s.index(c) for c in sorted(s)]).reshape([sz[c] if c in s else 1 for c in alpha]).expand([sz[c] for c in alpha]) if s else x
+    xs = [x.permute(*[s.index(c) for c in sorted(s)]).reshape([sz[c] if c in s else 1 for c in alpha]) if s else x
           for s, x in zip(inputs, xs)]
     return xs[0].uprod(*xs[1:]).sum([i for i,c in enumerate(alpha) if c not in rhs], dtype=dtype).permute(argsort(argsort(list(rhs))))
 
@@ -831,7 +831,7 @@ class OpMixin(ElementwiseMixin, ReduceMixin):
     if self.ndim == 0: return self
     x = self.transpose(axis, -1)
     last_dim_size = x.shape[-1]
-    x_unsqueezed = x.unsqueeze(-2).expand((None,)*(self.ndim-1)+(last_dim_size, None))
+    x_unsqueezed = x.unsqueeze(-2)
     x_cummax = x.cummax(-1)[0].detach()
     mask = type(self).ones(last_dim_size, last_dim_size, buffer=False, dtype=dtypes.bool).tril()
     ret = mask.where(x_unsqueezed - x_cummax.unsqueeze(-1), self.dtype.min).exp().sum(-1).log() + x_cummax
diff --git a/tinygrad/mixin/rand.py b/tinygrad/mixin/rand.py
index b621633591..003af96cd7 100644
--- a/tinygrad/mixin/rand.py
+++ b/tinygrad/mixin/rand.py
@@ -269,7 +269,7 @@ class RandMixin(OpMixin):
     if replacement or num_samples == 1:
       cdf = (cw := weight.cumsum(1).float()) / cw[:, -1].unsqueeze(1)
       unif_samples = type(self).rand(num_samples, cdf.shape[0], 1).to(self.device)  # type: ignore[attr-defined]
-      indices = (unif_samples.expand((-1, -1, cdf.shape[1])) >= cdf).sum(2).permute((1, 0))
+      indices = (unif_samples >= cdf).sum(2).permute((1, 0))
     else:
       # Efraimidis-Spirakis
       indices = (weight.rand_like(dtype=dtypes.float32).log2() / weight).topk(num_samples, dim=1)[1]
diff --git a/tinygrad/nn/__init__.py b/tinygrad/nn/__init__.py
index c4732bcd0d..203a73fcca 100644
--- a/tinygrad/nn/__init__.py
+++ b/tinygrad/nn/__init__.py
@@ -40,7 +40,7 @@ class BatchNorm:
 
   def calc_stats(self, x:Tensor) -> tuple[Tensor, Tensor]:
     shape_mask: list[int] = [1, -1, *([1]*(x.ndim-2))]
-    if self.track_running_stats and not TRAINING: return self.running_mean, self.running_var.reshape(shape=shape_mask).expand(x.shape)
+    if self.track_running_stats and not TRAINING: return self.running_mean, self.running_var.reshape(shape=shape_mask)
     # This requires two full memory accesses to x
     # https://github.com/pytorch/pytorch/blob/c618dc13d2aa23625cb0d7ada694137532a4fa33/aten/src/ATen/native/cuda/Normalization.cuh
     # There's "online" algorithms that fix this, like https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Welford's_Online_algorithm