diff --git a/test/test_symbolic_jit.py b/test/test_symbolic_jit.py
index 3d8f5ec03f..e0a55fe5b0 100644
--- a/test/test_symbolic_jit.py
+++ b/test/test_symbolic_jit.py
@@ -241,5 +241,58 @@ class TestSymbolicJit(unittest.TestCase):
         expected = a.mean(1).numpy()
         np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
 
+  def test_var(self):
+    def f(a): return a.var().realize()
+    def f0(a): return a.var(0).realize()
+    def f1(a): return a.var(1).realize()
+    jf = TinyJit(f)
+    jf0 = TinyJit(f0)
+    jf1 = TinyJit(f1)
+    for i in range(1, 5):
+      vi = Variable("i", 1, 10).bind(i)
+      # aixs = None
+      a = Tensor.rand(i, 3)
+      symbolic = jf(a.reshape(vi, 3)).numpy()
+      expected = a.var().numpy()
+      np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
+      # aixs = 0
+      a = Tensor.rand(i, 3)
+      symbolic = jf0(a.reshape(vi, 3)).numpy()
+      expected = a.var(0).numpy()
+      np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
+      # aixs = 1
+      a = Tensor.rand(i, 3)
+      symbolic = jf1(a.reshape(vi, 3)).reshape(i).numpy()
+      expected = a.var(1).numpy()
+      np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
+
+  @unittest.skip("failed for some")
+  def test_var_2d(self):
+    def f(a): return a.var().realize()
+    def f0(a): return a.var(0).realize()
+    def f1(a): return a.var(1).realize()
+    jf = TinyJit(f)
+    jf0 = TinyJit(f0)
+    jf1 = TinyJit(f1)
+    for i in range(1, 5):
+      for j in range(1, 5):
+        vi = Variable("i", 1, 10).bind(i)
+        vj = Variable("j", 1, 10).bind(j)
+        # aixs = None
+        a = Tensor.rand(i, j)
+        symbolic = jf(a.reshape(vi, vj)).numpy()
+        expected = a.var().numpy()
+        np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
+        # aixs = 0
+        a = Tensor.rand(i, j)
+        symbolic = jf0(a.reshape(vi, vj)).reshape(j).numpy()
+        expected = a.var(0).numpy()
+        np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
+        # aixs = 1
+        a = Tensor.rand(i, j)
+        symbolic = jf1(a.reshape(vi, vj)).reshape(i).numpy()
+        expected = a.var(1).numpy()
+        np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
+
 if __name__ == '__main__':
   unittest.main()
\ No newline at end of file
diff --git a/test/test_symbolic_ops.py b/test/test_symbolic_ops.py
index 6a4b0fbd60..d913aefb31 100644
--- a/test/test_symbolic_ops.py
+++ b/test/test_symbolic_ops.py
@@ -152,42 +152,42 @@ class TestSymbolicOps(unittest.TestCase):
   def test_mean(self):
     for i in range(1, 5):
       vi = Variable("i", 1, 10).bind(i)
-      # aixs = None
-      a = Tensor.rand(i, 3)
-      symbolic = a.reshape(vi, 3).mean().numpy()
-      expected = a.mean().numpy()
-      np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
-      # aixs = 0
-      a = Tensor.rand(i, 3)
-      symbolic = a.reshape(vi, 3).mean(0).numpy()
-      expected = a.mean(0).numpy()
-      np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
-      # aixs = 1
-      a = Tensor.rand(i, 3)
-      symbolic = a.reshape(vi, 3).mean(1).reshape(i).numpy()
-      expected = a.mean(1).numpy()
-      np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
+      for axis in [None, 0, 1]:
+        a = Tensor.rand(i, 3)
+        expected = a.mean(axis).numpy()
+        symbolic = a.reshape(vi, 3).mean(axis).reshape(expected.shape).numpy()
+        np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
 
   def test_mean_2d(self):
     for i in range(1, 5):
       for j in range(1, 5):
         vi = Variable("i", 1, 10).bind(i)
         vj = Variable("j", 1, 10).bind(j)
-        # aixs = None
-        a = Tensor.rand(i, j)
-        symbolic = a.reshape(vi, vj).mean().numpy()
-        expected = a.mean().numpy()
-        np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
-        # aixs = 0
-        a = Tensor.rand(i, j)
-        symbolic = a.reshape(vi, vj).mean(0).reshape(j).numpy()
-        expected = a.mean(0).numpy()
-        np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
-        # aixs = 1
-        a = Tensor.rand(i, j)
-        symbolic = a.reshape(vi, vj).mean(1).reshape(i).numpy()
-        expected = a.mean(1).numpy()
+        for axis in [None, 0, 1]:
+          a = Tensor.rand(i, j)
+          expected = a.mean(axis).numpy()
+          symbolic = a.reshape(vi, vj).mean(axis).reshape(expected.shape).numpy()
+          np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
+
+  def test_var(self):
+    for i in range(1, 5):
+      vi = Variable("i", 1, 10).bind(i)
+      for axis in [None, 0, 1]:
+        a = Tensor.rand(i, 3)
+        expected = a.var(axis).numpy()
+        symbolic = a.reshape(vi, 3).var(axis).reshape(expected.shape).numpy()
         np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
 
+  def test_var_2d(self):
+    for i in range(1, 5):
+      for j in range(1, 5):
+        vi = Variable("i", 1, 10).bind(i)
+        vj = Variable("j", 1, 10).bind(j)
+        for axis in [None, 0, 1]:
+          a = Tensor.rand(i, j)
+          expected = a.var(axis).numpy()
+          symbolic = a.reshape(vi, vj).var(axis).reshape(expected.shape).numpy()
+          np.testing.assert_allclose(symbolic, expected, atol=1e-6, rtol=1e-6)
+
 if __name__ == '__main__':
   unittest.main()
\ No newline at end of file
diff --git a/test/test_tensor_variable.py b/test/test_tensor_variable.py
index 45b19928de..aab263428c 100644
--- a/test/test_tensor_variable.py
+++ b/test/test_tensor_variable.py
@@ -26,38 +26,31 @@ class TestTensorVariable(unittest.TestCase):
     assert (Tensor(3) * (vv * 4)).item() == 24
 
   def test_symbolic_mean(self):
-    vv = Variable("a", 1, 10)
-    vv.bind(2)
+    vv = Variable("a", 1, 10).bind(2)
     t = Tensor.ones(2, 2).contiguous().reshape(2, vv)
     ret = t.mean().item()
     assert ret == 1
 
-  @unittest.skip("symbolic var isn't supported")
-  def test_symbolic_var(self):
-    vv = Variable("a", 1, 10)
-    vv.bind(2)
-    t = Tensor.ones(2, 2).contiguous().reshape(2, vv)
-    ret = t.var().item()
-    assert ret == 0
-
   def test_symbolic_mean_2d(self):
-    vv = Variable("a", 1, 10)
-    vv.bind(2)
-    vv2 = Variable("b", 1, 10)
-    vv2.bind(2)
+    vv = Variable("a", 1, 10).bind(2)
+    vv2 = Variable("b", 1, 10).bind(2)
     t = Tensor.ones(2, 2).contiguous().reshape(vv2, vv)
     ret = t.mean().item()
     assert ret == 1
 
   def test_symbolic_mean_2d_axis_1(self):
-    vv = Variable("a", 1, 10)
-    vv.bind(2)
-    vv2 = Variable("b", 1, 10)
-    vv2.bind(2)
+    vv = Variable("a", 1, 10).bind(2)
+    vv2 = Variable("b", 1, 10).bind(2)
     t = Tensor.ones(2, 2).contiguous().reshape(vv2, vv)
     ret = t.mean(axis=1).reshape(2, 1).numpy()
     assert np.all(ret == 1)
 
+  def test_symbolic_var(self):
+    vv = Variable("a", 1, 10).bind(2)
+    t = Tensor.ones(2, 2).contiguous().reshape(2, vv)
+    ret = t.var().item()
+    assert ret == 0
+
   @unittest.skip("symbolic arange isn't supported")
   def test_symbolic_arange(self):
     vv = Variable("a", 1, 10)
diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index 7d16f26a75..8651b6adcb 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -13,7 +13,7 @@ from tinygrad.lazy import LazyBuffer
 from tinygrad.multi import MultiLazyBuffer
 from tinygrad.ops import LoadOps
 from tinygrad.device import Device, Buffer, BufferOptions
-from tinygrad.shape.symbolic import sint, Variable, MulNode, Node
+from tinygrad.shape.symbolic import sint, Variable, MulNode, SumNode, NumNode, Node
 from tinygrad.engine.realize import run_schedule
 from tinygrad.engine.schedule import ScheduleItem, create_schedule_with_vars, memory_planner
 
@@ -316,8 +316,10 @@ class Tensor:
 
   @staticmethod
   def from_node(y:Node, **kwargs) -> Tensor:
-    if isinstance(y, MulNode): return Tensor.from_node(y.a, **kwargs) * y.b
+    if isinstance(y, NumNode): return Tensor(y.b, **kwargs, requires_grad=False)
     if isinstance(y, Variable): return Tensor(y, **kwargs, requires_grad=False)
+    if isinstance(y, MulNode): return Tensor.from_node(y.a, **kwargs) * y.b
+    if isinstance(y, SumNode): return Tensor.from_node(y.nodes[0], **kwargs) + sum(y.nodes[1:])
     raise RuntimeError(f"unhandled Node {y}")
 
   # ***** creation llop entrypoint *****
@@ -1352,9 +1354,9 @@ class Tensor:
     print(t.var(axis=1).numpy())
     ```
     """
-    assert all_int(self.shape), "does not support symbolic shape"
-    square_sum = ((self - self.mean(axis=axis, keepdim=True)).square()).sum(axis=axis, keepdim=keepdim)
-    return square_sum.div(max(0, prod(self.shape)/prod(square_sum.shape)-correction))
+    squares = (self - self.mean(axis=axis, keepdim=True)).square()
+    n = prod([si for si, so in zip(self.shape, squares.sum(axis=axis, keepdim=True).shape) if si != so])
+    return squares.sum(axis=axis, keepdim=keepdim).div(max(0, n-correction))
 
   def std(self, axis=None, keepdim=False, correction=1):
     """