diff --git a/extra/torch_backend/backend.py b/extra/torch_backend/backend.py
index a86b3d5477..c7f082cd2c 100644
--- a/extra/torch_backend/backend.py
+++ b/extra/torch_backend/backend.py
@@ -121,6 +121,12 @@ def cummax(self, dim):
 # TODO: move to tinygrad
 def nonzero(self): return aten.nonzero(self.cpu()).tiny()
 
+@torch.library.impl("aten::_linalg_eigh", "privateuseone")
+# TODO: move to tinygrad
+def _linalg_eigh(self, UPLO: str = 'U'):
+  w, v = torch.linalg.eigh(self.cpu(), UPLO=UPLO)
+  return w.tiny(), v.tiny()
+
 def upsample_backward(grad_out, output_size, input_size, *args, f=None): return f(grad_out.cpu(), output_size, input_size, *args).tiny()
 
 for i in [
diff --git a/extra/torch_backend/test.py b/extra/torch_backend/test.py
index 113c013c81..5d84226b00 100644
--- a/extra/torch_backend/test.py
+++ b/extra/torch_backend/test.py
@@ -170,6 +170,13 @@ class TestTorchBackend(unittest.TestCase):
     assert torch.equal(tensor_a, tensor_b)
     assert not torch.equal(tensor_a, tensor_c)
 
+  def test_linalg_eigh(self):
+    a = torch.tensor([[1, 2], [2, 1]], dtype=torch.float32, device=device)
+    w, v = torch.linalg.eigh(a)
+    np.testing.assert_equal(w.cpu().numpy(), [-1, 3])
+    recon = (v @ torch.diag(w) @ v.T).cpu().numpy()
+    np.testing.assert_allclose(recon, a.cpu().numpy(), atol=1e-6)
+
   def test_scalar_assign(self):
     a = torch.tensor([1, 2, 3], device=device)
     a[1] = 4