diff --git a/jax/_src/numpy/linalg.py b/jax/_src/numpy/linalg.py
index f015ae15bca5..b1482103129d 100644
--- a/jax/_src/numpy/linalg.py
+++ b/jax/_src/numpy/linalg.py
@@ -53,9 +53,26 @@ def cholesky(a):
 
 
 @_wraps(np.linalg.svd)
-@partial(jit, static_argnames=('full_matrices', 'compute_uv'))
-def svd(a, full_matrices=True, compute_uv=True):
+@partial(jit, static_argnames=('full_matrices', 'compute_uv', 'hermitian'))
+def svd(a, full_matrices: bool = True, compute_uv: bool = True,
+        hermitian: bool = False):
   a = _promote_arg_dtypes(jnp.asarray(a))
+  if hermitian:
+    w, v = lax_linalg.eigh(a)
+    s = lax.abs(v)
+    if compute_uv:
+      sign = lax.sign(v)
+      idxs = lax.broadcasted_iota(np.int64, s.shape, dimension=s.ndim - 1)
+      s, idxs, sign = lax.sort((s, idxs, sign), dimension=-1, num_keys=1)
+      s = lax.rev(s, dimensions=[s.ndim - 1])
+      idxs = lax.rev(idxs, dimensions=[s.ndim - 1])
+      sign = lax.rev(sign, dimensions=[s.ndim - 1])
+      u = jnp.take_along_axis(w, idxs[..., None, :], axis=-1)
+      vh = _H(u * sign[..., None, :])
+      return u, s, vh
+    else:
+      return lax.rev(lax.sort(s, dimension=-1), dimensions=[s.ndim-1])
+
   return lax_linalg.svd(a, full_matrices, compute_uv)
 
 
diff --git a/tests/linalg_test.py b/tests/linalg_test.py
index 92b2dbe284c4..b447f0b9093b 100644
--- a/tests/linalg_test.py
+++ b/tests/linalg_test.py
@@ -527,18 +527,19 @@ def testNorm(self, shape, dtype, ord, axis, keepdims):
     self._CompileAndCheck(jnp_fn, args_maker)
 
   @parameterized.named_parameters(jtu.cases_from_list(
-      {"testcase_name": "_n={}_full_matrices={}_compute_uv={}".format(
+      {"testcase_name": "_n={}_full_matrices={}_compute_uv={}_hermitian={}".format(
           jtu.format_shape_dtype_string(b + (m, n), dtype), full_matrices,
-          compute_uv),
+          compute_uv, hermitian),
        "b": b, "m": m, "n": n, "dtype": dtype, "full_matrices": full_matrices,
-       "compute_uv": compute_uv}
+       "compute_uv": compute_uv, "hermitian": hermitian}
       for b in [(), (3,), (2, 3)]
       for m in [0, 2, 7, 29, 53]
       for n in [0, 2, 7, 29, 53]
       for dtype in float_types + complex_types
       for full_matrices in [False, True]
-      for compute_uv in [False, True]))
-  def testSVD(self, b, m, n, dtype, full_matrices, compute_uv):
+      for compute_uv in [False, True]
+      for hermitian in ([False, True] if m == n else [False])))
+  def testSVD(self, b, m, n, dtype, full_matrices, compute_uv, hermitian):
     if (jnp.issubdtype(dtype, np.complexfloating) and
         jtu.device_under_test() == "tpu"):
       raise unittest.SkipTest("No complex SVD implementation")
@@ -551,7 +552,10 @@ def norm(x):
       return norm / (max(1, m, n) * jnp.finfo(dtype).eps)
 
     a, = args_maker()
-    out = jnp.linalg.svd(a, full_matrices=full_matrices, compute_uv=compute_uv)
+    if hermitian:
+      a = a + np.conj(T(a))
+    out = jnp.linalg.svd(a, full_matrices=full_matrices, compute_uv=compute_uv,
+                         hermitian=hermitian)
     if compute_uv:
       # Check the reconstructed matrices
       if full_matrices: