diff --git a/tests/sparse_test.py b/tests/sparse_test.py
index 1086429a9c43..02d9cfdd2128 100644
--- a/tests/sparse_test.py
+++ b/tests/sparse_test.py
@@ -138,8 +138,6 @@ def test_csr_fromdense_ad(self, shape, dtype):
   )
   @jax.default_matmul_precision("float32")
   def test_csr_matmul_ad(self, shape, dtype, bshape):
-    if jtu.is_device_rocm():
-      self.skipTest("test_csr_matmul_ad not supported on ROCm due to hipSPARSE issue")
     csr_matmul = sparse_csr._csr_matvec if len(bshape) == 1 else sparse_csr._csr_matmat
     tol = {np.float32: 2E-5, np.float64: 1E-12, np.complex64: 1E-5,
            np.complex128: 1E-12}
@@ -218,9 +216,6 @@ def test_csr_fromdense(self, shape, dtype):
     transpose=[True, False],
   )
   def test_csr_matvec(self, shape, dtype, transpose):
-    if jtu.is_device_rocm():
-      self.skipTest("test_csr_matvec not supported on ROCm due to hipSPARSE issue")
-
     op = lambda M: M.T if transpose else M
 
     v_rng = jtu.rand_default(self.rng())
@@ -589,8 +584,6 @@ def test_coo_spmm(self, shape, dtype, transpose):
   )
   @jtu.run_on_devices("gpu")
   def test_csr_spmv(self, shape, dtype, transpose):
-    if jtu.is_device_rocm():
-      self.skipTest("test_csr_spmv not supported on ROCm due to hipSPARSE issue")
     rng_sparse = sptu.rand_sparse(self.rng())
     rng_dense = jtu.rand_default(self.rng())
 
@@ -1040,8 +1033,6 @@ def test_transpose(self, shape, dtype, Obj):
     for Obj in [sparse.CSR, sparse.CSC, sparse.COO, sparse.BCOO]))
   @jax.default_matmul_precision("float32")
   def test_matmul(self, shape, dtype, Obj, bshape):
-    if jtu.is_device_rocm():
-      self.skipTest("test_matmul not supported on ROCm due to hipSPARSE issue")
     rng = sptu.rand_sparse(self.rng(), post=jnp.array)
     rng_b = jtu.rand_default(self.rng())
     M = rng(shape, dtype)