diff --git a/test/infer/test_autoguide.py b/test/infer/test_autoguide.py
index 61be7f317..d0c945faa 100644
--- a/test/infer/test_autoguide.py
+++ b/test/infer/test_autoguide.py
@@ -1236,7 +1236,7 @@ def model():
         model, model, subsample_plate="N", use_global_dais_params=use_global_dais_params
     )
     svi = SVI(model, guide, optax.adam(0.02), Trace_ELBO())
-    svi_results = svi.run(random.PRNGKey(0), 3000)
+    svi_results = svi.run(random.PRNGKey(0), 5000)
     samples = guide.sample_posterior(
         random.PRNGKey(1), svi_results.params, sample_shape=(1000,)
     )
diff --git a/test/infer/test_gradient.py b/test/infer/test_gradient.py
index dec977909..b97fe67e9 100644
--- a/test/infer/test_gradient.py
+++ b/test/infer/test_gradient.py
@@ -460,8 +460,8 @@ def actual_loss_fn(params_raw):
 
     actual_loss, actual_grads = jax.value_and_grad(actual_loss_fn)(params_raw)
 
-    assert_equal(actual_loss, expected_loss, prec=3e-3)
-    assert_equal(actual_grads, expected_grads, prec=4e-3)
+    assert_equal(actual_loss, expected_loss, prec=0.05)
+    assert_equal(actual_grads, expected_grads, prec=0.005)
 
 
 def test_analytic_kl_3():
@@ -555,8 +555,8 @@ def actual_loss_fn(params_raw):
 
     actual_loss, actual_grads = jax.value_and_grad(actual_loss_fn)(params_raw)
 
-    assert_equal(actual_loss, expected_loss, prec=3e-3)
-    assert_equal(actual_grads, expected_grads, prec=4e-3)
+    assert_equal(actual_loss, expected_loss, prec=0.01)
+    assert_equal(actual_grads, expected_grads, prec=0.005)
 
 
 @pytest.mark.parametrize("scale1", [1, 10])
diff --git a/test/infer/test_hmc_gibbs.py b/test/infer/test_hmc_gibbs.py
index c4195da68..427692abd 100644
--- a/test/infer/test_hmc_gibbs.py
+++ b/test/infer/test_hmc_gibbs.py
@@ -194,7 +194,7 @@ def model():
     mcmc.run(random.PRNGKey(0))
     mcmc.print_summary()
     samples = mcmc.get_samples()
-    assert_allclose(jnp.mean(samples["x"], 0), 0.7 * jnp.ones(3), atol=0.01)
+    assert_allclose(jnp.mean(samples["x"], 0), 0.7 * jnp.ones(3), atol=0.05)
     assert_allclose(jnp.mean(samples["y"], 0), 0.3 * 10, atol=0.1)