diff --git a/sentence_transformers/sparse_encoder/model.py b/sentence_transformers/sparse_encoder/model.py
index 3e0da8949..781e47c5b 100644
--- a/sentence_transformers/sparse_encoder/model.py
+++ b/sentence_transformers/sparse_encoder/model.py
@@ -502,6 +502,10 @@ def encode(
 
         max_active_dims = max_active_dims if max_active_dims is not None else self.max_active_dims
 
+        forward_kwargs = dict(kwargs)
+        if max_active_dims is not None:
+            forward_kwargs["max_active_dims"] = max_active_dims
+
         all_embeddings = []
         length_sorted_idx = np.argsort([-self._input_length(sen) for sen in inputs])
         if self._can_flatten_inputs():
@@ -514,7 +518,7 @@ def encode(
             features = batch_to_device(features, device)
 
             with torch.inference_mode():
-                embeddings = self.forward(features, **kwargs)["sentence_embedding"]
+                embeddings = self.forward(features, **forward_kwargs)["sentence_embedding"]
 
                 if max_active_dims is not None:
                     embeddings = select_max_active_dims(embeddings, max_active_dims=max_active_dims)
diff --git a/tests/sparse_encoder/test_model.py b/tests/sparse_encoder/test_model.py
index f4876a177..fa9a72785 100644
--- a/tests/sparse_encoder/test_model.py
+++ b/tests/sparse_encoder/test_model.py
@@ -232,6 +232,31 @@ def test_inference_free_splade(inference_free_splade_bert_tiny_model: SparseEnco
     assert model[0].sub_modules["document"][0].max_seq_length == 256
 
 
+def test_inference_free_splade_max_active_dims_routing(inference_free_splade_bert_tiny_model: SparseEncoder):
+    model = inference_free_splade_bert_tiny_model
+    query = "What is the capital of France?"
+    document = "The capital of France is Paris."
+
+    # Encode without max_active_dims — baseline
+    query_emb = model.encode_query(query)
+    doc_emb = model.encode_document(document)
+
+    # Encode with max_active_dims — should route to the same sub-modules
+    query_emb_mad = model.encode_query(query, max_active_dims=50)
+    doc_emb_mad = model.encode_document(document, max_active_dims=50)
+
+    # The non-zero indices of the max_active_dims result should be a subset of the baseline
+    query_baseline_indices = query_emb.coalesce().indices()[0]
+    query_mad_indices = query_emb_mad.coalesce().indices()[0]
+    assert set(query_mad_indices.tolist()).issubset(set(query_baseline_indices.tolist()))
+    assert query_emb_mad._nnz() <= 50
+
+    doc_baseline_indices = doc_emb.coalesce().indices()[0]
+    doc_mad_indices = doc_emb_mad.coalesce().indices()[0]
+    assert set(doc_mad_indices.tolist()).issubset(set(doc_baseline_indices.tolist()))
+    assert doc_emb_mad._nnz() <= 50
+
+
 def test_encode_advanced_parameters(splade_bert_tiny_model: SparseEncoder, monkeypatch: pytest.MonkeyPatch):
     """Test that additional parameters are correctly passed to encode"""
     model = splade_bert_tiny_model
@@ -263,6 +288,32 @@ def spy_encode(*args, **kwargs):
     assert kwargs["custom_param"] == "value"
 
 
+def test_csr_max_active_dims_passed_to_forward(csr_bert_tiny_model: SparseEncoder, monkeypatch: pytest.MonkeyPatch):
+    model = csr_bert_tiny_model
+    assert isinstance(model[-1], SparseAutoEncoder)
+    assert model[-1].k == 16
+
+    # Verify that max_active_dims is passed to SparseAutoEncoder.forward()
+    forward_calls = []
+    original_forward = model[-1].forward
+
+    def spy_forward(*args, **kwargs):
+        forward_calls.append(kwargs)
+        return original_forward(*args, **kwargs)
+
+    monkeypatch.setattr(model[-1], "forward", spy_forward)
+
+    model.encode("Hello world", max_active_dims=5)
+    assert len(forward_calls) == 1
+    assert forward_calls[0]["max_active_dims"] == 5
+
+    # Without max_active_dims, the model's default max_active_dims is used
+    forward_calls.clear()
+    model.encode("Hello world")
+    assert len(forward_calls) == 1
+    assert forward_calls[0]["max_active_dims"] == model.max_active_dims
+
+
 def test_max_active_dims_set_init(splade_bert_tiny_model: SparseEncoder, csr_bert_tiny_model: SparseEncoder, tmp_path):
     splade_bert_tiny_model.save_pretrained(str(tmp_path / "splade_bert_tiny"))
     csr_bert_tiny_model.save_pretrained(str(tmp_path / "csr_bert_tiny"))