diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 039718c04..beebcd755 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -1494,6 +1494,49 @@ ggml_tensor * llm_graph_context::build_attn(
 
     return cur;
 }
+ggml_tensor * llm_graph_context::build_recurrent_state(
+    const llama_kv_cache_recurrent_state * kv_state,
+        ggml_cgraph * gf,
+        ggml_tensor * s,
+        ggml_tensor * state_copy,
+            int32_t   state_size,
+            int32_t   n_seqs,
+               bool   avoid_copies) const {
+
+    const auto n_kv    = kv_state->get_n_kv();
+    const auto kv_head = kv_state->get_head();
+    const auto rs_zero = kv_state->get_rs_z();
+
+    ggml_tensor * states = ggml_reshape_2d(ctx0, s, state_size, kv_state->get_size());
+
+    // Clear a single state which will then be copied to the other cleared states.
+    // Note that this is a no-op when the view is zero-sized.
+    ggml_tensor * state_zero = ggml_view_1d(ctx0, states, state_size*(rs_zero >= 0), rs_zero*states->nb[1]*(rs_zero >= 0));
+    ggml_build_forward_expand(gf, ggml_scale_inplace(ctx0, state_zero, 0));
+
+    ggml_tensor * output_states;
+
+    if (!avoid_copies) {
+        // copy states
+        // NOTE: assuming the copy destinations are ALL contained between kv_head and kv_head + n_kv
+        // {state_size, kv_size} -> {state_size, n_seqs}
+        output_states = ggml_get_rows(ctx0, states, ggml_view_1d(ctx0, state_copy, n_seqs, 0));
+        ggml_build_forward_expand(gf, output_states);
+    } else {
+        // FIXME: make the gathering operation happen before the copy below
+        //        (maybe with an optional lambda function passed as a parameter instead of `avoid_copies`?)
+        output_states = states;
+    }
+
+    // copy extra states which won't be changed further (between n_seqs and n_kv)
+    ggml_tensor * states_extra = ggml_get_rows(ctx0, states, ggml_view_1d(ctx0, state_copy, n_kv - n_seqs, n_seqs*state_copy->nb[0]));
+    ggml_build_forward_expand(gf,
+        ggml_cpy(ctx0,
+            states_extra,
+            ggml_view_1d(ctx0, s, state_size*(n_kv - n_seqs), (kv_head + n_seqs)*state_size*ggml_element_size(s))));
+
+    return output_states;
+}
 
 llm_graph_input_rs * llm_graph_context::build_rs_inp_recurrent() const {
     const auto * kv_state = static_cast<const llama_kv_cache_recurrent_state *>(mstate);
@@ -1519,40 +1562,7 @@ ggml_tensor * llm_graph_context::build_rs(
                bool   avoid_copies) const {
 
     const auto * kv_state = static_cast<const llama_kv_cache_recurrent_state *>(mstate);
-
-    const auto n_kv    = kv_state->get_n_kv();
-    const auto kv_head = kv_state->get_head();
-    const auto rs_zero = kv_state->get_rs_z();
-
-    ggml_tensor * states = ggml_reshape_2d(ctx0, s, state_size, kv_state->get_size());
-
-    // Clear a single state which will then be copied to the other cleared states.
-    // Note that this is a no-op when the view is zero-sized.
-    ggml_tensor * state_zero = ggml_view_1d(ctx0, states, state_size*(rs_zero >= 0), rs_zero*states->nb[1]*(rs_zero >= 0));
-    ggml_build_forward_expand(gf, ggml_scale_inplace(ctx0, state_zero, 0));
-
-    ggml_tensor * output_states;
-
-    if (!avoid_copies) {
-        // copy states
-        // NOTE: assuming the copy destinations are ALL contained between kv_head and kv_head + n_kv
-        // {state_size, kv_size} -> {state_size, n_seqs}
-        output_states = ggml_get_rows(ctx0, states, ggml_view_1d(ctx0, inp->s_copy, n_seqs, 0));
-        ggml_build_forward_expand(gf, output_states);
-    } else {
-        // FIXME: make the gathering operation happen before the copy below
-        //        (maybe with an optional lambda function passed as a parameter instead of `avoid_copies`?)
-        output_states = states;
-    }
-
-    // copy extra states which won't be changed further (between n_seqs and n_kv)
-    ggml_tensor * states_extra = ggml_get_rows(ctx0, states, ggml_view_1d(ctx0, inp->s_copy, n_kv - n_seqs, n_seqs*inp->s_copy->nb[0]));
-    ggml_build_forward_expand(gf,
-        ggml_cpy(ctx0,
-            states_extra,
-            ggml_view_1d(ctx0, s, state_size*(n_kv - n_seqs), (kv_head + n_seqs)*state_size*ggml_element_size(s))));
-
-    return output_states;
+    return build_recurrent_state(kv_state, gf, s, inp->s_copy, state_size, n_seqs, avoid_copies);
 }
 
 llm_graph_input_rs_hybrid_recurrent * llm_graph_context::build_rs_inp_hybrid_recurrent() const {
@@ -1578,40 +1588,7 @@ ggml_tensor * llm_graph_context::build_rs(
                bool   avoid_copies) const {
 
     const auto * kv_state = static_cast<const llama_kv_cache_hybrid_recurrent_state *>(mstate)->get_state_recurrent();
-
-    const auto n_kv    = kv_state->get_n_kv();
-    const auto kv_head = kv_state->get_head();
-    const auto rs_zero = kv_state->get_rs_z();
-
-    ggml_tensor * states = ggml_reshape_2d(ctx0, s, state_size, kv_state->get_size());
-
-    // Clear a single state which will then be copied to the other cleared states.
-    // Note that this is a no-op when the view is zero-sized.
-    ggml_tensor * state_zero = ggml_view_1d(ctx0, states, state_size*(rs_zero >= 0), rs_zero*states->nb[1]*(rs_zero >= 0));
-    ggml_build_forward_expand(gf, ggml_scale_inplace(ctx0, state_zero, 0));
-
-    ggml_tensor * output_states;
-
-    if (!avoid_copies) {
-        // copy states
-        // NOTE: assuming the copy destinations are ALL contained between kv_head and kv_head + n_kv
-        // {state_size, kv_size} -> {state_size, n_seqs}
-        output_states = ggml_get_rows(ctx0, states, ggml_view_1d(ctx0, inp->s_copy, n_seqs, 0));
-        ggml_build_forward_expand(gf, output_states);
-    } else {
-        // FIXME: make the gathering operation happen before the copy below
-        //        (maybe with an optional lambda function passed as a parameter instead of `avoid_copies`?)
-        output_states = states;
-    }
-
-    // copy extra states which won't be changed further (between n_seqs and n_kv)
-    ggml_tensor * states_extra = ggml_get_rows(ctx0, states, ggml_view_1d(ctx0, inp->s_copy, n_kv - n_seqs, n_seqs*inp->s_copy->nb[0]));
-    ggml_build_forward_expand(gf,
-        ggml_cpy(ctx0,
-            states_extra,
-            ggml_view_1d(ctx0, s, state_size*(n_kv - n_seqs), (kv_head + n_seqs)*state_size*ggml_element_size(s))));
-
-    return output_states;
+    return build_recurrent_state(kv_state, gf, s, inp->s_copy, state_size, n_seqs, avoid_copies);
 }
 
 ggml_tensor * llm_graph_context::build_rwkv_token_shift_load(
diff --git a/src/llama-graph.h b/src/llama-graph.h
index 77f19a673..f705ea81d 100644
--- a/src/llama-graph.h
+++ b/src/llama-graph.h
@@ -622,6 +622,15 @@ struct llm_graph_context {
     // recurrent
     //
 
+    ggml_tensor * build_recurrent_state(
+        const llama_kv_cache_recurrent_state * kv_state,
+            ggml_cgraph * gf,
+            ggml_tensor * s,
+            ggml_tensor * state_copy,
+                int32_t   state_size,
+                int32_t   n_seqs,
+                   bool   avoid_copies = false) const;
+
     llm_graph_input_rs * build_rs_inp_recurrent() const;
 
     ggml_tensor * build_rs(