diff --git a/src/llama-kv-cache-unified.cpp b/src/llama-kv-cache-unified.cpp
index f0dcf04d3..74d99182d 100644
--- a/src/llama-kv-cache-unified.cpp
+++ b/src/llama-kv-cache-unified.cpp
@@ -937,17 +937,17 @@ ggml_tensor * llama_kv_cache_unified::get_v(ggml_context * ctx, int32_t il, uint
                 hparams.n_embd_head_v, hparams.n_head_kv(il), n_kv, ns,
                 ggml_row_size(v->type, hparams.n_embd_head_v),    // v->nb[1]
                 ggml_row_size(v->type, hparams.n_embd_v_gqa(il)), // v->nb[2]
-                size_virt,
+                size_virt,                                        // v->nb[3]
                 size_virt*sinfo.s0);
     }
 
     // note: v->nb[1] > v->nb[2]
     return ggml_view_4d(ctx, v,
             n_kv, hparams.n_head_kv(il), hparams.n_embd_head_v, ns,
-            ggml_row_size(v->type, v->ne[1]*hparams.n_embd_head_v), // v->nb[1]
-            ggml_row_size(v->type, v->ne[1]),                       // v->nb[2]
-            size_virt,
-            size_virt*sinfo.s0);
+            ggml_row_size(v->type, v->ne[1]*n_seq_virt*hparams.n_embd_head_v), // v->nb[1]
+            ggml_row_size(v->type, v->ne[1]*n_seq_virt),                       // v->nb[2]
+            ggml_row_size(v->type, v->ne[1]),                                  // v->nb[3]
+            ggml_row_size(v->type, v->ne[1]*sinfo.s0));
 }
 
 ggml_tensor * llama_kv_cache_unified::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * kv_idxs, int32_t il, const slot_info & sinfo) const {
@@ -961,20 +961,9 @@ ggml_tensor * llama_kv_cache_unified::cpy_k(ggml_context * ctx, ggml_tensor * k_
     k_cur = ggml_reshape_2d(ctx, k_cur, k->ne[0], n_tokens);
 
     if (kv_idxs && supports_set_rows) {
-        const uint32_t ns = sinfo.s1 - sinfo.s0 + 1;
+        k = ggml_reshape_2d(ctx, k, k->ne[0], k->ne[1]*k->ne[2]);
 
-        const uint64_t size_virt = ggml_row_size(k->type, hparams.n_embd_k_gqa(il)*get_size());
-
-        ggml_tensor * k_view = ggml_view_3d(ctx, k, k->ne[0], k->ne[1], ns,
-                ggml_row_size(k->type, k->ne[0]),
-                size_virt,
-                size_virt*sinfo.s0);
-
-        k_cur = ggml_reshape_3d(ctx, k_cur, k_cur->ne[0], k_cur->ne[1]/ns, ns);
-
-        kv_idxs = ggml_reshape_2d(ctx, kv_idxs, n_tokens/ns, ns);
-
-        return ggml_set_rows(ctx, k_view, k_cur, kv_idxs);
+        return ggml_set_rows(ctx, k, k_cur, kv_idxs);
     }
 
     // TODO: fallback to old ggml_cpy() method for backwards compatibility
@@ -1000,45 +989,27 @@ ggml_tensor * llama_kv_cache_unified::cpy_v(ggml_context * ctx, ggml_tensor * v_
     v_cur = ggml_reshape_2d(ctx, v_cur, n_embd_v_gqa, n_tokens);
 
     if (kv_idxs && supports_set_rows) {
-        const uint32_t ns = sinfo.s1 - sinfo.s0 + 1;
-
-        const uint64_t size_virt = ggml_row_size(v->type, hparams.n_embd_v_gqa(il)*get_size());
-
         if (!v_trans) {
-            ggml_tensor * v_view = ggml_view_3d(ctx, v, v->ne[0], v->ne[1], ns,
-                    ggml_row_size(v->type, v->ne[0]),
-                    size_virt,
-                    size_virt*sinfo.s0);
+            v = ggml_reshape_2d(ctx, v, v->ne[0], v->ne[1]*v->ne[2]);
 
-            v_cur = ggml_reshape_3d(ctx, v_cur, v_cur->ne[0], v_cur->ne[1]/ns, ns);
-
-            kv_idxs = ggml_reshape_2d(ctx, kv_idxs, n_tokens/ns, ns);
-
-            return ggml_set_rows(ctx, v_view, v_cur, kv_idxs);
+            return ggml_set_rows(ctx, v, v_cur, kv_idxs);
         }
 
         // the row becomes a single element
-        ggml_tensor * v_view = ggml_view_4d(ctx, v, 1, v->ne[1], v->ne[0], ns,
-                ggml_row_size(v->type, 1),
-                ggml_row_size(v->type, v->ne[1]),
-                size_virt,
-                size_virt*sinfo.s0);
+        ggml_tensor * v_view = ggml_reshape_3d(ctx, v, 1, v->ne[1]*v->ne[2], v->ne[0]);
 
         // note: the V cache is transposed when not using flash attention
-        v_cur = ggml_permute(ctx, ggml_reshape_4d(ctx, v_cur, v_cur->ne[0], 1, v_cur->ne[1]/ns, ns), 2, 0, 1, 3);
+        v_cur = ggml_permute(ctx, ggml_reshape_3d(ctx, v_cur, v_cur->ne[0], 1, v_cur->ne[1]), 2, 0, 1, 3);
 
         // note: we can be more explicit here at the cost of extra cont
         //       however, above we take advantage that a row of single element is always contiguous regardless of the row stride
-        //v_cur = ggml_reshape_3d(ctx, v_cur, n_embd_v_gqa, v_cur->ne[1]/ns, ns);
         //v_cur = ggml_transpose(ctx, v_cur);
-        //v_cur = ggml_cont_4d(ctx, v_cur, 1, v_cur->ne[0], v_cur->ne[1], v_cur->ne[2]);
+        //v_cur = ggml_cont_3d(ctx, v_cur, 1, v_cur->ne[0], v_cur->ne[1]);
 
         // we broadcast the KV indices n_embd_v_gqa times
-        // v       [1,           n_kv,        n_embd_v_gqa, ns]
-        // v_cur   [1,           n_tokens/ns, n_embd_v_gqa, ns]
-        // kv_idxs [n_tokens/ns, 1,           ns]
-
-        kv_idxs = ggml_reshape_3d(ctx, kv_idxs, n_tokens/ns, 1, ns);
+        // v       [1,        n_kv*n_seq_virt, n_embd_v_gqa]
+        // v_cur   [1,        n_tokens,        n_embd_v_gqa]
+        // kv_idxs [n_tokens, 1,               1]
 
         return ggml_set_rows(ctx, v_view, v_cur, kv_idxs);
     }
@@ -1077,8 +1048,10 @@ void llama_kv_cache_unified::set_input_kv_idxs(ggml_tensor * dst, const llama_ub
     int64_t * data = (int64_t *) dst->data;
 
     for (uint32_t s = 0; s < sinfo.n_seq_virt(); ++s) {
+        const int64_t offs = sinfo.seq_id_virt[s]*get_size();
+
         for (uint32_t i = 0; i < sinfo.size(); ++i) {
-            data[s*sinfo.size() + i] = sinfo.idxs[s][i];
+            data[s*sinfo.size() + i] = offs + sinfo.idxs[s][i];
         }
     }
 }