diff --git a/src/llama.cpp b/src/llama.cpp
index 2bdbf2a0..f9a59c79 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -2685,7 +2685,6 @@ struct llama_kv_cache {
     std::vector<struct ggml_tensor *> v_l;
 
     // DeepSeek MLA
-    std::vector<struct ggml_tensor *> kr_l; // per layer
     std::vector<struct ggml_tensor *> kv_l;
     std::vector<struct ggml_tensor *> kvt_l;
 
@@ -3166,7 +3165,6 @@ static bool llama_kv_cache_init(
     cache.v_l.reserve(n_layer);
 
     // DeepSeek MLA
-    cache.kr_l.reserve(n_layer);
     cache.kv_l.reserve(n_layer);
     cache.kvt_l.reserve(n_layer);
 
@@ -3179,18 +3177,13 @@ static bool llama_kv_cache_init(
         ggml_tensor * v;
         if (cparams.mla_attn && model.layers[i].wk_b && model.layers[i].wv_b) {
             // DeepSeek MLA
-            //k = ggml_new_tensor_1d(ctx, type_k, 1);
-            //v = ggml_new_tensor_1d(ctx, type_v, 1);
             const uint32_t n_embd_head_qk_rope = hparams.n_rot;
             const uint32_t kv_lora_rank = hparams.n_lora_kv;
             LLAMA_LOG_INFO("%s: layer %d: n_embd_head_qk_rope = %d, kv_lora_rank = %d\n", __func__, i, n_embd_head_qk_rope, kv_lora_rank);
-            ggml_tensor * kr = ggml_new_tensor_1d(ctx, cache.type_kr, n_embd_head_qk_rope*kv_size);
-            ggml_tensor * kv = ggml_new_tensor_1d(ctx, cache.type_kv, kv_lora_rank*kv_size);
+            ggml_tensor * kv = ggml_new_tensor_1d(ctx, cache.type_kv, (kv_lora_rank + n_embd_head_qk_rope)*kv_size);
             ggml_tensor * kvt = ggml_new_tensor_1d(ctx, cache.type_kv, kv_lora_rank*kv_size);
-            ggml_format_name(kr, "cache_kr_l%d", i);
             ggml_format_name(kv, "cache_kv_l%d", i);
             ggml_format_name(kvt, "cache_kvt_l%d", i);
-            cache.kr_l.push_back(kr);
             cache.kv_l.push_back(kv);
             cache.kvt_l.push_back(kvt);
         }
@@ -13457,7 +13450,6 @@ struct llm_build_context {
                         0);
                 cb(kv_compressed, "kv_compressed", il);
 
-                //kv_compressed = ggml_cont(ctx0, kv_compressed); // TODO: the CUDA backend does not support non-contiguous norm
                 kv_compressed = llm_build_norm(ctx0, kv_compressed, hparams,
                         model.layers[il].attn_kv_a_norm, NULL,
                         LLM_NORM_RMS, cb, il);
@@ -13465,13 +13457,6 @@ struct llm_build_context {
 
                 if (lctx.cparams.mla_attn && model.layers[il].wk_b && model.layers[il].wv_b) {
 
-                    struct ggml_tensor * kv_cache_view = ggml_view_1d(ctx0, kv_self.kv_l[il], n_tokens*kv_lora_rank,
-                            ggml_row_size(kv_self.kv_l[il]->type, kv_lora_rank)*kv_head);
-                    cb(kv_cache_view, "kv_cache_view", il);
-
-                    // note: storing c^KV in the KV cache
-                    ggml_build_forward_expand(gf, ggml_cpy(ctx0, kv_compressed, kv_cache_view));
-
                     struct ggml_tensor * kv_cache_trans_view = ggml_view_2d(ctx0, kv_self.kvt_l[il], n_tokens, kv_lora_rank,
                             ggml_row_size(kv_self.kv_l[il]->type, kv_self.size), ggml_row_size(kv_self.kv_l[il]->type, kv_head));
                     cb(kv_cache_trans_view, "kv_cache_trans_view", il);
@@ -13479,13 +13464,6 @@ struct llm_build_context {
                     // note: storing transposed c^KV in the transposed KV cache
                     ggml_build_forward_expand(gf, ggml_cpy(ctx0, ggml_transpose(ctx0, kv_compressed), kv_cache_trans_view));
 
-                    struct ggml_tensor * kv_cache =
-                        ggml_view_2d(ctx0, kv_self.kv_l[il],
-                                kv_lora_rank, n_kv,
-                                ggml_row_size(kv_self.kv_l[il]->type, kv_lora_rank),
-                                0);
-                    cb(kv_cache, "kv_cache", il);
-
                     struct ggml_tensor * kv_cache_trans =
                         ggml_view_2d(ctx0, kv_self.kvt_l[il],
                                 n_kv, kv_lora_rank,
@@ -13493,19 +13471,16 @@ struct llm_build_context {
                                 0);
                     cb(kv_cache_trans, "kv_cache_trans", il);
 
-                    struct ggml_tensor * kr_cache_view = ggml_view_1d(ctx0, kv_self.kr_l[il], n_tokens*n_embd_head_qk_rope,
-                            ggml_row_size(kv_self.kr_l[il]->type, n_embd_head_qk_rope)*kv_head);
-                    cb(kr_cache_view, "kr_cache_view", il);
+                    ggml_tensor * kvr = ggml_concat(ctx0, kv_compressed, ggml_permute(ctx0, k_rope, 0, 2, 1, 3), 0);
+                    cb(kvr, "kvr", il);
 
-                    // note: storing RoPE-ed version of K^R in the KV cache
-                    ggml_build_forward_expand(gf, ggml_cpy(ctx0, k_rope, kr_cache_view));
-
-                    struct ggml_tensor * kr_cache =
-                        ggml_view_2d(ctx0, kv_self.kr_l[il],
-                                n_embd_head_qk_rope, n_kv,
-                                ggml_row_size(kv_self.kr_l[il]->type, n_embd_head_qk_rope),
-                                0);
-                    cb(kr_cache, "kr_cache", il);
+                    ggml_tensor * kv_cache_view = ggml_view_1d(ctx0, kv_self.kv_l[il], n_tokens*(kv_lora_rank + n_embd_head_qk_rope),
+                            ggml_row_size(kv_self.kv_l[il]->type, kv_lora_rank + n_embd_head_qk_rope)*kv_head);
+                    ggml_build_forward_expand(gf, ggml_cpy(ctx0, kvr, kv_cache_view));
+                    ggml_tensor * kv_cache = ggml_view_2d(ctx0, kv_self.kv_l[il],
+                            kv_lora_rank + n_embd_head_qk_rope, n_kv,
+                            ggml_row_size(kv_self.kv_l[il]->type, kv_lora_rank + n_embd_head_qk_rope), 0);
+                    cb(kv_cache, "kv_cache", il);
 
                     struct ggml_tensor * wk_b = ggml_view_3d(ctx0, model.layers[il].wk_b, n_embd_head_qk_nope, kv_lora_rank, n_head,
                             ggml_row_size(model.layers[il].wk_b->type, n_embd_head_qk_nope),
@@ -13518,33 +13493,20 @@ struct llm_build_context {
                     struct ggml_tensor * q_nope2 = ggml_mul_mat(ctx0, wk_b, q_nope);
                     cb(q_nope2, "q_nope2", il);
 
+                    ggml_tensor * q = ggml_concat(ctx0, q_nope2, ggml_permute(ctx0, q_rope, 0, 2, 1, 3), 0);
+                    cb(q, "q", il);
                     if (!pp_opt) {
-                        q_nope2 = ggml_permute(ctx0, q_nope2, 0, 2, 1, 3);
-                        cb(q_nope2, "q_nope2_perm", il);
+                        q = ggml_permute(ctx0, q, 0, 2, 1, 3);
+                        cb(q, "q_perm", il);
                     }
-                    struct ggml_tensor * kq_nope = ggml_mul_mat(ctx0, kv_cache, q_nope2);
-                    cb(kq_nope, "kq_nope", il);
-
-                    if (!pp_opt) {
-                        kq_nope = ggml_permute(ctx0, kq_nope, 0, 2, 1, 3);
-                        cb(kq_nope, "kq_nope_perm", il);
-                    }
-
-                    if (pp_opt) {
-                        q_rope = ggml_permute(ctx0, q_rope, 0, 2, 1, 3);
-                        cb(q_rope, "q_rope_perm", il);
-                    }
-                    struct ggml_tensor * kq_rope = ggml_mul_mat(ctx0, kr_cache, q_rope);
-                    cb(kq_rope, "kq_rope", il);
-
-                    if (!pp_opt) {
-                        kq_rope = ggml_permute(ctx0, kq_rope, 0, 2, 1, 3);
-                        cb(kq_rope, "kq_rope_perm", il);
-                    }
-
-                    struct ggml_tensor * kq = ggml_add(ctx0, kq_nope, kq_rope);
+                    ggml_tensor * kq = ggml_mul_mat(ctx0, kv_cache, q);
                     cb(kq, "kq", il);
 
+		            if (!pp_opt) {
+                        kq = ggml_cont(ctx0, ggml_permute(ctx0, kq, 0, 2, 1, 3));
+                        cb(kq, "kq_perm", il);
+                    }
+
                     kq = ggml_soft_max_ext(ctx0, kq, KQ_mask, kq_scale, hparams.f_max_alibi_bias);
                     cb(kq, "kq_soft_max_ext", il);
 
@@ -13561,7 +13523,9 @@ struct llm_build_context {
                         cb(kqv_compressed, "kqv_compressed_perm", il);
                     }
 
-                    struct ggml_tensor * wv_b = ggml_view_3d(ctx0, model.layers[il].wv_b, kv_lora_rank, n_embd_head_v, n_head, ggml_row_size(model.layers[il].wv_b->type, kv_lora_rank), ggml_row_size(model.layers[il].wv_b->type, kv_lora_rank * n_embd_head_v), 0);
+                    struct ggml_tensor * wv_b = ggml_view_3d(ctx0, model.layers[il].wv_b, kv_lora_rank, n_embd_head_v, n_head,
+                            ggml_row_size(model.layers[il].wv_b->type, kv_lora_rank),
+                            ggml_row_size(model.layers[il].wv_b->type, kv_lora_rank)*n_embd_head_v, 0);
                     cb(wv_b, "wv_b", il);
 
                     struct ggml_tensor * kqv = ggml_mul_mat(ctx0, wv_b, kqv_compressed);
@@ -18033,14 +17997,9 @@ struct llama_context * llama_new_context_with_model(
         }
 
 	    {
-            size_t memory_size_kr = 0;
             size_t memory_size_kv = 0;
             size_t memory_size_kvt = 0;
 
-            for (auto & kr : ctx->kv_self.kr_l) {
-                memory_size_kr += ggml_nbytes(kr);
-            }
-
             for (auto & kv : ctx->kv_self.kv_l) {
                 memory_size_kv += ggml_nbytes(kv);
             }
@@ -18049,10 +18008,9 @@ struct llama_context * llama_new_context_with_model(
                 memory_size_kvt += ggml_nbytes(kvt);
             }
 
-            if (memory_size_kr + memory_size_kv + memory_size_kvt > 0) {
-                LLAMA_LOG_INFO("%s: KV self size  = %7.2f MiB, K^R (%s): %7.2f MiB, c^KV (%s): %7.2f MiB, kv^T (%s): %7.2f MiB\n", __func__,
-                        (float)(memory_size_kr + memory_size_kv + memory_size_kvt) / (1024.0f * 1024.0f),
-                        ggml_type_name(type_k), (float)memory_size_kr / (1024.0f * 1024.0f),
+            if (memory_size_kv + memory_size_kvt > 0) {
+                LLAMA_LOG_INFO("%s: KV self size  = %7.2f MiB, c^KV (%s): %7.2f MiB, kv^T (%s): %7.2f MiB\n", __func__,
+                        (float)(memory_size_kv + memory_size_kvt) / (1024.0f * 1024.0f),
                         ggml_type_name(type_v), (float)memory_size_kv / (1024.0f * 1024.0f),
                         ggml_type_name(type_v), (float)memory_size_kvt / (1024.0f * 1024.0f));
             }