diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
index a70b69c5a..e87254269 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -446,6 +446,14 @@ class TensorNameMap:
             "model.layers.{bid}.self_attn.kv_b_proj", # deepseek2
         ),
 
+        MODEL_TENSOR.ATTN_K_B: (
+            "model.layers.{bid}.self_attn.k_b_proj",  # deepseek2
+        ),
+
+        MODEL_TENSOR.ATTN_V_B: (
+            "model.layers.{bid}.self_attn.v_b_proj",  # deepseek2
+        ),
+
         MODEL_TENSOR.ATTN_Q_A_NORM: (
             "model.layers.{bid}.self_attn.q_a_layernorm", # deepseek2
         ),
diff --git a/src/llama.cpp b/src/llama.cpp
index 67a80eb2f..17d257339 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -3173,8 +3173,17 @@ static bool llama_kv_cache_init(
         const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i) + hparams.n_embd_v_s();
 
         struct ggml_context * ctx = offload ? ctx_map.at(model.buft_layer[i].buft) : cache.ctxs.front();
-        ggml_tensor * k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*kv_size);
-        ggml_tensor * v = ggml_new_tensor_1d(ctx, type_v, n_embd_v_gqa*kv_size);
+        ggml_tensor * k;
+        ggml_tensor * v;
+        if (cparams.mla_attn && model.layers[i].wk_b && model.layers[i].wv_b) {
+               k = ggml_new_tensor_1d(ctx, type_k, 1);
+               v = ggml_new_tensor_1d(ctx, type_v, 1);
+        }
+        else {
+               k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*kv_size);
+               v = ggml_new_tensor_1d(ctx, type_v, n_embd_v_gqa*kv_size);
+       }
+
         ggml_format_name(k, "cache_k_l%d", i);
         ggml_format_name(v, "cache_v_l%d", i);
         cache.k_l.push_back(k);
@@ -13368,6 +13377,10 @@ struct llm_build_context {
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
         struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
+        // whether to use n_tokens as the matrix dimension during multiplication or n_head
+        // n_tokens is higher during prompt processing, this allows to optimize for this case
+        bool pp_opt = n_tokens > n_head;
+
         for (int il = 0; il < n_layer; ++il) {
             struct ggml_tensor * inpSA = inpL;
 
@@ -13496,43 +13509,54 @@ struct llm_build_context {
                     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), ggml_row_size(model.layers[il].wk_b->type, kv_lora_rank * n_embd_head_qk_nope), 0);
                     cb(wk_b, "wk_b", il);
 
-                    struct ggml_tensor * q_nope_perm = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);
-                    cb(q_nope_perm, "q_nope_perm", il);
+                    q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);
+                    cb(q_nope, "q_nope_perm", il);
 
-                    struct ggml_tensor * q_nope2 = ggml_mul_mat(ctx0, wk_b, q_nope_perm);
+                    struct ggml_tensor * q_nope2 = ggml_mul_mat(ctx0, wk_b, q_nope);
                     cb(q_nope2, "q_nope2", il);
 
-                    struct ggml_tensor * q_nope2_perm = ggml_permute(ctx0, q_nope2, 0, 2, 1, 3);
-                    cb(q_nope2_perm, "q_nope2_perm", il);
-
-                    struct ggml_tensor * kq_nope = ggml_mul_mat(ctx0, kv_cache, q_nope2_perm);
+                    if (!pp_opt) {
+                        q_nope2 = ggml_permute(ctx0, q_nope2, 0, 2, 1, 3);
+                        cb(q_nope2, "q_nope2_perm", il);
+                    }
+                    struct ggml_tensor * kq_nope = ggml_mul_mat(ctx0, kv_cache, q_nope2);
                     cb(kq_nope, "kq_nope", il);
 
-                    // Huh? This is not used anywhere
-                    //struct ggml_tensor * q_pe_perm = ggml_permute(ctx0, q_pe, 0, 3, 2, 1);
-                    //cb(q_pe_perm, "q_pe_perm", 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_pe = ggml_permute(ctx0, q_pe, 0, 2, 1, 3);
+                        cb(q_pe, "q_pe_perm", il);
+                    }
                     struct ggml_tensor * kq_pe = ggml_mul_mat(ctx0, kr_cache, q_pe);
                     cb(kq_pe, "kq_pe", il);
 
+                    if (!pp_opt) {
+                        kq_pe = ggml_permute(ctx0, kq_pe, 0, 2, 1, 3);
+                        cb(kq_pe, "kq_pe_perm", il);
+                    }
+
                     struct ggml_tensor * kq = ggml_add(ctx0, kq_nope, kq_pe);
                     cb(kq, "kq", il);
 
-                    // We need this copy because soft_max expects a contiguous tensor
-                    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);
 
-                    struct ggml_tensor * kq_perm = ggml_permute(ctx0, kq, 0, 2, 1, 3);
-                    cb(kq_perm, "kq_soft_max_ext_perm", il);
+		    if (!pp_opt) {
+                        kq = ggml_permute(ctx0, kq, 0, 2, 1, 3);
+                        cb(kq, "kq_soft_max_ext_perm", il);
+                    }
 
-                    struct ggml_tensor * kqv_compressed = ggml_mul_mat(ctx0, kv_cache_trans, kq_perm);
+                    struct ggml_tensor * kqv_compressed = ggml_mul_mat(ctx0, kv_cache_trans, kq);
                     cb(kqv_compressed, "kqv_compressed", il);
 
-                    kqv_compressed = ggml_permute(ctx0, kqv_compressed, 0, 2, 1, 3);
-                    cb(kqv_compressed, "kqv_compressed_perm", il);
+                    if (!pp_opt) {
+                        kqv_compressed = ggml_permute(ctx0, kqv_compressed, 0, 2, 1, 3);
+                        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);
                     cb(wv_b, "wv_b", il);