diff --git a/dist/jni/cp_inference.cpp b/dist/jni/cp_inference.cpp
index 2cd435d..371d778 100644
--- a/dist/jni/cp_inference.cpp
+++ b/dist/jni/cp_inference.cpp
@@ -31,16 +31,33 @@ static std::vector<float> read_floats(const char* path, size_t expect) {
     fclose(f); return v;
 }
 
+// Garde-t-on le tenseur en F16 ou on convertit en F32 ?
+// Rule : les poids matmul (attn_q/k/v/output, ffn_gate/up/down) restent F16 -> moitié BW
+// dans les gros mul_mat (~85% du coût CP). Les norms restent en F32 car ggml_mul exige
+// même type des deux opérandes (xn_f32 * w_norm_f32). Économie pratique : embeds ~1024
+// éléments * 5 layers * 6 norms = 30 KB de norms, négligeable. Les matmul valent
+// ~5 * (3*1024*1024 attn + 3*1024*MLP ffn) qui dominent.
+static bool cp_keep_f16(const std::string& name) {
+    // Tout ce qui n'est pas "_norm.weight" peut rester F16. Sécurité : on n'autorise
+    // que les noms connus du CP qwen3 (5L). Les autres tombent en F32 par défaut.
+    if (name.find("_norm.weight") != std::string::npos) return false;
+    return true;
+}
+
 bool cp_load(CPState& s, const char* gguf_path, const char* heads_path, const char* embs_path) {
     ggml_context* meta = nullptr;
     gguf_init_params p; p.no_alloc = true; p.ctx = &meta;
     gguf_context* g = gguf_init_from_file(gguf_path, p);
     if (!g) { fprintf(stderr, "cp_load: gguf open fail %s\n", gguf_path); return false; }
     int64_t n = gguf_get_n_tensors(g);
+
+    // Budget : on alloue à la taille native (F16 reste F16 si autorisé, sinon F32). Borne
+    // sup = somme des max(nb_f32, nb_native) pour être tranquille.
     size_t bytes = 0;
     for (int64_t i = 0; i < n; i++) {
         ggml_tensor* mt = ggml_get_tensor(meta, gguf_get_tensor_name(g, i));
-        bytes += ggml_nelements(mt) * sizeof(float);
+        size_t nb_f32 = (size_t)ggml_nelements(mt) * sizeof(float);
+        bytes += nb_f32; // sup
     }
     ggml_init_params ip = { bytes + (size_t)n * ggml_tensor_overhead() + (1u << 20), nullptr, false };
     s.weights_ctx = ggml_init(ip);
@@ -48,19 +65,36 @@ bool cp_load(CPState& s, const char* gguf_path, const char* heads_path, const ch
     FILE* f = fopen(gguf_path, "rb");
     const size_t off = gguf_get_data_offset(g);
     std::vector<uint8_t> tmp;
+    size_t kept_f16 = 0, conv_f32 = 0;
     for (int64_t i = 0; i < n; i++) {
         const char* name = gguf_get_tensor_name(g, i);
         ggml_tensor* mt = ggml_get_tensor(meta, name);
-        ggml_tensor* t32 = ggml_new_tensor(s.weights_ctx, GGML_TYPE_F32, ggml_n_dims(mt), mt->ne);
-        size_t nb = ggml_nbytes(mt);
+        size_t nb_src = ggml_nbytes(mt);
         int64_t ne = ggml_nelements(mt);
-        tmp.resize(nb);
+        tmp.resize(nb_src);
         fseek(f, off + gguf_get_tensor_offset(g, i), SEEK_SET);
-        if (fread(tmp.data(), 1, nb, f) != nb) { fprintf(stderr, "cp_load read fail %s\n", name); fclose(f); return false; }
-        if (mt->type == GGML_TYPE_F32)      memcpy(t32->data, tmp.data(), nb);
-        else if (mt->type == GGML_TYPE_F16) ggml_fp16_to_fp32_row((const ggml_fp16_t*)tmp.data(), (float*)t32->data, ne);
-        else { fprintf(stderr, "cp_load unsupported type %s for %s\n", ggml_type_name(mt->type), name); fclose(f); return false; }
-        s.tensors[name] = t32;
+        if (fread(tmp.data(), 1, nb_src, f) != nb_src) { fprintf(stderr, "cp_load read fail %s\n", name); fclose(f); return false; }
+
+        ggml_type dst_type = GGML_TYPE_F32;
+        if (mt->type == GGML_TYPE_F16 && cp_keep_f16(name)) dst_type = GGML_TYPE_F16;
+        else if (mt->type == GGML_TYPE_F32)                  dst_type = GGML_TYPE_F32;
+        else                                                  dst_type = GGML_TYPE_F32;  // F16 -> F32 pour les norms
+
+        ggml_tensor* t = ggml_new_tensor(s.weights_ctx, dst_type, ggml_n_dims(mt), mt->ne);
+        if (dst_type == mt->type) {
+            memcpy(t->data, tmp.data(), nb_src);
+            if (dst_type == GGML_TYPE_F16) kept_f16++;
+        } else if (mt->type == GGML_TYPE_F16 && dst_type == GGML_TYPE_F32) {
+            ggml_fp16_to_fp32_row((const ggml_fp16_t*)tmp.data(), (float*)t->data, ne);
+            conv_f32++;
+        } else if (mt->type == GGML_TYPE_F32 && dst_type == GGML_TYPE_F32) {
+            memcpy(t->data, tmp.data(), nb_src);
+        } else {
+            fprintf(stderr, "cp_load unsupported cast %s -> %s for %s\n",
+                    ggml_type_name(mt->type), ggml_type_name(dst_type), name);
+            fclose(f); return false;
+        }
+        s.tensors[name] = t;
     }
     fclose(f);
     gguf_free(g);
@@ -68,8 +102,9 @@ bool cp_load(CPState& s, const char* gguf_path, const char* heads_path, const ch
     const size_t TAB = (size_t)N_CB * N_VOCAB * N_EMBD;
     s.heads      = read_floats(heads_path, TAB); if (s.heads.empty()) return false;
     s.codec_embs = read_floats(embs_path,  TAB); if (s.codec_embs.empty()) return false;
-    fprintf(stderr, "cp_load: %lld tensors + heads(%.0f MB) + codec_embs(%.0f MB) OK\n",
-            (long long)n, TAB * 4 / 1048576.0, TAB * 4 / 1048576.0);
+    fprintf(stderr, "cp_load: %lld tensors (f16 kept=%zu, f32=%zu) + heads(%.0f MB) + codec_embs(%.0f MB) OK\n",
+            (long long)n, kept_f16, conv_f32 + (size_t)(n - (int64_t)(kept_f16 + conv_f32)),
+            TAB * 4 / 1048576.0, TAB * 4 / 1048576.0);
     return true;
 }