diff --git a/tests/test-delta-chunk.cpp b/tests/test-delta-chunk.cpp
index 9c18bc49..2c56ecea 100644
--- a/tests/test-delta-chunk.cpp
+++ b/tests/test-delta-chunk.cpp
@@ -248,36 +248,36 @@ static bool run_ggml_op_cuda(const Case & c, const Tensors & t, Result & r, std:
     const int hd = c.hd;
     const size_t out_size = (size_t)hd * c.nt * c.hv * c.nseq;
     const size_t st_size = (size_t)hd * hd * c.hv * c.nseq;
+    // no-alloc context: ggml_backend_alloc_ctx_tensors assigns the buffers.
+    // (Pre-allocated tensors trip GGML_ASSERT(ggml_get_no_alloc(ctx)).)
     const size_t mem = (out_size + st_size) * 4
         + (t.q.size() + t.k.size() + t.v.size() + t.g.size() + t.beta.size() + t.state.size()) * 4
         + 64 * 1024 * 1024;
-    struct ggml_init_params ip = { mem, nullptr, false };
+    struct ggml_init_params ip = { mem, nullptr, true };
     struct ggml_context * ctx = ggml_init(ip);
     if (!ctx) { err = "ggml_init failed"; ggml_backend_free(be); return false; }
 
-    auto mkc = [&](const char * name, int64_t n0, int64_t n1, int64_t n2, int64_t n3, const void * data, size_t nbytes) {
+    auto mkc = [&](const char * name, int64_t n0, int64_t n1, int64_t n2, int64_t n3) {
         struct ggml_tensor * ten = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, n0, n1, n2, n3);
         ggml_set_name(ten, name);
-        memcpy(ten->data, data, nbytes);
         return ten;
     };
-    struct ggml_tensor * q = mkc("q", hd, c.nt, c.hk, c.nseq, t.q.data(), t.q.size() * 4);
-    struct ggml_tensor * k = mkc("k", hd, c.nt, c.hk, c.nseq, t.k.data(), t.k.size() * 4);
-    struct ggml_tensor * v = mkc("v", hd, c.nt, c.hv, c.nseq, t.v.data(), t.v.size() * 4);
+    struct ggml_tensor * q = mkc("q", hd, c.nt, c.hk, c.nseq);
+    struct ggml_tensor * k = mkc("k", hd, c.nt, c.hk, c.nseq);
+    struct ggml_tensor * v = mkc("v", hd, c.nt, c.hv, c.nseq);
     // canonical [b,t,h] flat == contiguous base [hv,t,s] flat: permute to the
     // exact view topology production uses ([nt,1,hv,s] / [1,nt,hv,s]).
-    struct ggml_tensor * gbase = mkc("gbase", c.hv, c.nt, c.nseq, 1, t.g.data(), t.g.size() * 4);
+    struct ggml_tensor * gbase = mkc("gbase", c.hv, c.nt, c.nseq, 1);
     struct ggml_tensor * g = ggml_permute(ctx, gbase, 2, 0, 3, 1);
     ggml_set_name(g, "g");
-    struct ggml_tensor * bbase = mkc("bbase", c.hv, 1, c.nt, c.nseq, t.beta.data(), t.beta.size() * 4);
+    struct ggml_tensor * bbase = mkc("bbase", c.hv, 1, c.nt, c.nseq);
     struct ggml_tensor * beta = ggml_permute(ctx, bbase, 2, 0, 1, 3);
     ggml_set_name(beta, "beta");
-    struct ggml_tensor * st = mkc("st", hd, hd * c.hv, 1, c.nseq, t.state.data(), t.state.size() * 4);
+    struct ggml_tensor * st = mkc("st", hd, hd * c.hv, 1, c.nseq);
     struct ggml_tensor * saved = nullptr;
     if (c.saved && c.nt > 1) {
         saved = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, (int64_t)(c.nt - 1) * st_size);
         ggml_set_name(saved, "saved");
-        memset(saved->data, 0, ggml_nbytes(saved));
     }
     struct ggml_tensor * res = ggml_delta_net(ctx, q, k, v, g, beta, st, saved);
     res->op_params[0] = c.repeat;
@@ -286,6 +286,12 @@ static bool run_ggml_op_cuda(const Case & c, const Tensors & t, Result & r, std:
     {
         ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors(ctx, be);
         if (!buf) { err = "CUDA alloc failed"; goto fail; }
+        ggml_backend_tensor_set(q, t.q.data(), 0, t.q.size() * 4);
+        ggml_backend_tensor_set(k, t.k.data(), 0, t.k.size() * 4);
+        ggml_backend_tensor_set(v, t.v.data(), 0, t.v.size() * 4);
+        ggml_backend_tensor_set(gbase, t.g.data(), 0, t.g.size() * 4);
+        ggml_backend_tensor_set(bbase, t.beta.data(), 0, t.beta.size() * 4);
+        ggml_backend_tensor_set(st, t.state.data(), 0, t.state.size() * 4);
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx, 16, false);
         ggml_build_forward_expand(gf, res);
         ggml_backend_graph_compute(be, gf);