diff --git a/tests/test-sampling.cpp b/tests/test-sampling.cpp
index 7cd96c5cd3..1a04ac5b11 100644
--- a/tests/test-sampling.cpp
+++ b/tests/test-sampling.cpp
@@ -192,6 +192,73 @@ static void test_top_n_sigma(const std::vector<float> & probs, const std::vector
     tester.check();
 }
 
+static void test_dist_rng(uint32_t seed, bool blue_noise, enum llama_rng_type rng_type,
+                          const std::vector<llama_token> & expected, const char * desc) {
+    const int n_vocab   = 16;
+    const int n_samples = 32;
+
+    // fixed non-uniform distribution: token i has logit log(i+1)
+    std::vector<llama_token_data> data(n_vocab);
+    for (int i = 0; i < n_vocab; i++) {
+        data[i] = {i, logf((float)(i + 1)), 0.0f};
+    }
+
+    auto * sampler = llama_sampler_init_dist_rng(seed, blue_noise, rng_type);
+    std::vector<llama_token> tokens(n_samples);
+
+    for (int i = 0; i < n_samples; i++) {
+        std::vector<llama_token_data> cur(data);
+        llama_token_data_array cur_p = {cur.data(), cur.size(), -1, false};
+        llama_sampler_apply(sampler, &cur_p);
+        GGML_ASSERT(cur_p.selected >= 0 && cur_p.selected < (llama_token)n_vocab);
+        tokens[i] = cur_p.data[cur_p.selected].id;
+    }
+
+    if (expected.empty()) {
+        // print sequence for capture
+        printf("test_dist_rng %s: {", desc);
+        for (int i = 0; i < n_samples; i++) {
+            printf("%s%d", i ? ", " : "", tokens[i]);
+        }
+        printf("}\n");
+    } else {
+        // verify against known sequence
+        GGML_ASSERT((int)expected.size() == n_samples);
+        bool match = true;
+        for (int i = 0; i < n_samples; i++) {
+            if (tokens[i] != expected[i]) {
+                match = false;
+                break;
+            }
+        }
+        if (!match) {
+            printf("test_dist_rng %s: MISMATCH\n  got:      {", desc);
+            for (int i = 0; i < n_samples; i++) {
+                printf("%s%d", i ? ", " : "", tokens[i]);
+            }
+            printf("}\n  expected: {");
+            for (int i = 0; i < n_samples; i++) {
+                printf("%s%d", i ? ", " : "", expected[i]);
+            }
+            printf("}\n");
+            GGML_ASSERT(false);
+        }
+
+        // also verify reset reproduces same sequence
+        llama_sampler_reset(sampler);
+        for (int i = 0; i < n_samples; i++) {
+            std::vector<llama_token_data> cur(data);
+            llama_token_data_array cur_p = {cur.data(), cur.size(), -1, false};
+            llama_sampler_apply(sampler, &cur_p);
+            GGML_ASSERT(cur_p.data[cur_p.selected].id == tokens[i]);
+        }
+
+        printf("test_dist_rng %-30s OK\n", desc);
+    }
+
+    llama_sampler_free(sampler);
+}
+
 static void test_sampler_queue(const size_t n_vocab, const std::string & samplers_sequence, const int top_k, const float top_p, const float min_p
 ) {
     sampler_tester tester(n_vocab);
@@ -392,6 +459,13 @@ int main(void) {
     test_sampler_queue(10000, "mkp", 100, 0.8f, 0.1f);
     test_sampler_queue(10000, "mpk", 100, 0.8f, 0.1f);
 
+    test_dist_rng(42, false, LLAMA_RNG_TYPE_LOWBIAS32,
+        {5, 12, 8, 10, 12, 11, 10, 8, 8, 10, 11, 9, 7, 6, 11, 13, 14, 15, 13, 4, 12, 14, 13, 13, 14, 12, 5, 15, 4, 13, 15, 12},
+        "lowbias32");
+    test_dist_rng(42, true,  LLAMA_RNG_TYPE_LOWBIAS32,
+        {10, 5, 12, 8, 15, 13, 3, 10, 13, 12, 2, 15, 8, 14, 5, 11, 7, 9, 15, 11, 8, 2, 12, 14, 7, 9, 13, 10, 14, 5, 12, 15},
+        "lowbias32 + blue noise");
+
     printf("OK\n");
 
     test_perf();