diff --git a/examples/batched/README.md b/examples/batched/README.md
index 6013aab01f..5f2c59e1d6 100644
--- a/examples/batched/README.md
+++ b/examples/batched/README.md
@@ -42,3 +42,15 @@ llama_print_timings: prompt eval time =  4089.11 ms /   118 tokens (   34.65 ms
 llama_print_timings:        eval time =     0.00 ms /     1 runs   (    0.00 ms per token,      inf tokens per second)
 llama_print_timings:       total time =  4156.04 ms
 ```
+
+### Using backend samplers
+It is possible to run this example using backend samplers so that sampling is
+performed on the backend device, like a GPU.
+```bash
+./llama-batched \
+    -m models/Qwen2.5-VL-3B-Instruct-Q8_0.gguf -p "Hello my name is" \
+    -np 4 -kvu \
+    --backend_sampling --top-k 80 --backend_dist
+```
+The `--verbose` flag can be added to see more detailed output and also show
+that the backend samplers are being used.
diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp
index 1a5de5928a..e9d1fc95c2 100644
--- a/examples/batched/batched.cpp
+++ b/examples/batched/batched.cpp
@@ -2,6 +2,7 @@
 #include "common.h"
 #include "log.h"
 #include "llama.h"
+#include "sampling.h"
 
 #include <algorithm>
 #include <cstdio>
@@ -64,6 +65,18 @@ int main(int argc, char ** argv) {
     ctx_params.n_ctx   = n_kv_req;
     ctx_params.n_batch = std::max(n_predict, n_parallel);
 
+    std::vector<llama_sampler_seq_config> sampler_configs(n_parallel);
+    if (params.sampling.backend_sampling) {
+        for (int32_t i = 0; i < n_parallel; ++i) {
+            llama_sampler * backend_sampler = common_sampler_backend_init(model, params.sampling);
+            if (backend_sampler) {
+                sampler_configs[i] = { i, backend_sampler };
+            }
+        }
+        ctx_params.samplers   = sampler_configs.data();
+        ctx_params.n_samplers = n_parallel;
+    }
+
     llama_context * ctx = llama_init_from_model(model, ctx_params);
 
     auto sparams = llama_sampler_chain_default_params();