diff --git a/src/models.js b/src/models.js
index 6c0dfb73f..9d5d4d12c 100644
--- a/src/models.js
+++ b/src/models.js
@@ -237,6 +237,7 @@ async function getSession(pretrained_model_name_or_path, fileName, options) {
     const session_config = {
         dtype: selectedDtype,
         kv_cache_dtype,
+        device: selectedDevice,
     }
 
     // Construct the model file name
@@ -417,6 +418,10 @@ function validateInputs(session, inputs) {
     return checkedInputs;
 }
 
+// Currently, Transformers.js doesn't support simultaneous execution of sessions in WASM/WebGPU.
+// For this reason, we need to chain the inference calls (otherwise we get "Error: Session already started").
+let webInferenceChain = Promise.resolve();
+
 /**
  * Executes an InferenceSession using the specified inputs.
  * NOTE: `inputs` must contain at least the input names of the model.
@@ -433,17 +438,28 @@ async function sessionRun(session, inputs) {
     try {
         // pass the original ort tensor
         const ortFeed = Object.fromEntries(Object.entries(checkedInputs).map(([k, v]) => [k, v.ort_tensor]));
-        let output = await session.run(ortFeed);
-        output = replaceTensors(output);
-        return output;
+        const run = () => session.run(ortFeed);
+        const output = await ((apis.IS_BROWSER_ENV || apis.IS_WEBWORKER_ENV)
+            ? (webInferenceChain = webInferenceChain.then(run))
+            : run());
+        return replaceTensors(output);
     } catch (e) {
         // Error messages can be long (nested) and uninformative. For this reason,
         // we apply minor formatting to show the most important information
         const formatted = Object.fromEntries(Object.entries(checkedInputs)
-            .map(([k, { type, dims, data }]) => [k, {
+            .map(([k, tensor]) => {
                 // Extract these properties from the underlying ORT tensor
-                type, dims, data,
-            }]));
+                const unpacked = {
+                    type: tensor.type,
+                    dims: tensor.dims,
+                    location: tensor.location,
+                }
+                if (unpacked.location !== "gpu-buffer") {
+                    // Only return the data if it's not a GPU buffer
+                    unpacked.data = tensor.data;
+                }
+                return [k, unpacked];
+            }));
 
         // This usually occurs when the inputs are of the wrong type.
         console.error(`An error occurred during model execution: "${e}".`);
@@ -5223,7 +5239,7 @@ export class RTDetrV2ForObjectDetection extends RTDetrV2PreTrainedModel {
     }
 }
 
-export class RTDetrV2ObjectDetectionOutput extends RTDetrObjectDetectionOutput {}
+export class RTDetrV2ObjectDetectionOutput extends RTDetrObjectDetectionOutput { }
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
@@ -5238,7 +5254,7 @@ export class RFDetrForObjectDetection extends RFDetrPreTrainedModel {
     }
 }
 
-export class RFDetrObjectDetectionOutput extends RTDetrObjectDetectionOutput {}
+export class RFDetrObjectDetectionOutput extends RTDetrObjectDetectionOutput { }
 //////////////////////////////////////////////////
 
 //////////////////////////////////////////////////
diff --git a/tests/utils/generation.test.js b/tests/utils/generation.test.js
index 2eab931c4..377816ff3 100644
--- a/tests/utils/generation.test.js
+++ b/tests/utils/generation.test.js
@@ -282,6 +282,67 @@ describe("PKV caching", () => {
     }, MAX_MODEL_DISPOSE_TIME);
   });
 
+  describe("LlamaForCausalLM (onnxruntime-genai)", () => {
+    const model_id = "onnx-internal-testing/tiny-random-LlamaForCausalLM-GQA";
+    /** @type {LlamaForCausalLM} */
+    let model;
+    /** @type {LlamaTokenizer} */
+    let tokenizer;
+    beforeAll(async () => {
+      model = await LlamaForCausalLM.from_pretrained(model_id, DEFAULT_MODEL_OPTIONS);
+      tokenizer = await LlamaTokenizer.from_pretrained(model_id);
+    }, MAX_MODEL_LOAD_TIME);
+
+    it(
+      "batch_size=1",
+      async () => {
+        const inputs = tokenizer("1");
+
+        // Generate first sequence w/o PKV
+        // NOTE: `return_dict_in_generate=true` is required to get PKV
+        const { past_key_values, sequences } = await model.generate({
+          ...inputs,
+          max_new_tokens: 5,
+          do_sample: false,
+          return_dict_in_generate: true,
+        });
+
+        // Update output with new text
+        const decoded = tokenizer.batch_decode(sequences, {
+          skip_special_tokens: false,
+        })[0];
+        const new_inputs = tokenizer(decoded + "2", {
+          add_special_tokens: false,
+        });
+
+        // Run w/o PKV
+        const generated_ids = await model.generate({
+          ...new_inputs,
+          max_new_tokens: 3,
+          do_sample: false,
+        });
+
+        // Run w/ PKV
+        const generated_ids_pkv = await model.generate({
+          ...new_inputs,
+          past_key_values,
+          max_new_tokens: 3,
+          do_sample: false,
+        });
+
+        const target = [[128000n, 16n, 34732n, 98805n, 116404n, 68265n, 99392n, 17n, 21855n, 60933n, 14285n]];
+
+        expect(generated_ids.tolist()).toEqual(target);
+        expect(generated_ids_pkv.tolist()).toEqual(target);
+      },
+      MAX_TEST_EXECUTION_TIME,
+    );
+
+    afterAll(async () => {
+      await model?.dispose();
+    }, MAX_MODEL_DISPOSE_TIME);
+  });
+
   describe("LlavaForConditionalGeneration", () => {
     const model_id = "Xenova/tiny-random-LlavaForConditionalGeneration";
     /** @type {LlavaForConditionalGeneration} */