diff --git a/extension/llm/runner/test/test_text_llm_runner.cpp b/extension/llm/runner/test/test_text_llm_runner.cpp
index e7dbb762dd9..7e9468a5f01 100644
--- a/extension/llm/runner/test/test_text_llm_runner.cpp
+++ b/extension/llm/runner/test/test_text_llm_runner.cpp
@@ -497,4 +497,56 @@ TEST_F(RunnerTest, GenerateEmptyWithoutPrefillFails) {
   EXPECT_EQ(err, Error::InvalidState);
 }
 
+// Test that TextTokenGenerator works correctly in non-kv-cache mode.
+// Exercises the code path fixed by reserving capacity before from_blob:
+// without reserve(), vector reallocation would invalidate the data pointer.
+TEST_F(RunnerTest, NonKvCacheGenerateCompletesSuccessfully) {
+  auto tokenizer = createMockTokenizer();
+  auto text_decoder_runner = createMockTextDecoderRunner();
+
+  // In non-kv-cache mode, the input tensor should grow by 1 token each step.
+  // Verify data is readable each time (catches dangling pointers under ASan).
+  int step_count = 0;
+  ON_CALL(*text_decoder_runner, step)
+      .WillByDefault(
+          [&](executorch::extension::TensorPtr& tokens_tensor, int64_t) {
+            // Initial tokens = 4 (prompt 1,2,3 + prefill token 4).
+            // Each step appends one token before the next call.
+            int64_t expected_size = 4 + step_count;
+            EXPECT_EQ(tokens_tensor->size(1), expected_size);
+
+            // Read data to verify the pointer is still valid.
+            auto* data = tokens_tensor->const_data_ptr<int64_t>();
+            EXPECT_EQ(data[0], 1); // first prompt token
+            EXPECT_EQ(data[1], 2);
+            EXPECT_EQ(data[2], 3);
+            EXPECT_EQ(data[3], 4); // prefill token
+
+            step_count++;
+            return Result<executorch::aten::Tensor>(tensor);
+          });
+
+  Stats stats;
+  auto eos_ids = std::make_unique<std::unordered_set<uint64_t>>(
+      std::unordered_set<uint64_t>{100});
+  TextTokenGenerator generator(
+      tokenizer.get(),
+      text_decoder_runner.get(),
+      false, // use_kv_cache = false
+      std::move(eos_ids),
+      &stats);
+
+  // 4 tokens: prompt (1,2,3) + prefill token (4)
+  std::vector<uint64_t> tokens = {1, 2, 3, 4};
+  // Generate enough tokens that the vector would reallocate without reserve.
+  int32_t max_new_tokens = 20;
+
+  auto result = generator.generate(
+      tokens, 4, max_new_tokens, 0.0f, [](const std::string&) {});
+
+  EXPECT_TRUE(result.ok());
+  EXPECT_EQ(result.get(), max_new_tokens);
+  EXPECT_EQ(step_count, max_new_tokens);
+}
+
 } // namespace
diff --git a/extension/llm/runner/text_token_generator.h b/extension/llm/runner/text_token_generator.h
index 128de05d1d9..4bbc91a01c7 100644
--- a/extension/llm/runner/text_token_generator.h
+++ b/extension/llm/runner/text_token_generator.h
@@ -77,11 +77,23 @@ class ET_EXPERIMENTAL TextTokenGenerator {
     } else {
       token_data = tokens;
       token_shape = {1, static_cast<int>(tokens.size())};
+      // Prevent reallocation that would invalidate from_blob's data pointer.
+      token_data.reserve(token_data.size() + max_new_tokens);
     }
 
-    // initialize tensor wrappers
+    // Create tensor wrapper. For non-kv-cache, use max capacity shape so
+    // numel_bound_ is large enough for subsequent resize_tensor_ptr calls,
+    // then resize down to the actual token count.
+    auto max_shape = use_kv_cache_
+        ? token_shape
+        : std::vector<executorch::aten::SizesType>{
+              1, static_cast<int>(tokens.size() + max_new_tokens)};
     auto tokens_managed = from_blob(
-        token_data.data(), token_shape, executorch::aten::ScalarType::Long);
+        token_data.data(), max_shape, executorch::aten::ScalarType::Long);
+    if (!use_kv_cache_) {
+      ET_CHECK_OK_OR_RETURN_ERROR(
+          resize_tensor_ptr(tokens_managed, token_shape));
+    }
 
     should_stop_ = false;