diff --git a/extension/llm/sampler/sampler.cpp b/extension/llm/sampler/sampler.cpp
index 3beda885d6f..d41da96f07e 100644
--- a/extension/llm/sampler/sampler.cpp
+++ b/extension/llm/sampler/sampler.cpp
@@ -69,6 +69,56 @@ int32_t Sampler::sample_mult(T* probabilities, float coin) {
   return vocab_size_ - 1; // in case of rounding errors
 }
 
+template <typename T>
+int32_t Sampler::sample_topk(T* probabilities, float coin) {
+  // top-k sampling samples from the k highest-probability tokens.
+  // coin is a random number in [0, 1), usually from random_f32().
+  //
+  // TODO: probindex is allocated on every call; lifting it to a member
+  // would avoid per-token heap allocation in autoregressive loops.
+  const int n = vocab_size_;
+  const int k = std::min(topk_, n);
+  // Defensive: callers gate on topk_ > 0, but a private helper should not
+  // rely on external invariants. Fall back to a deterministic index.
+  if (k <= 0) {
+    return 0;
+  }
+
+  std::unique_ptr<ProbIndex<T>[]> probindex =
+      std::make_unique<ProbIndex<T>[]>(n);
+  for (int i = 0; i < n; i++) {
+    probindex[i].index = i;
+    probindex[i].prob = probabilities[i];
+  }
+
+  auto compare = [](const ProbIndex<T>& a, const ProbIndex<T>& b) {
+    return a.prob > b.prob;
+  };
+  // Partial sort: only the top-k entries need to be sorted in descending order.
+  std::partial_sort(
+      probindex.get(), probindex.get() + k, probindex.get() + n, compare);
+
+  // Sum of the top-k probabilities. Used to scale `coin` instead of
+  // explicitly renormalizing the k probs — mathematically equivalent and
+  // saves k divisions. Accumulate in float so FP16/BF16 inputs don't lose
+  // precision over k summands.
+  float topk_sum = 0.0f;
+  for (int i = 0; i < k; i++) {
+    topk_sum += static_cast<float>(probindex[i].prob);
+  }
+
+  // Sample from the (implicitly renormalized) top-k distribution.
+  const float r = coin * topk_sum;
+  float cdf = 0.0f;
+  for (int i = 0; i < k; i++) {
+    cdf += static_cast<float>(probindex[i].prob);
+    if (r < cdf) {
+      return probindex[i].index;
+    }
+  }
+  return probindex[k - 1].index; // in case of rounding errors
+}
+
 template <typename T>
 int32_t Sampler::sample_topp(T* probabilities, float coin) {
   // top-p sampling (or "nucleus sampling") samples from the smallest set of
@@ -186,7 +236,10 @@ int32_t Sampler::sample(T* logits) {
     // flip a (float) coin (this is our source of entropy for sampling)
     float coin = random_f32(&rng_state_);
     // we sample from this distribution to get the next token
-    if (topp_ <= 0 || topp_ >= 1) {
+    if (topk_ > 0 && topk_ < vocab_size_) {
+      // top-k sampling, restrict to the k most likely tokens
+      next = sample_topk(logits, coin);
+    } else if (topp_ <= 0 || topp_ >= 1) {
       // simply sample from the predicted probability distribution
       next = sample_mult(logits, coin);
     } else {
diff --git a/extension/llm/sampler/sampler.h b/extension/llm/sampler/sampler.h
index 1525f38692a..4a480edc1ef 100644
--- a/extension/llm/sampler/sampler.h
+++ b/extension/llm/sampler/sampler.h
@@ -44,6 +44,13 @@ class ET_EXPERIMENTAL Sampler {
 
   Sampler(int32_t vocab_size, float temperature);
 
+  // Enable top-k filtering. k <= 0 or k >= vocab_size disables top-k.
+  // When top-k is enabled, top-p is ignored — the two modes are mutually
+  // exclusive in this implementation.
+  void set_topk(int32_t topk) {
+    topk_ = topk;
+  }
+
   template <typename T>
   int32_t sample(T* logits);
 
@@ -51,6 +58,8 @@ class ET_EXPERIMENTAL Sampler {
   template <typename T>
   int32_t sample_topp(T* probabilities, float coin);
   template <typename T>
+  int32_t sample_topk(T* probabilities, float coin);
+  template <typename T>
   int32_t sample_mult(T* probabilities, float coin);
   template <typename T>
   int32_t sample_argmax(T* probabilities);
@@ -60,6 +69,8 @@ class ET_EXPERIMENTAL Sampler {
   // reciprocal of temperature, or 0 if temperature == 0.
   float inv_temperature_;
   float topp_;
+  // 0 (or >= vocab_size_) means top-k is disabled.
+  int32_t topk_ = 0;
   unsigned long long rng_state_;
 };
 
diff --git a/extension/llm/sampler/test/test_sampler.cpp b/extension/llm/sampler/test/test_sampler.cpp
index 044a39458ea..8463c2e9678 100644
--- a/extension/llm/sampler/test/test_sampler.cpp
+++ b/extension/llm/sampler/test/test_sampler.cpp
@@ -8,6 +8,8 @@
 
 #include <executorch/extension/llm/sampler/sampler.h>
 
+#include <set>
+
 #include <gtest/gtest.h>
 #include <torch/torch.h>
 
@@ -39,3 +41,114 @@ TEST(SamplerTest, TestArgMaxWithFP16) {
   input[0][0][396] = 1.0f;
   EXPECT_EQ(sampler.sample(input.data_ptr<c10::Half>()), 396);
 }
+
+TEST(SamplerTest, TestTopKRestrictsToCandidates) {
+  // With topk=3, sampling must always return one of the top-3 indices,
+  // regardless of the random draw.
+  Sampler sampler{
+      /*vocab_size*/ 100,
+      /*temperature*/ 1.0f,
+      /*topp*/ 0.0f, // disable top-p so we exercise top-k alone
+      /*rng_seed*/ 42};
+  sampler.set_topk(3);
+
+  // Construct logits where indices {7, 13, 42} dominate.
+  torch::Tensor input = torch::full({100}, -10.0f, at::kFloat);
+  input[7] = 5.0f;
+  input[13] = 4.5f;
+  input[42] = 4.0f;
+
+  std::set<int32_t> allowed = {7, 13, 42};
+  for (int trial = 0; trial < 50; ++trial) {
+    // Re-fill logits each trial because sample() mutates them in place.
+    torch::Tensor logits = input.clone();
+    int32_t out = sampler.sample(logits.data_ptr<float>());
+    EXPECT_TRUE(allowed.count(out)) << "trial " << trial << " got " << out;
+  }
+}
+
+TEST(SamplerTest, TestTopKDisabledByZero) {
+  // topk=0 means disabled. With topp disabled, sampling collapses to
+  // multinomial over the full vocab, but the dominant token should still
+  // win the vast majority of the time.
+  Sampler sampler{
+      /*vocab_size*/ 50,
+      /*temperature*/ 1.0f,
+      /*topp*/ 0.0f,
+      /*rng_seed*/ 7};
+  sampler.set_topk(0); // disabled
+
+  torch::Tensor input = torch::full({50}, -10.0f, at::kFloat);
+  input[11] = 20.0f; // dominant
+
+  int hits = 0;
+  for (int trial = 0; trial < 20; ++trial) {
+    torch::Tensor logits = input.clone();
+    if (sampler.sample(logits.data_ptr<float>()) == 11) {
+      hits++;
+    }
+  }
+  EXPECT_GE(hits, 18); // dominant token should win nearly every time
+}
+
+TEST(SamplerTest, TestTopKWithFP16) {
+  // Smoke test the FP16 template instantiation of the top-k path.
+  Sampler sampler{
+      /*vocab_size*/ 50,
+      /*temperature*/ 1.0f,
+      /*topp*/ 0.0f,
+      /*rng_seed*/ 99};
+  sampler.set_topk(2);
+
+  torch::Tensor input = torch::full({50}, -10.0f, at::kHalf);
+  input[3] = 5.0f;
+  input[8] = 4.5f;
+
+  std::set<int32_t> allowed = {3, 8};
+  for (int trial = 0; trial < 30; ++trial) {
+    torch::Tensor logits = input.clone();
+    int32_t out = sampler.sample(logits.data_ptr<c10::Half>());
+    EXPECT_TRUE(allowed.count(out)) << "trial " << trial << " got " << out;
+  }
+}
+
+TEST(SamplerTest, TestTopKEqualsOneIsArgmax) {
+  // topk=1 should behave like greedy argmax even with temperature > 0.
+  Sampler sampler{
+      /*vocab_size*/ 100,
+      /*temperature*/ 1.0f,
+      /*topp*/ 0.0f,
+      /*rng_seed*/ 123};
+  sampler.set_topk(1);
+
+  torch::Tensor input = torch::rand({100}, at::kFloat);
+  input[57] = 100.0f; // make 57 the unambiguous max
+
+  for (int trial = 0; trial < 10; ++trial) {
+    torch::Tensor logits = input.clone();
+    EXPECT_EQ(sampler.sample(logits.data_ptr<float>()), 57);
+  }
+}
+
+TEST(SamplerTest, TestTopKTakesPrecedenceOverTopP) {
+  // When both top-k and top-p are set, top-k should restrict the candidate
+  // set; top-p alone would admit a third token that top-k=2 must exclude.
+  Sampler sampler{
+      /*vocab_size*/ 100,
+      /*temperature*/ 1.0f,
+      /*topp*/ 0.99f, // would keep nearly the whole vocab on its own
+      /*rng_seed*/ 99};
+  sampler.set_topk(2);
+
+  torch::Tensor input = torch::full({100}, -10.0f, at::kFloat);
+  input[3] = 5.0f;
+  input[8] = 4.5f;
+  input[19] = 4.0f; // would be in the top-p set but is excluded by top-k=2
+
+  std::set<int32_t> allowed = {3, 8};
+  for (int trial = 0; trial < 50; ++trial) {
+    torch::Tensor logits = input.clone();
+    int32_t out = sampler.sample(logits.data_ptr<float>());
+    EXPECT_TRUE(allowed.count(out)) << "trial " << trial << " got " << out;
+  }
+}