diff --git a/ggml/src/iqk/iqk_cpu_ops.cpp b/ggml/src/iqk/iqk_cpu_ops.cpp
index 9a20e0c8..4581a1dd 100644
--- a/ggml/src/iqk/iqk_cpu_ops.cpp
+++ b/ggml/src/iqk/iqk_cpu_ops.cpp
@@ -16,6 +16,7 @@
 #include <algorithm>
 #include <cmath>
 #include <cstring>
+//#include <thread>
 
 #ifdef __ARM_NEON
 #include <arm_neon.h>
@@ -503,3 +504,49 @@ void iqk_hadamard(struct ggml_tensor * dst, int ith, int nth) {
         fast_ht(nh, y);
     }
 }
+
+namespace {
+float iqk_exp_with_thresh_impl(int n, float * logits, float max, float min) {
+    float sum = 0;
+#ifdef __AVX2__
+    auto vmax = _mm256_set1_ps(max);
+    auto vmin = _mm256_set1_ps(min);
+    auto vsum = _mm256_setzero_ps();
+    for (int j = 0; j < n/8; ++j) {
+        auto x = _mm256_loadu_ps(logits);
+        auto mask = _mm256_cmp_ps(x, vmin, _CMP_GE_OQ);
+        auto exp_x = v_expf(_mm256_sub_ps(x, vmax));
+        exp_x = _mm256_and_ps(exp_x, mask);
+        vsum = _mm256_add_ps(vsum, exp_x);
+        _mm256_storeu_ps(logits, exp_x);
+        logits += 8;
+    }
+    sum = hsum_float_8(vsum);
+    for (int j = 0; j < n - 8*(n/8); ++j) {
+        float p = logits[j] > min ? expf(logits[j] - max) : 0;
+        sum += p;
+        logits[j] = p;
+    }
+#else
+    for (int j = 0; j < n; ++j) {
+        float p = logits[j] > min ? expf(logits[j] - max) : 0;
+        sum += p;
+        logits[j] = p;
+    }
+#endif
+    return sum;
+}
+}
+
+float iqk_exp_with_thresh(int n, float * logits, float max, float min) {
+    return iqk_exp_with_thresh_impl(n, logits, max, min);
+    //if (n < (1 << 16)) return iqk_exp_with_thresh_impl(n, logits, max, min);
+    //std::array<float, 2> result;
+    //auto compute = [logits, max, min, &result] (int first, int last, int ith) {
+    //    result[ith] = iqk_exp_with_thresh_impl(last - first, logits + first, max, min);
+    //};
+    //auto t = std::thread(compute, 0, n/2, 0);
+    //compute(n/2, n, 1);
+    //t.join();
+    //return result[0] + result[1];
+}
diff --git a/ggml/src/iqk/iqk_cpu_ops.h b/ggml/src/iqk/iqk_cpu_ops.h
index 8656d7f1..267c3e85 100644
--- a/ggml/src/iqk/iqk_cpu_ops.h
+++ b/ggml/src/iqk/iqk_cpu_ops.h
@@ -30,6 +30,8 @@ void iqk_mul_multi_add(struct ggml_tensor * dst, int ith, int nth);
 
 void iqk_hadamard(struct ggml_tensor * dst, int ith, int nth);
 
+float iqk_exp_with_thresh(int n, float * logits, float max, float min);
+
 #ifdef __cplusplus
 }
 #endif
diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp
index b598a2f1..deb6f273 100644
--- a/src/llama-sampling.cpp
+++ b/src/llama-sampling.cpp
@@ -2,6 +2,8 @@
 #include "llama-vocab.h"
 #include "llama-grammar.h"
 
+#include "iqk/iqk_cpu_ops.h"
+
 #include <algorithm>
 #include <cstring>
 #include <ctime>
@@ -1131,7 +1133,7 @@ void llama_prep_adaptive_p_impl(
               struct llama_sampling * smpl,
              llama_token_data_array * candidates,
     struct llama_sampler_adaptive_p * adapt_p_ctx) {
-    constexpr float kDelta = 16.6f;
+    constexpr float kDelta = 30.0f; //16.6f;
     auto t_start = ggml_time_us();
     auto & orig_prob = adapt_p_ctx->orig_prob;
     if (candidates->size != orig_prob.size() || candidates->sorted) {
@@ -1141,18 +1143,26 @@ void llama_prep_adaptive_p_impl(
         GGML_ABORT("Bad candidates in adaptive_p sampler");
     }
 
-    float max_logit = candidates->data[0].logit;
-    for (int j = 1; j < int(candidates->size); ++j) {
-        max_logit = std::max(max_logit, candidates->data[j].logit);
-    }
-    float min_logit = max_logit - kDelta;
-    float cum_prob = 0.0f;
+    float max_logit = -INFINITY;
     for (int j = 0; j < int(candidates->size); ++j) {
-        float prob = candidates->data[j].logit > min_logit ? expf(candidates->data[j].logit - max_logit) : 0.0f;
-        cum_prob += prob;
-        orig_prob[j] = prob;
+        orig_prob[j] = candidates->data[j].logit;
+        max_logit = std::max(max_logit, orig_prob[j]);
     }
-    adapt_p_ctx->cum_orig_prob = cum_prob;
+    adapt_p_ctx->cum_orig_prob = iqk_exp_with_thresh(orig_prob.size(), orig_prob.data(), max_logit, max_logit - kDelta);
+
+    //float max_logit = candidates->data[0].logit;
+    //for (int j = 1; j < int(candidates->size); ++j) {
+    //    max_logit = std::max(max_logit, candidates->data[j].logit);
+    //}
+    //float min_logit = max_logit - kDelta;
+    //float cum_prob = 0.0f;
+    //for (int j = 0; j < int(candidates->size); ++j) {
+    //    float prob = candidates->data[j].logit > min_logit ? expf(candidates->data[j].logit - max_logit) : 0.0f;
+    //    cum_prob += prob;
+    //    orig_prob[j] = prob;
+    //}
+    //adapt_p_ctx->cum_orig_prob = cum_prob;
+
     if (smpl) smpl->t_sample_us += ggml_time_us() - t_start;
 }