diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp
index 699264553a..7bfb8751ae 100644
--- a/src/llama-quant.cpp
+++ b/src/llama-quant.cpp
@@ -650,9 +650,7 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
         GGML_TYPE_IQ3_XXS,
         GGML_TYPE_Q3_K,
         GGML_TYPE_IQ4_XS,
-        GGML_TYPE_Q4_1,
         GGML_TYPE_Q4_K,
-        GGML_TYPE_Q5_1,
         GGML_TYPE_Q5_K,
         GGML_TYPE_Q6_K,
         GGML_TYPE_Q8_0
@@ -961,10 +959,24 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
         ml.load_data_for(tensor);
 
         // Dequantize sampled rows into f32_sample
-        const int rows_sample_per_expert = activations_data ? 512 : 256;
         const int64_t n_per_row = tensor->ne[0];
         const int64_t nrows_total = tensor->ne[1];
         const int64_t ne2 = tensor->ne[2] > 0 ? tensor->ne[2] : 1;
+
+        // Compute rows based on tensor shape and slice count
+        auto sample_rows = [](const int64_t n, const int64_t rows, const int64_t n2, const bool has_acts) -> int64_t {
+            const double tensor_budget = has_acts ? 1 * 1024 * 1024 : 0.5 * 1024 * 1024;
+            const double scale_rows = std::clamp(std::sqrt(std::max(1.0, (double)rows) / 4096.0), 0.5, 2.0); // favour more rows for large nrt
+            const double slice_budget = tensor_budget * scale_rows / std::max<int64_t>(1, n2);
+            const int64_t min_rows = has_acts ? 128 : 64;
+            const int64_t max_rows = 4096;
+            int64_t total_rows = std::llround(slice_budget / std::max<int64_t>(1, n));
+            total_rows = std::max<int64_t>(min_rows, std::min<int64_t>(total_rows, std::min<int64_t>(rows, max_rows)));
+            if (rows <= min_rows * 2) { total_rows = rows; } // use all rows for small tensors
+            return total_rows;
+        };
+
+        const int64_t rows_sample_per_expert = sample_rows(n_per_row, nrows_total, ne2, activations_data != nullptr);
         std::vector<float> f32_sample;
         f32_sample.reserve((size_t)ne2 * (size_t)std::min<int64_t>(nrows_total, rows_sample_per_expert) * (size_t)n_per_row);
         std::vector<int64_t> rows_sample(ne2, 0);