diff --git a/cpp/src/neighbors/detail/cagra/cagra_build.cuh b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
index 96ff8344d3..515e464bb8 100644
--- a/cpp/src/neighbors/detail/cagra/cagra_build.cuh
+++ b/cpp/src/neighbors/detail/cagra/cagra_build.cuh
@@ -6,6 +6,7 @@
 
 #include "../../../core/nvtx.hpp"
 #include "../../../preprocessing/quantize/vpq_build-ext.cuh"
+#include "../../ivf_pq/ivf_pq_fp16_overflow.cuh"
 #include "graph_core.cuh"
 
 #include <raft/core/copy.cuh>
@@ -2197,6 +2198,24 @@ index<T, IdxT> build(
       knn_build_params = cagra::graph_build_params::ivf_pq_params(dataset.extents(), params.metric);
     }
   }
+
+  // Guard against potential FP16 distance overflow for large-magnitude datasets. Applies to any
+  // IVF-PQ graph build (auto-selected above or explicitly set in params) -> fall back to FP32.
+  if (auto* pq = std::get_if<cagra::graph_build_params::ivf_pq_params>(&knn_build_params)) {
+    const bool using_fp16_distance = pq->search_params.internal_distance_dtype == CUDA_R_16F ||
+                                     pq->search_params.coarse_search_dtype == CUDA_R_16F;
+    if (using_fp16_distance &&
+        ivf_pq::helpers::estimate_fp16_overflow(res, dataset, params.metric)) {
+      RAFT_LOG_WARN(
+        "IVF-PQ internal type of FP16 is likely insufficient for this dataset to avoid overflow in "
+        "distance computations -> "
+        "Switching 'internal_distance_dtype' and 'coarse_search_dtype' to FP32");
+      pq->search_params.internal_distance_dtype = CUDA_R_32F;
+      pq->search_params.coarse_search_dtype     = CUDA_R_32F;
+      // lut_dtype is left unchanged because its per-subspace terms are smaller by a factor of
+      // pq_dim and therefore, less likely to overflow.
+    }
+  }
   RAFT_EXPECTS(
     params.metric != cuvs::distance::DistanceType::BitwiseHamming ||
       std::holds_alternative<cagra::graph_build_params::iterative_search_params>(
diff --git a/cpp/src/neighbors/ivf_pq/ivf_pq_fp16_overflow.cuh b/cpp/src/neighbors/ivf_pq/ivf_pq_fp16_overflow.cuh
new file mode 100644
index 0000000000..2f99da9e06
--- /dev/null
+++ b/cpp/src/neighbors/ivf_pq/ivf_pq_fp16_overflow.cuh
@@ -0,0 +1,137 @@
+/*
+ * SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION.
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+#pragma once
+
+#include "../detail/ann_utils.cuh"  // cuvs::spatial::knn::detail::utils::mapping
+
+#include <cuvs/distance/distance.hpp>
+
+#include <raft/core/device_mdarray.hpp>
+#include <raft/core/error.hpp>
+#include <raft/core/mdspan.hpp>
+#include <raft/core/operators.hpp>
+#include <raft/core/resource/cuda_stream.hpp>
+#include <raft/core/resource/device_memory_resource.hpp>
+#include <raft/core/resources.hpp>
+#include <raft/linalg/map_reduce.cuh>
+#include <raft/linalg/reduce.cuh>
+#include <raft/matrix/sample_rows.cuh>
+#include <raft/random/rng.cuh>
+#include <raft/util/cudart_utils.hpp>
+
+#include <cstdint>
+
+namespace cuvs::neighbors::ivf_pq::detail {
+
+/**
+ * Estimate max_i ||x_i||^2 over the dataset by uniformly sampling a fraction from it.
+ *
+ * Decision: Uniform sampling is selected as it is sufficient to detect FP16 overflow in
+ * the datasets, where overflow-causing large vectors are frequent (e.g. SIFT 1M). For 
+ * dataset with rare large outliers, we might preferably sample biasedly towards large vectors,
+ * e.g. via top-k selection over the vectors with largest L_inf norm.
+ */
+template <typename DataT, typename Accessor>
+float estimate_max_squared_norm(
+  raft::resources const& handle,
+  raft::mdspan<const DataT, raft::matrix_extent<int64_t>, raft::row_major, Accessor> dataset)
+{
+  auto stream          = raft::resource::get_cuda_stream(handle);
+  const int64_t n_rows = dataset.extent(0);
+  const int64_t dim    = dataset.extent(1);
+
+  // Determine sample size based on a smooth saturation equation. The equation satisfies:
+  // - n_sample is always less than or equal to n_rows
+  // - n_sample saturates to kSaturation when n_rows is inf
+  // - n_sample increases fast for small n_rows and slow to saturation for large n_rows
+  // Idea: we sample most of the dataset when it is small-sized, and only a small fraction
+  // (up to a maximum/saturation number) when the dataset size grows large.
+  // kSaturation and kDelay are selected as a compromise between runtime and outlier recall.
+  constexpr int64_t kSaturation = 20000;
+  constexpr int64_t kDelay      = kSaturation * 10;
+  RAFT_EXPECTS(kDelay >= kSaturation,
+               "kDelay must not be smaller than kSaturation so that n_sample is always less than "
+               "or equal to n_rows");
+  int64_t n_sample = (n_rows * kSaturation + (n_rows + kDelay - 1)) / (n_rows + kDelay);
+
+  // Sample from the dataset
+  auto mr = raft::resource::get_workspace_resource_ref(handle);
+  auto sample =
+    raft::make_device_mdarray<DataT>(handle, mr, raft::make_extents<int64_t>(n_sample, dim));
+  raft::matrix::sample_rows<DataT, int64_t>(
+    handle, raft::random::RngState{137}, dataset, sample.view());
+
+  // Compute float-mapped squared norm
+  auto d_map_sq_norm = raft::make_device_vector<float, int64_t>(handle, n_sample);
+  raft::linalg::reduce<raft::Apply::ALONG_ROWS>(
+    handle,
+    raft::make_const_mdspan(sample.view()),
+    d_map_sq_norm.view(),
+    0.0f,
+    false,
+    [] __device__(DataT v, auto) -> float {
+      float e = cuvs::spatial::knn::detail::utils::mapping<float>{}(v);
+      return e * e;
+    },
+    raft::add_op(),
+    raft::identity_op());
+  // Compute max of squared norm vector
+  auto d_max_sq = raft::make_device_scalar<float>(handle, 0.0f);
+  raft::linalg::map_reduce(handle,
+                           raft::make_const_mdspan(d_map_sq_norm.view()),
+                           d_max_sq.view(),
+                           0.0f,
+                           raft::identity_op(),
+                           raft::max_op());
+
+  float max_sq = 0.0f;
+  raft::update_host(&max_sq, d_max_sq.data_handle(), 1, stream);
+  raft::resource::sync_stream(handle);
+
+  return max_sq;
+}
+
+}  // namespace cuvs::neighbors::ivf_pq::detail
+
+namespace cuvs::neighbors::ivf_pq::helpers {
+
+/**
+ * @brief Estimate whether FP16 is likely insufficient for IVF-PQ's full-magnitude distance
+ * computations on this dataset (i.e. `internal_distance_dtype` and `coarse_search_dtype`).
+ *
+ * We bound the largest achievable score from the dataset's vector norms. With R = max_i ||x_i||
+ * (estimated from a random sample of the dataset):
+ *   - L2Expanded:     ||x - y||^2 = ||x||^2 + ||y||^2 - 2<x,y> <= (||x|| + ||y||)^2 <= 4 * R^2
+ *   - InnerProduct:   |<x, y>|    <= ||x|| * ||y||                                  <=     R^2
+ *   - CosineExpanded: data is L2-normalized, so |score| <= 1 and overflow is impossible.
+ */
+template <typename DataT, typename Accessor>
+bool estimate_fp16_overflow(
+  raft::resources const& handle,
+  raft::mdspan<const DataT, raft::matrix_extent<int64_t>, raft::row_major, Accessor> dataset,
+  cuvs::distance::DistanceType metric)
+{
+  if (dataset.extent(0) == 0) { return false; }
+
+  // Cosine similarity scores does normalization itself, so overflow won't happen
+  if (metric == cuvs::distance::DistanceType::CosineExpanded) { return false; }
+
+  // FP16 largest finite value, with a defensive margin to also avoid precision loss near the limit.
+  constexpr float kFp16Max              = 65504.0f;
+  constexpr float kFp16DefensiveMargin  = 0.25f;
+  const float overflow_detect_threshold = kFp16DefensiveMargin * kFp16Max;
+
+  const float max_vector_sq_norm =
+    cuvs::neighbors::ivf_pq::detail::estimate_max_squared_norm(handle, dataset);
+
+  const float max_distance_sq_norm = metric == cuvs::distance::DistanceType::L2Expanded
+                                       ? 4.0f * max_vector_sq_norm
+                                       : max_vector_sq_norm;
+
+  return max_distance_sq_norm > overflow_detect_threshold;
+}
+
+}  // namespace cuvs::neighbors::ivf_pq::helpers