diff --git a/mlx/compile.cpp b/mlx/compile.cpp
index 279fb03287..f5082010cc 100644
--- a/mlx/compile.cpp
+++ b/mlx/compile.cpp
@@ -370,7 +370,7 @@ void compile_simplify(
   auto get_scalar_rep = [](const array& a) {
     uint64_t v = 0;
     int dtype;
-    switch (a.dtype().size) {
+    switch (a.dtype().size()) {
       case 1:
         v = *a.data<uint8_t>();
         break;
@@ -384,7 +384,7 @@ void compile_simplify(
         v = *a.data<uint64_t>();
         break;
     }
-    return std::make_pair(v, a.dtype().val);
+    return std::make_pair(v, a.dtype().val());
   };
 
   for (auto& a : tape) {
diff --git a/mlx/dtype.cpp b/mlx/dtype.cpp
index cf78a7805a..665512f04b 100644
--- a/mlx/dtype.cpp
+++ b/mlx/dtype.cpp
@@ -81,11 +81,12 @@ constexpr Dtype::Category type_to_category[num_types] = {
 } // namespace
 
 Dtype promote_types(const Dtype& t1, const Dtype& t2) {
-  return Dtype(type_rules[static_cast<int>(t1.val)][static_cast<int>(t2.val)]);
+  return Dtype(
+      type_rules[static_cast<int>(t1.val())][static_cast<int>(t2.val())]);
 }
 
 Dtype::Kind kindof(const Dtype& t) {
-  return type_kinds[static_cast<int>(t.val)];
+  return type_kinds[static_cast<int>(t.val())];
 }
 
 template <>
@@ -167,7 +168,7 @@ bool issubdtype(const Dtype::Category& cat, const Dtype& type) {
 }
 
 bool issubdtype(const Dtype& type, const Dtype::Category& cat) {
-  return issubdtype(type_to_category[static_cast<uint32_t>(type.val)], cat);
+  return issubdtype(type_to_category[static_cast<uint32_t>(type.val())], cat);
 }
 
 bool issubdtype(const Dtype::Category& a, const Dtype::Category& b) {
diff --git a/mlx/dtype.h b/mlx/dtype.h
index 5f9ee27cc1..11d61e378b 100644
--- a/mlx/dtype.h
+++ b/mlx/dtype.h
@@ -47,12 +47,21 @@ struct Dtype {
     generic
   };
 
-  Val val;
-  const uint8_t size;
-  constexpr explicit Dtype(Val val, uint8_t size) : val(val), size(size) {}
+  constexpr explicit Dtype(Val val, uint8_t size) : val_(val), size_(size) {}
+
   constexpr operator Val() const {
-    return val;
+    return val_;
   }
+  constexpr Val val() const {
+    return val_;
+  }
+  constexpr uint8_t size() const {
+    return size_;
+  }
+
+ private:
+  Val val_;
+  uint8_t size_;
 };
 
 inline constexpr Dtype bool_{Dtype::Val::bool_, sizeof(bool)};
@@ -91,7 +100,7 @@ bool issubdtype(const Dtype::Category& a, const Dtype::Category& b);
 Dtype promote_types(const Dtype& t1, const Dtype& t2);
 
 inline uint8_t size_of(const Dtype& t) {
-  return t.size;
+  return t.size();
 }
 
 Dtype::Kind kindof(const Dtype& t);
diff --git a/mlx/io/gguf.cpp b/mlx/io/gguf.cpp
index be1f382bae..dffb2aa1d7 100644
--- a/mlx/io/gguf.cpp
+++ b/mlx/io/gguf.cpp
@@ -64,7 +64,7 @@ std::tuple<allocator::Buffer, Dtype> extract_tensor_data(gguf_tensor* tensor) {
     memcpy(
         buffer.raw_ptr(),
         tensor->weights_data,
-        tensor->num_weights * equivalent_dtype.value().size);
+        tensor->num_weights * equivalent_dtype.value().size());
     return {buffer, equivalent_dtype.value()};
   }
   // Otherwise, we convert to float16.
diff --git a/mlx/io/gguf_quants.cpp b/mlx/io/gguf_quants.cpp
index 4f89b22784..8e6a5b2f9e 100644
--- a/mlx/io/gguf_quants.cpp
+++ b/mlx/io/gguf_quants.cpp
@@ -120,7 +120,7 @@ void gguf_load_quantized(
 
   std::vector<int> weights_shape = shape;
   weights_shape.back() /= (weights_per_byte * 4);
-  auto w_nbytes = uint32.size *
+  auto w_nbytes = uint32.size() *
       std::accumulate(weights_shape.begin(),
                       weights_shape.end(),
                       1,
@@ -130,7 +130,7 @@ void gguf_load_quantized(
 
   // For scales and bias
   shape[shape.size() - 1] = shape[shape.size() - 1] / weights_per_block;
-  auto sb_nbytes = float16.size *
+  auto sb_nbytes = float16.size() *
       std::accumulate(shape.begin(), shape.end(), 1, std::multiplies<size_t>());
 
   array scales(allocator::malloc(sb_nbytes), shape, float16);
diff --git a/mlx/utils.cpp b/mlx/utils.cpp
index 34882884ba..e3c2c72bd7 100644
--- a/mlx/utils.cpp
+++ b/mlx/utils.cpp
@@ -58,11 +58,11 @@ inline void PrintFormatter::print(std::ostream& os, complex64_t val) {
 PrintFormatter global_formatter;
 
 Dtype result_type(const std::vector<array>& arrays) {
-  std::vector<Dtype> dtypes(1, bool_);
+  Dtype t = bool_;
   for (auto& arr : arrays) {
-    dtypes.push_back(promote_types(dtypes.back(), arr.dtype()));
+    t = promote_types(t, arr.dtype());
   }
-  return dtypes.back();
+  return t;
 }
 
 std::vector<int> broadcast_shapes(
diff --git a/python/src/array.cpp b/python/src/array.cpp
index 71ecd658fc..c9bc8eebeb 100644
--- a/python/src/array.cpp
+++ b/python/src/array.cpp
@@ -97,7 +97,8 @@ void init_array(nb::module_& m) {
       See the :ref:`list of types <data_types>` for more details
       on available data types.
       )pbdoc")
-      .def_ro("size", &Dtype::size, R"pbdoc(Size of the type in bytes.)pbdoc")
+      .def_prop_ro(
+          "size", &Dtype::size, R"pbdoc(Size of the type in bytes.)pbdoc")
       .def(
           "__repr__",
           [](const Dtype& t) {
@@ -112,7 +113,7 @@ void init_array(nb::module_& m) {
             return nb::isinstance<Dtype>(other) && t == nb::cast<Dtype>(other);
           })
       .def("__hash__", [](const Dtype& t) {
-        return static_cast<int64_t>(t.val);
+        return static_cast<int64_t>(t.val());
       });
 
   m.attr("bool_") = nb::cast(bool_);