diff --git a/mlx/backend/cpu/arg_reduce.cpp b/mlx/backend/cpu/arg_reduce.cpp
index 66468912d..41ab9fb60 100644
--- a/mlx/backend/cpu/arg_reduce.cpp
+++ b/mlx/backend/cpu/arg_reduce.cpp
@@ -17,14 +17,14 @@ void arg_reduce(const array& in, array& out, const OpT& op, int axis) {
   Strides strides = remove_index(in.strides(), axis);
   Shape shape = remove_index(in.shape(), axis);
   auto in_ptr = in.data<InT>();
-  auto out_ptr = out.data<uint32_t>();
+  auto out_ptr = out.data<int64_t>();
 
-  for (uint32_t i = 0; i < out.size(); ++i) {
+  for (int64_t i = 0; i < out.size(); ++i) {
     auto loc = elem_to_loc(i, shape, strides);
     auto local_in_ptr = in_ptr + loc;
-    uint32_t ind_v = 0;
+    int64_t ind_v = 0;
     InT v = (*local_in_ptr);
-    for (uint32_t j = 0; j < axis_size; ++j, local_in_ptr += axis_stride) {
+    for (int64_t j = 0; j < axis_size; ++j, local_in_ptr += axis_stride) {
       op(j, (*local_in_ptr), &ind_v, &v);
     }
     out_ptr[i] = ind_v;
diff --git a/mlx/backend/cpu/binary.cpp b/mlx/backend/cpu/binary.cpp
index 35aa2a3e0..94dac1435 100644
--- a/mlx/backend/cpu/binary.cpp
+++ b/mlx/backend/cpu/binary.cpp
@@ -17,7 +17,7 @@ namespace mlx::core {
 namespace {
 
 template <typename Op>
-void binary(const array& a, const array& b, array& out, Op op, Stream stream) {
+void binary(const array& a, const array& b, array& out, Op /* op */, Stream stream) {
   auto bopt = get_binary_op_type(a, b);
   set_binary_op_output_data(a, b, out, bopt);
 
@@ -81,7 +81,7 @@ void comparison_op(
     const array& a,
     const array& b,
     array& out,
-    Op op,
+    Op /* op */,
     Stream stream) {
   auto bopt = get_binary_op_type(a, b);
   set_binary_op_output_data(a, b, out, bopt);
@@ -146,7 +146,7 @@ void binary_float(
     const array& a,
     const array& b,
     array& out,
-    Op op,
+    Op /* op */,
     Stream stream) {
   auto bopt = get_binary_op_type(a, b);
   set_binary_op_output_data(a, b, out, bopt);
@@ -187,7 +187,7 @@ void binary_int(
     const array& a,
     const array& b,
     array& out,
-    Op op,
+    Op /* op */,
     Stream stream) {
   auto bopt = get_binary_op_type(a, b);
   set_binary_op_output_data(a, b, out, bopt);
diff --git a/mlx/backend/cpu/binary_two.h b/mlx/backend/cpu/binary_two.h
index fa0ca7996..7038310e3 100644
--- a/mlx/backend/cpu/binary_two.h
+++ b/mlx/backend/cpu/binary_two.h
@@ -99,7 +99,7 @@ void binary_op_dispatch_dims(
   ContiguousIterator a_it(shape, a_strides, ndim - 2);
   ContiguousIterator b_it(shape, b_strides, ndim - 2);
   auto stride = out_strides[ndim - 3];
-  for (size_t elem = 0; elem < a.size(); elem += stride) {
+  for (int64_t elem = 0; elem < std::ssize(a); elem += stride) {
     binary_op_dims<T, U, Op, 2>(
         a_ptr + a_it.loc,
         b_ptr + b_it.loc,
@@ -137,21 +137,21 @@ void binary_op(
   if (bopt == BinaryOpType::ScalarScalar) {
     std::tie(*out_a_ptr, *out_b_ptr) = op(*a_ptr, *b_ptr);
   } else if (bopt == BinaryOpType::ScalarVector) {
-    for (size_t i = 0; i < b.data_size(); ++i) {
+    for (int64_t i = 0; i < b.data_size(); ++i) {
       std::tie(*out_a_ptr, *out_b_ptr) = op(*a_ptr, *b_ptr);
       out_a_ptr++;
       out_b_ptr++;
       b_ptr++;
     }
   } else if (bopt == BinaryOpType::VectorScalar) {
-    for (size_t i = 0; i < a.data_size(); ++i) {
+    for (int64_t i = 0; i < a.data_size(); ++i) {
       std::tie(*out_a_ptr, *out_b_ptr) = op(*a_ptr, *b_ptr);
       out_a_ptr++;
       out_b_ptr++;
       a_ptr++;
     }
   } else { // VectorVector
-    for (size_t i = 0; i < a.size(); ++i) {
+    for (int64_t i = 0; i < a.size(); ++i) {
       std::tie(*out_a_ptr, *out_b_ptr) = op(*a_ptr, *b_ptr);
       out_a_ptr++;
       out_b_ptr++;
diff --git a/mlx/backend/cpu/conv.cpp b/mlx/backend/cpu/conv.cpp
index 5d4638ade..e278a2b7f 100644
--- a/mlx/backend/cpu/conv.cpp
+++ b/mlx/backend/cpu/conv.cpp
@@ -860,7 +860,7 @@ void explicit_gemm_conv_1D_cpu(
     const std::vector<int>& padding_lo,
     const std::vector<int>& padding_hi,
     const std::vector<int>& wt_strides,
-    const std::vector<int>& wt_dilation,
+    const std::vector<int>& /* wt_dilation */,
     Stream stream) {
   const int N = in.shape(0); // Batch size, should be the same as out.shape(0)
   const int iH = in.shape(1); // Input spatial dim
@@ -1003,7 +1003,7 @@ void explicit_gemm_conv_ND_cpu(
     const std::vector<int>& padding_lo,
     const std::vector<int>& padding_hi,
     const std::vector<int>& wt_strides,
-    const std::vector<int>& wt_dilation,
+    const std::vector<int>& /* wt_dilation */,
     const bool flip,
     Stream stream) {
   const int N = in.shape(0); // Batch size, should be the same as out.shape(0)
@@ -1023,7 +1023,7 @@ void explicit_gemm_conv_ND_cpu(
   // Pad input
   Shape padded_shape(in.shape().size());
   padded_shape.front() = N;
-  for (size_t i = 0; i < iDim.size(); i++) {
+  for (int i = 0; i < iDim.size(); i++) {
     padded_shape[i + 1] = iDim[i] + padding_lo[i] + padding_hi[i];
   }
   padded_shape.back() = C;
@@ -1054,20 +1054,20 @@ void explicit_gemm_conv_ND_cpu(
   // Make strided view
   Shape strided_shape(oDim.size() + wDim.size() + 2);
   strided_shape.front() = N;
-  for (size_t i = 0; i < oDim.size(); i++) {
+  for (int i = 0; i < oDim.size(); i++) {
     strided_shape[i + 1] = oDim[i];
   }
-  for (size_t i = 0; i < wDim.size(); i++) {
+  for (int i = 0; i < wDim.size(); i++) {
     strided_shape[i + 1 + oDim.size()] = wDim[i];
   }
   strided_shape.back() = C;
 
   Strides strided_strides(in.shape().size() * 2 - 2);
   strided_strides[0] = in_padded.strides()[0];
-  for (size_t i = 0; i < wt_strides.size(); i++) {
+  for (int i = 0; i < std::ssize(wt_strides); i++) {
     strided_strides[i + 1] = in_padded.strides()[i + 1] * wt_strides[i];
   }
-  for (size_t i = 1; i < in_padded.strides().size(); i++) {
+  for (int i = 1; i < std::ssize(in_padded.strides()); i++) {
     strided_strides[i + wt_strides.size()] = in_padded.strides()[i];
   }
 
diff --git a/mlx/backend/cpu/eig.cpp b/mlx/backend/cpu/eig.cpp
index 0d1f95a57..ea94a324d 100644
--- a/mlx/backend/cpu/eig.cpp
+++ b/mlx/backend/cpu/eig.cpp
@@ -70,7 +70,7 @@ void eig_impl(
     auto eig_tmp = static_cast<T*>(eig_tmp_data.buffer.raw_ptr());
     auto vec_tmp = static_cast<T*>(vec_tmp_data.buffer.raw_ptr());
     auto work_buf = array::Data{allocator::malloc(sizeof(T) * lwork)};
-    for (size_t i = 0; i < size / (N * N); ++i) {
+    for (int64_t i = 0; i < size / (N * N); ++i) {
       geev<T>(
           &jobl,
           &jobr,
diff --git a/mlx/backend/cpu/eigh.cpp b/mlx/backend/cpu/eigh.cpp
index d457c1fd9..8b586656d 100644
--- a/mlx/backend/cpu/eigh.cpp
+++ b/mlx/backend/cpu/eigh.cpp
@@ -165,7 +165,7 @@ void eigh_impl(
     EighWork<T> work(jobz, uplo, N);
 
     // Work loop
-    for (size_t i = 0; i < size / (N * N); ++i) {
+    for (int64_t i = 0; i < size / (N * N); ++i) {
       work.run(vec_ptr, eig_ptr);
       vec_ptr += N * N;
       eig_ptr += N;
diff --git a/mlx/backend/cpu/encoder.h b/mlx/backend/cpu/encoder.h
index b8e33ca81..df65e5367 100644
--- a/mlx/backend/cpu/encoder.h
+++ b/mlx/backend/cpu/encoder.h
@@ -20,8 +20,8 @@ struct CommandEncoder {
   CommandEncoder(CommandEncoder&&) = delete;
   CommandEncoder& operator=(CommandEncoder&&) = delete;
 
-  void set_input_array(const array& a) {}
-  void set_output_array(array& a) {}
+  void set_input_array(const array& /* a */) {}
+  void set_output_array(array& /* a */) {}
 
   // Hold onto a temporary until any already scheduled tasks which use it as
   // an input are complete.