diff --git a/crates/cubecl-lac/src/matmul/cmma/compute_loop.rs b/crates/cubecl-lac/src/matmul/cmma/compute_loop.rs
index 2932b4d1..38d0ad62 100644
--- a/crates/cubecl-lac/src/matmul/cmma/compute_loop.rs
+++ b/crates/cubecl-lac/src/matmul/cmma/compute_loop.rs
@@ -117,7 +117,7 @@ pub mod tests {
     ) {
         let mut lhs = SharedMemory::<FC>::new(Comptime::get(m * k));
         let mut rhs = SharedMemory::<FC>::new(Comptime::get(k * n));
-        let mut accumulate = SharedMemory::<F>::new(Comptime::get(m * n));
+
         for i in range(0u32, Comptime::get(m * k), Comptime::new(false)) {
             lhs[i] = lhs_tensor[i];
         }
@@ -125,7 +125,7 @@ pub mod tests {
             rhs[i] = rhs_tensor[i];
         }
         for i in range(0u32, Comptime::get(m * n), Comptime::new(false)) {
-            accumulate[i] = F::new(0.);
+            accumulate_array[i] = F::new(0.);
         }
 
         let shared_memories = SharedMemories { lhs, rhs };
@@ -134,17 +134,17 @@ pub mod tests {
         compute_loop(shared_memories, accumulators, config);
 
         let offset = UNIT_POS_Y * UInt::new(512);
-        let slice = accumulate_array.slice_mut(offset, offset + UInt::new(256));
+        let slice_0 = accumulate_array.slice_mut(offset, offset + UInt::new(256));
         cmma::store::<F>(
-            slice,
+            slice_0,
             &accumulators.first,
             UInt::new(16),
             cmma::MatrixLayout::RowMajor,
         );
 
-        let slice = accumulate_array.slice_mut(offset + UInt::new(256), offset + UInt::new(512));
+        let slice_1 = accumulate_array.slice_mut(offset + UInt::new(256), offset + UInt::new(512));
         cmma::store::<F>(
-            slice,
+            slice_1,
             &accumulators.second,
             UInt::new(16),
             cmma::MatrixLayout::RowMajor,
@@ -233,16 +233,19 @@ pub mod tests {
             return;
         }
 
-        let lhs = range_tensor_f16::<R>(16, 32, device);
-        let rhs = range_tensor_f16::<R>(32, 32, device);
-        let results = create_empty::<R>(16, 32, device);
+        let m = 16;
+        let k = 32;
+        let n = 32;
+        let lhs = range_tensor_f16::<R>(m, k, device);
+        let rhs = range_tensor_f16::<R>(k, n, device);
+        let results = create_empty::<R>(m, n, device);
         let cube_dim = CubeDim::new(32, 1, 1);
         let cube_count = CubeCount::Static(1, 1, 1);
 
         let config = CmmaConfig {
-            block_size_m: UInt::new(16),
-            block_size_k: UInt::new(32),
-            block_size_n: UInt::new(32),
+            block_size_m: UInt::new(m as u32),
+            block_size_k: UInt::new(k as u32),
+            block_size_n: UInt::new(n as u32),
             tile_size: UInt::new(16),
             check_m_bounds: false,
             check_k_bounds: false,
@@ -256,10 +259,10 @@ pub mod tests {
             cube_dim,
             TensorArg::new(&lhs.handle, &lhs.strides, &lhs.shape),
             TensorArg::new(&rhs.handle, &rhs.strides, &rhs.shape),
-            ArrayArg::new(&results, 512),
-            UInt::new(16),
-            UInt::new(32),
-            UInt::new(32),
+            ArrayArg::new(&results, m * n),
+            UInt::new(m as u32),
+            UInt::new(k as u32),
+            UInt::new(n as u32),
             config,
         );