diff --git a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/DotBenchmark.kt b/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/DotBenchmark.kt
index 63165baaa..53d7ee9b6 100644
--- a/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/DotBenchmark.kt
+++ b/benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/DotBenchmark.kt
@@ -15,7 +15,9 @@ import space.kscience.kmath.linear.invoke
 import space.kscience.kmath.linear.linearSpace
 import space.kscience.kmath.multik.multikAlgebra
 import space.kscience.kmath.operations.DoubleField
+import space.kscience.kmath.operations.invoke
 import space.kscience.kmath.structures.Buffer
+import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
 import kotlin.random.Random
 
 @State(Scope.Benchmark)
@@ -32,6 +34,9 @@ internal class DotBenchmark {
             random.nextDouble()
         }
 
+        val tensor1 = DoubleTensorAlgebra.randomNormal(shape = intArrayOf(dim, dim), 12224)
+        val tensor2 = DoubleTensorAlgebra.randomNormal(shape = intArrayOf(dim, dim), 12225)
+
         val cmMatrix1 = CMLinearSpace { matrix1.toCM() }
         val cmMatrix2 = CMLinearSpace { matrix2.toCM() }
 
@@ -78,4 +83,9 @@ internal class DotBenchmark {
     fun doubleDot(blackhole: Blackhole) = with(DoubleField.linearSpace) {
         blackhole.consume(matrix1 dot matrix2)
     }
+
+    @Benchmark
+    fun doubleTensorDot(blackhole: Blackhole) = DoubleTensorAlgebra.invoke {
+        blackhole.consume(tensor1 dot tensor2)
+    }
 }
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
index 50252ad31..a75e5a8e3 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/DoubleTensorAlgebra.kt
@@ -421,7 +421,7 @@ public open class DoubleTensorAlgebra :
 
         for ((res, ab) in resTensor.matrixSequence().zip(newThis.matrixSequence().zip(newOther.matrixSequence()))) {
             val (a, b) = ab
-            dotTo(a.as2D(), b.as2D(), res.as2D(), l, m1, n)
+            dotTo(a, b, res, l, m1, n)
         }
 
         return if (penultimateDim) {
diff --git a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/linUtils.kt b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/linUtils.kt
index 2fb5b949f..aba6167ce 100644
--- a/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/linUtils.kt
+++ b/kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/internal/linUtils.kt
@@ -54,18 +54,26 @@ internal val <T> BufferedTensor<T>.matrices: VirtualBuffer<BufferedTensor<T>>
 internal fun <T> BufferedTensor<T>.matrixSequence(): Sequence<BufferedTensor<T>> = matrices.asSequence()
 
 internal fun dotTo(
-    a: MutableStructure2D<Double>,
-    b: MutableStructure2D<Double>,
-    res: MutableStructure2D<Double>,
+    a: BufferedTensor<Double>,
+    b: BufferedTensor<Double>,
+    res: BufferedTensor<Double>,
     l: Int, m: Int, n: Int,
 ) {
+    val aStart = a.bufferStart
+    val bStart = b.bufferStart
+    val resStart = res.bufferStart
+
+    val aBuffer = a.mutableBuffer
+    val bBuffer = b.mutableBuffer
+    val resBuffer = res.mutableBuffer
+
     for (i in 0 until l) {
         for (j in 0 until n) {
             var curr = 0.0
             for (k in 0 until m) {
-                curr += a[i, k] * b[k, j]
+                curr += aBuffer[aStart + i * m + k] * bBuffer[bStart + k * n + j]
             }
-            res[i, j] = curr
+            resBuffer[resStart + i * n + j] = curr
         }
     }
 }
diff --git a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
index 03357f1e1..205ae2fee 100644
--- a/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
+++ b/kmath-tensors/src/commonTest/kotlin/space/kscience/kmath/tensors/core/TestDoubleTensorAlgebra.kt
@@ -107,6 +107,8 @@ internal class TestDoubleTensorAlgebra {
         val tensor11 = fromArray(intArrayOf(3, 2), doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0))
         val tensor2 = fromArray(intArrayOf(3), doubleArrayOf(10.0, 20.0, 30.0))
         val tensor3 = fromArray(intArrayOf(1, 1, 3), doubleArrayOf(-1.0, -2.0, -3.0))
+        val tensor4 = fromArray(intArrayOf(2, 3, 3), (1..18).map { it.toDouble() }.toDoubleArray())
+        val tensor5 = fromArray(intArrayOf(2, 3, 3), (1..18).map { 1 + it.toDouble() }.toDoubleArray())
 
         val res12 = tensor1.dot(tensor2)
         assertTrue(res12.mutableBuffer.array() contentEquals doubleArrayOf(140.0, 320.0))
@@ -123,6 +125,13 @@ internal class TestDoubleTensorAlgebra {
         val res11 = tensor1.dot(tensor11)
         assertTrue(res11.mutableBuffer.array() contentEquals doubleArrayOf(22.0, 28.0, 49.0, 64.0))
         assertTrue(res11.shape contentEquals intArrayOf(2, 2))
+
+        val res45 = tensor4.dot(tensor5)
+        assertTrue(res45.mutableBuffer.array() contentEquals doubleArrayOf(
+            36.0, 42.0, 48.0, 81.0, 96.0, 111.0, 126.0, 150.0, 174.0,
+            468.0, 501.0, 534.0, 594.0, 636.0, 678.0, 720.0, 771.0, 822.0
+        ))
+        assertTrue(res45.shape contentEquals intArrayOf(2, 3, 3))
     }
 
     @Test