diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
index 8890662d99b52..33b5c5fa3f465 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.mllib.linalg.distributed
 
 import breeze.linalg.{DenseMatrix => BDM}
+import breeze.linalg.{DenseVector => BDV}
 
 import org.apache.spark.annotation.Since
 import org.apache.spark.mllib.linalg._
@@ -244,11 +245,30 @@ class IndexedRowMatrix @Since("1.0.0") (
    */
   @Since("1.0.0")
   def multiply(B: Matrix): IndexedRowMatrix = {
-    val mat = toRowMatrix().multiply(B)
-    val indexedRows = rows.map(_.index).zip(mat.rows).map { case (i, v) =>
-      IndexedRow(i, v)
+    val n = numCols().toInt
+    val k = B.numCols
+    require(n == B.numRows, s"Dimension mismatch: $n vs ${B.numRows}")
+
+    require(B.isInstanceOf[DenseMatrix],
+      s"Only support dense matrix at this time but found ${B.getClass.getName}.")
+
+    val Bb = rows.context.broadcast(B.asBreeze.asInstanceOf[BDM[Double]].toDenseVector.toArray)
+    val AB = rows.mapPartitions { iter =>
+      val Bi = Bb.value
+      iter.map { row =>
+        val index = row.index
+        val vector = row.vector
+        val v = BDV.zeros[Double](k)
+        var i = 0
+        while (i < k) {
+          v(i) = vector.asBreeze.dot(new BDV(Bi, i * n, 1, n))
+          i += 1
+        }
+        IndexedRow(index, Vectors.fromBreeze(v))
+      }
     }
-    new IndexedRowMatrix(indexedRows, nRows, B.numCols)
+
+    new IndexedRowMatrix(AB, 0L, B.numCols)
   }
 
   /**
@@ -274,3 +294,4 @@ class IndexedRowMatrix @Since("1.0.0") (
     mat
   }
 }
+