diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
index 6709bd79bc820..df088255f1bae 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.mllib.regression
 
 import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.mllib.feature.StandardScaler
 import org.apache.spark.{Logging, SparkException}
 import org.apache.spark.rdd.RDD
@@ -39,6 +40,8 @@ import org.apache.spark.storage.StorageLevel
 abstract class GeneralizedLinearModel(val weights: Vector, val intercept: Double)
   extends Serializable {
 
+  private var bcWeights: Option[Broadcast[Vector]] = None
+
   /**
    * Predict the result given a data point and the weights learned.
    *
@@ -57,11 +60,17 @@ abstract class GeneralizedLinearModel(val weights: Vector, val intercept: Double
   def predict(testData: RDD[Vector]): RDD[Double] = {
     // A small optimization to avoid serializing the entire model. Only the weightsMatrix
     // and intercept is needed.
-    val localWeights = weights
-    val bcWeights = testData.context.broadcast(localWeights)
+    bcWeights match {
+      case None => {
+        val localWeights = weights
+        bcWeights = Some(testData.context.broadcast(localWeights))
+      }
+      case _ =>
+    }
+    val localBcWeights = bcWeights
     val localIntercept = intercept
     testData.mapPartitions { iter =>
-      val w = bcWeights.value
+      val w = localBcWeights.get.value
       iter.map(v => predictPoint(v, w, localIntercept))
     }
   }