diff --git a/docs/status.md b/docs/status.md
index 99759bcf60c..674ef76d471 100644
--- a/docs/status.md
+++ b/docs/status.md
@@ -61,7 +61,7 @@ one of the following tracking labels.
 | ceil                     | yes           | yes          | yes            | yes             | yes         |
 | cholesky                 | yes           | yes          | yes            | yes             | revisit     |
 | clamp                    | yes           | revisit      | yes            | yes             | yes         |
-| collective_broadcast     | yes           | revisit      | yes            | no              | no          |
+| collective_broadcast     | yes           | revisit      | yes            | no              | yes         |
 | collective_permute       | yes           | revisit      | yes            | no              | yes         |
 | compare                  | yes           | yes          | yes            | yes             | yes         |
 | complex                  | yes           | yes          | yes            | yes             | yes         |
diff --git a/stablehlo/reference/Ops.cpp b/stablehlo/reference/Ops.cpp
index 0d8ead15768..ee3ce800c08 100644
--- a/stablehlo/reference/Ops.cpp
+++ b/stablehlo/reference/Ops.cpp
@@ -328,6 +328,25 @@ SmallVector<InterpreterValue> eval(Region &region,
       auto operand = scope.findTensor(clzOp.getOperand());
       auto result = evalClzOp(operand, clzOp.getType());
       scope.add(clzOp.getResult(), result);
+    } else if (auto collectiveBroadcastOp =
+                   dyn_cast<CollectiveBroadcastOp>(op)) {
+      auto operand = scope.findTensor(collectiveBroadcastOp.getOperand());
+
+      auto replicaGroupsAttr = collectiveBroadcastOp.getReplicaGroups();
+      auto replicaGroupsShape = replicaGroupsAttr.getShapedType().getShape();
+      SmallVector<SmallVector<uint32_t>> replicaGroups(replicaGroupsShape[0]);
+      auto replicaGroupsIt = replicaGroupsAttr.getValues<int64_t>().begin();
+      for (auto &replicaGroup : replicaGroups)
+        for (auto i = 0; i < replicaGroupsShape[1]; ++i, ++replicaGroupsIt)
+          replicaGroup.push_back(*replicaGroupsIt);
+
+      ChannelId channelId = 0;
+      if (auto channelHandle = collectiveBroadcastOp.getChannelHandle())
+        channelId = channelHandle->getHandle();
+
+      auto result =
+          evalCollectiveBroadcastOp(operand, replicaGroups, channelId, process);
+      scope.add(collectiveBroadcastOp.getResult(), result);
     } else if (auto collectivePermuteOp = dyn_cast<CollectivePermuteOp>(op)) {
       auto operand = scope.findTensor(collectivePermuteOp.getOperand());
 
@@ -1074,6 +1093,28 @@ Tensor evalClzOp(const Tensor &operand, ShapedType resultType) {
   return result;
 }
 
+Tensor evalCollectiveBroadcastOp(
+    const Tensor &operand, SmallVector<SmallVector<uint32_t>> replicaGroups,
+    ChannelId channelId, Process *process) {
+  if (!process)
+    llvm::report_fatal_error(
+        "collective_broadcast is only supported when run via "
+        "interpreter.run_parallel");
+
+  ProcessGroups processGroups;
+  if (channelId <= 0) processGroups = process->crossReplica(replicaGroups);
+  if (channelId > 0) processGroups = process->crossPartition(replicaGroups);
+
+  auto processGroup = processGroups.findGroup(process->getId());
+  if (processGroup)
+    return process->rendezvous(*processGroup, channelId, operand)
+        .lookup((*processGroup)[0]);
+
+  return evalBroadcastInDimOp(
+      makeScalar(convert(operand.getElementType(), 0.0)), {},
+      operand.getType());
+}
+
 Tensor evalCollectivePermuteOp(
     const Tensor &operand, SmallVector<SmallVector<uint32_t>> sourceTargetPairs,
     ChannelId channelId, Process *process) {
diff --git a/stablehlo/reference/Ops.h b/stablehlo/reference/Ops.h
index b85c57d1de0..6b58066caae 100644
--- a/stablehlo/reference/Ops.h
+++ b/stablehlo/reference/Ops.h
@@ -62,6 +62,9 @@ Tensor evalCeilOp(const Tensor &operand, ShapedType resultType);
 Tensor evalClampOp(const Tensor &min, const Tensor &operand, const Tensor &max,
                    ShapedType resultType);
 Tensor evalClzOp(const Tensor &operand, ShapedType resultType);
+Tensor evalCollectiveBroadcastOp(
+    const Tensor &operand, SmallVector<SmallVector<uint32_t>> replicaGroups,
+    ChannelId channelId, Process *process);
 Tensor evalCollectivePermuteOp(
     const Tensor &operand, SmallVector<SmallVector<uint32_t>> sourceTargetPairs,
     ChannelId channelId, Process *process);
diff --git a/stablehlo/reference/ProcessGrid.cpp b/stablehlo/reference/ProcessGrid.cpp
index b6fd1f4cca4..72cc4b3ec89 100644
--- a/stablehlo/reference/ProcessGrid.cpp
+++ b/stablehlo/reference/ProcessGrid.cpp
@@ -49,8 +49,8 @@ bool ProcessId::operator==(const ProcessId &other) const {
 
 std::optional<ProcessGroup> ProcessGroups::findGroup(ProcessId processId) {
   for (auto processGroup : *this)
-    for (auto id : processGroup)
-      if (id == processId) return processGroup;
+    if (llvm::find(processGroup, processId) != processGroup.end())
+      return processGroup;
 
   return std::nullopt;
 }
diff --git a/stablehlo/tests/interpret/collective_broadcast.mlir b/stablehlo/tests/interpret/collective_broadcast.mlir
new file mode 100644
index 00000000000..bb2e5f4f658
--- /dev/null
+++ b/stablehlo/tests/interpret/collective_broadcast.mlir
@@ -0,0 +1,223 @@
+// RUN: stablehlo-translate --interpret -split-input-file %s
+
+module @cross_replica {
+  func.func @collective_broadcast(%operand : tensor<1x2xi64>) -> tensor<1x2xi64> {
+    %result = "stablehlo.collective_broadcast"(%operand) {
+      replica_groups = dense<[[2, 1]]> : tensor<1x2xi64>,
+      channel_handle = #stablehlo.channel_handle<handle = 0, type = 0>
+    } : (tensor<1x2xi64>) -> tensor<1x2xi64>
+    return %result : tensor<1x2xi64>
+  }
+  func.func @main() {
+    %operand0 = stablehlo.constant dense<[[1, 2]]> : tensor<1x2xi64>
+    %operand1 = stablehlo.constant dense<[[3, 4]]> : tensor<1x2xi64>
+    %operand2 = stablehlo.constant dense<[[5, 6]]> : tensor<1x2xi64>
+    %operand3 = stablehlo.constant dense<[[7, 8]]> : tensor<1x2xi64>
+    %results:4 = "interpreter.run_parallel"(%operand0, %operand1, %operand2, %operand3) {
+      programs=[[@collective_broadcast], [@collective_broadcast],
+                [@collective_broadcast], [@collective_broadcast]]
+    } : (tensor<1x2xi64>, tensor<1x2xi64>, tensor<1x2xi64>, tensor<1x2xi64>) ->
+        (tensor<1x2xi64>, tensor<1x2xi64>, tensor<1x2xi64>, tensor<1x2xi64>)
+    check.expect_eq_const %results#0, dense<[[0, 0]]> : tensor<1x2xi64>
+    check.expect_eq_const %results#1, dense<[[5, 6]]> : tensor<1x2xi64>
+    check.expect_eq_const %results#2, dense<[[5, 6]]> : tensor<1x2xi64>
+    check.expect_eq_const %results#3, dense<[[0, 0]]> : tensor<1x2xi64>
+    func.return
+  }
+}
+
+// -----
+
+module @cross_replica_multiple_output {
+  func.func @collective_broadcast(%operand : tensor<1x2xi64>) -> tensor<1x2xi64> {
+    %result = "stablehlo.collective_broadcast"(%operand) {
+      replica_groups = dense<[[2, 1, 0]]> : tensor<1x3xi64>,
+      channel_handle = #stablehlo.channel_handle<handle = 0, type = 0>
+    } : (tensor<1x2xi64>) -> tensor<1x2xi64>
+    return %result : tensor<1x2xi64>
+  }
+  func.func @main() {
+    %operand0 = stablehlo.constant dense<[[1, 2]]> : tensor<1x2xi64>
+    %operand1 = stablehlo.constant dense<[[3, 4]]> : tensor<1x2xi64>
+    %operand2 = stablehlo.constant dense<[[5, 6]]> : tensor<1x2xi64>
+    %operand3 = stablehlo.constant dense<[[7, 8]]> : tensor<1x2xi64>
+    %results:4 = "interpreter.run_parallel"(%operand0, %operand1, %operand2, %operand3) {
+      programs=[[@collective_broadcast], [@collective_broadcast],
+                [@collective_broadcast], [@collective_broadcast]]
+    } : (tensor<1x2xi64>, tensor<1x2xi64>, tensor<1x2xi64>, tensor<1x2xi64>) ->
+        (tensor<1x2xi64>, tensor<1x2xi64>, tensor<1x2xi64>, tensor<1x2xi64>)
+    check.expect_eq_const %results#0, dense<[[5, 6]]> : tensor<1x2xi64>
+    check.expect_eq_const %results#1, dense<[[5, 6]]> : tensor<1x2xi64>
+    check.expect_eq_const %results#2, dense<[[5, 6]]> : tensor<1x2xi64>
+    check.expect_eq_const %results#3, dense<[[0, 0]]> : tensor<1x2xi64>
+    func.return
+  }
+}
+
+// -----
+
+module @cross_replica_single_replica {
+  func.func @collective_broadcast(%operand : tensor<1x2xi64>) -> tensor<1x2xi64> {
+    %result = "stablehlo.collective_broadcast"(%operand) {
+      replica_groups = dense<[[0]]> : tensor<1x1xi64>,
+      channel_handle = #stablehlo.channel_handle<handle = 0, type = 0>
+    } : (tensor<1x2xi64>) -> tensor<1x2xi64>
+    return %result : tensor<1x2xi64>
+  }
+  func.func @main() {
+    %operand0 = stablehlo.constant dense<[[1, 2]]> : tensor<1x2xi64>
+    %operand1 = stablehlo.constant dense<[[3, 4]]> : tensor<1x2xi64>
+    %operand2 = stablehlo.constant dense<[[5, 6]]> : tensor<1x2xi64>
+    %operand3 = stablehlo.constant dense<[[7, 8]]> : tensor<1x2xi64>
+    %results:4 = "interpreter.run_parallel"(%operand0, %operand1, %operand2, %operand3) {
+      programs=[[@collective_broadcast, @collective_broadcast,
+                 @collective_broadcast, @collective_broadcast]]
+    } : (tensor<1x2xi64>, tensor<1x2xi64>, tensor<1x2xi64>, tensor<1x2xi64>) ->
+        (tensor<1x2xi64>, tensor<1x2xi64>, tensor<1x2xi64>, tensor<1x2xi64>)
+    check.expect_eq_const %results#0, dense<[[1, 2]]> : tensor<1x2xi64>
+    check.expect_eq_const %results#1, dense<[[3, 4]]> : tensor<1x2xi64>
+    check.expect_eq_const %results#2, dense<[[5, 6]]> : tensor<1x2xi64>
+    check.expect_eq_const %results#3, dense<[[7, 8]]> : tensor<1x2xi64>
+    func.return
+  }
+}
+
+// -----
+
+module @cross_replica_multiple_partitions {
+  func.func @collective_broadcast(%operand : tensor<1x2xi64>) -> tensor<1x2xi64> {
+    %result = "stablehlo.collective_broadcast"(%operand) {
+      replica_groups = dense<[[1, 0]]> : tensor<1x2xi64>,
+      channel_handle = #stablehlo.channel_handle<handle = 0, type = 0>
+    } : (tensor<1x2xi64>) -> tensor<1x2xi64>
+    return %result : tensor<1x2xi64>
+  }
+  func.func @main() {
+    %operand0 = stablehlo.constant dense<[[1, 2]]> : tensor<1x2xi64>
+    %operand1 = stablehlo.constant dense<[[3, 4]]> : tensor<1x2xi64>
+    %operand2 = stablehlo.constant dense<[[5, 6]]> : tensor<1x2xi64>
+    %operand3 = stablehlo.constant dense<[[7, 8]]> : tensor<1x2xi64>
+    %results:4 = "interpreter.run_parallel"(%operand0, %operand1, %operand2, %operand3) {
+      programs=[[@collective_broadcast, @collective_broadcast],
+                [@collective_broadcast, @collective_broadcast]]
+    } : (tensor<1x2xi64>, tensor<1x2xi64>, tensor<1x2xi64>, tensor<1x2xi64>) ->
+        (tensor<1x2xi64>, tensor<1x2xi64>, tensor<1x2xi64>, tensor<1x2xi64>)
+    check.expect_eq_const %results#0, dense<[[5, 6]]> : tensor<1x2xi64>
+    check.expect_eq_const %results#1, dense<[[7, 8]]> : tensor<1x2xi64>
+    check.expect_eq_const %results#2, dense<[[5, 6]]> : tensor<1x2xi64>
+    check.expect_eq_const %results#3, dense<[[7, 8]]> : tensor<1x2xi64>
+    func.return
+  }
+}
+
+// -----
+
+module @cross_partition {
+  func.func @collective_broadcast(%operand : tensor<1x2xi64>) -> tensor<1x2xi64> {
+    %result = "stablehlo.collective_broadcast"(%operand) {
+      replica_groups = dense<[[2, 1]]> : tensor<1x2xi64>,
+      channel_handle = #stablehlo.channel_handle<handle = 1, type = 0>
+    } : (tensor<1x2xi64>) -> tensor<1x2xi64>
+    return %result : tensor<1x2xi64>
+  }
+  func.func @main() {
+    %operand0 = stablehlo.constant dense<[[1, 2]]> : tensor<1x2xi64>
+    %operand1 = stablehlo.constant dense<[[3, 4]]> : tensor<1x2xi64>
+    %operand2 = stablehlo.constant dense<[[5, 6]]> : tensor<1x2xi64>
+    %operand3 = stablehlo.constant dense<[[7, 8]]> : tensor<1x2xi64>
+    %results:4 = "interpreter.run_parallel"(%operand0, %operand1, %operand2, %operand3) {
+      programs=[[@collective_broadcast, @collective_broadcast,
+                 @collective_broadcast, @collective_broadcast]]
+    } : (tensor<1x2xi64>, tensor<1x2xi64>, tensor<1x2xi64>, tensor<1x2xi64>) ->
+        (tensor<1x2xi64>, tensor<1x2xi64>, tensor<1x2xi64>, tensor<1x2xi64>)
+    check.expect_eq_const %results#0, dense<[[0, 0]]> : tensor<1x2xi64>
+    check.expect_eq_const %results#1, dense<[[5, 6]]> : tensor<1x2xi64>
+    check.expect_eq_const %results#2, dense<[[5, 6]]> : tensor<1x2xi64>
+    check.expect_eq_const %results#3, dense<[[0, 0]]> : tensor<1x2xi64>
+    func.return
+  }
+}
+
+// -----
+
+module @cross_partition_multiple_output {
+  func.func @collective_broadcast(%operand : tensor<1x2xi64>) -> tensor<1x2xi64> {
+    %result = "stablehlo.collective_broadcast"(%operand) {
+      replica_groups = dense<[[2, 1, 0]]> : tensor<1x3xi64>,
+      channel_handle = #stablehlo.channel_handle<handle = 1, type = 0>
+    } : (tensor<1x2xi64>) -> tensor<1x2xi64>
+    return %result : tensor<1x2xi64>
+  }
+  func.func @main() {
+    %operand0 = stablehlo.constant dense<[[1, 2]]> : tensor<1x2xi64>
+    %operand1 = stablehlo.constant dense<[[3, 4]]> : tensor<1x2xi64>
+    %operand2 = stablehlo.constant dense<[[5, 6]]> : tensor<1x2xi64>
+    %operand3 = stablehlo.constant dense<[[7, 8]]> : tensor<1x2xi64>
+    %results:4 = "interpreter.run_parallel"(%operand0, %operand1, %operand2, %operand3) {
+      programs=[[@collective_broadcast, @collective_broadcast,
+                 @collective_broadcast, @collective_broadcast]]
+    } : (tensor<1x2xi64>, tensor<1x2xi64>, tensor<1x2xi64>, tensor<1x2xi64>) ->
+        (tensor<1x2xi64>, tensor<1x2xi64>, tensor<1x2xi64>, tensor<1x2xi64>)
+    check.expect_eq_const %results#0, dense<[[5, 6]]> : tensor<1x2xi64>
+    check.expect_eq_const %results#1, dense<[[5, 6]]> : tensor<1x2xi64>
+    check.expect_eq_const %results#2, dense<[[5, 6]]> : tensor<1x2xi64>
+    check.expect_eq_const %results#3, dense<[[0, 0]]> : tensor<1x2xi64>
+    func.return
+  }
+}
+
+// -----
+
+module @cross_partition_single_partition {
+  func.func @collective_broadcast(%operand : tensor<1x2xi64>) -> tensor<1x2xi64> {
+    %result = "stablehlo.collective_broadcast"(%operand) {
+      replica_groups = dense<[[0]]> : tensor<1x1xi64>,
+      channel_handle = #stablehlo.channel_handle<handle = 1, type = 0>
+    } : (tensor<1x2xi64>) -> tensor<1x2xi64>
+    return %result : tensor<1x2xi64>
+  }
+  func.func @main() {
+    %operand0 = stablehlo.constant dense<[[1, 2]]> : tensor<1x2xi64>
+    %operand1 = stablehlo.constant dense<[[3, 4]]> : tensor<1x2xi64>
+    %operand2 = stablehlo.constant dense<[[5, 6]]> : tensor<1x2xi64>
+    %operand3 = stablehlo.constant dense<[[7, 8]]> : tensor<1x2xi64>
+    %results:4 = "interpreter.run_parallel"(%operand0, %operand1, %operand2, %operand3) {
+      programs=[[@collective_broadcast], [@collective_broadcast],
+                [@collective_broadcast], [@collective_broadcast]]
+    } : (tensor<1x2xi64>, tensor<1x2xi64>, tensor<1x2xi64>, tensor<1x2xi64>) ->
+        (tensor<1x2xi64>, tensor<1x2xi64>, tensor<1x2xi64>, tensor<1x2xi64>)
+    check.expect_eq_const %results#0, dense<[[1, 2]]> : tensor<1x2xi64>
+    check.expect_eq_const %results#1, dense<[[3, 4]]> : tensor<1x2xi64>
+    check.expect_eq_const %results#2, dense<[[5, 6]]> : tensor<1x2xi64>
+    check.expect_eq_const %results#3, dense<[[7, 8]]> : tensor<1x2xi64>
+    func.return
+  }
+}
+
+// -----
+
+module @cross_partition_multiple_replicas {
+  func.func @collective_broadcast(%operand : tensor<1x2xi64>) -> tensor<1x2xi64> {
+    %result = "stablehlo.collective_broadcast"(%operand) {
+      replica_groups = dense<[[1, 0]]> : tensor<1x2xi64>,
+      channel_handle = #stablehlo.channel_handle<handle = 1, type = 0>
+    } : (tensor<1x2xi64>) -> tensor<1x2xi64>
+    return %result : tensor<1x2xi64>
+  }
+  func.func @main() {
+    %operand0 = stablehlo.constant dense<[[1, 2]]> : tensor<1x2xi64>
+    %operand1 = stablehlo.constant dense<[[3, 4]]> : tensor<1x2xi64>
+    %operand2 = stablehlo.constant dense<[[5, 6]]> : tensor<1x2xi64>
+    %operand3 = stablehlo.constant dense<[[7, 8]]> : tensor<1x2xi64>
+    %results:4 = "interpreter.run_parallel"(%operand0, %operand1, %operand2, %operand3) {
+      programs=[[@collective_broadcast, @collective_broadcast],
+                [@collective_broadcast, @collective_broadcast]]
+    } : (tensor<1x2xi64>, tensor<1x2xi64>, tensor<1x2xi64>, tensor<1x2xi64>) ->
+        (tensor<1x2xi64>, tensor<1x2xi64>, tensor<1x2xi64>, tensor<1x2xi64>)
+    check.expect_eq_const %results#0, dense<[[3, 4]]> : tensor<1x2xi64>
+    check.expect_eq_const %results#1, dense<[[3, 4]]> : tensor<1x2xi64>
+    check.expect_eq_const %results#2, dense<[[7, 8]]> : tensor<1x2xi64>
+    check.expect_eq_const %results#3, dense<[[7, 8]]> : tensor<1x2xi64>
+    func.return
+  }
+}