diff --git a/src/target/source/codegen_webgpu.cc b/src/target/source/codegen_webgpu.cc
index 83079a9f0756..1d1df91dc4a4 100644
--- a/src/target/source/codegen_webgpu.cc
+++ b/src/target/source/codegen_webgpu.cc
@@ -125,6 +125,7 @@ runtime::FunctionInfo CodeGenWebGPU::AddFunction(const PrimFunc& f, bool skip_re
   name_supply_->ReserveName("var");
   name_supply_->ReserveName("let");
   name_supply_->ReserveName("const");
+  name_supply_->ReserveName("std");
 
   // skip the first underscore, so SSA variable starts from
   name_supply_->FreshName("v_");
diff --git a/web/emcc/wasm_runtime.cc b/web/emcc/wasm_runtime.cc
index 2f7135595843..9744750b80db 100644
--- a/web/emcc/wasm_runtime.cc
+++ b/web/emcc/wasm_runtime.cc
@@ -173,5 +173,51 @@ TVM_REGISTER_GLOBAL("tvmjs.runtime.ArrayConcat").set_body([](TVMArgs args, TVMRe
   }
   *ret = Array<ObjectRef>(data);
 });
+
+NDArray ConcatEmbeddings(const std::vector<NDArray>& embeddings) {
+  // Get output shape
+  int64_t hidden_size = embeddings[0]->shape[1];
+  DLDataType dtype = embeddings[0]->dtype;
+  DLDevice device = embeddings[0]->device;
+  int seqLen = 0;
+  for (int i = 0; i < embeddings.size(); ++i) {
+    ICHECK_EQ(embeddings[i]->ndim, 2);
+    ICHECK_EQ(embeddings[i]->shape[1], hidden_size);
+    seqLen += embeddings[i]->shape[0];
+  }
+
+  // Create output
+  std::vector<int64_t> shape;
+  shape.push_back(seqLen);
+  shape.push_back(hidden_size);
+  NDArray result = NDArray::Empty(shape, dtype, device);
+
+  // Copy
+  int offset = 0;
+  for (int i = 0; i < embeddings.size(); i++) {
+    const DLTensor& copy_src = *(embeddings[i].operator->());
+    const DLTensor* p_copy_dst = result.operator->();
+    DLTensor copy_dst = *p_copy_dst;
+    copy_dst.shape = embeddings[i]->shape;
+    copy_dst.byte_offset =
+        offset * hidden_size * ((embeddings[i]->dtype.bits * embeddings[i]->dtype.lanes + 7) / 8);
+    NDArray::CopyFromTo(&copy_src, &copy_dst);
+    offset += embeddings[i]->shape[0];
+  }
+
+  return result;
+}
+
+// Concatenate n NDArrays
+TVM_REGISTER_GLOBAL("tvmjs.runtime.ConcatEmbeddings").set_body([](TVMArgs args, TVMRetValue* ret) {
+  std::vector<NDArray> embeddings;
+  for (int i = 0; i < args.size(); ++i) {
+    ICHECK_EQ(args[i].type_code(), kTVMNDArrayHandle);
+    embeddings.push_back(args[i]);
+  }
+  NDArray result = ConcatEmbeddings(std::move(embeddings));
+  *ret = result;
+});
+
 }  // namespace runtime
 }  // namespace tvm
diff --git a/web/src/runtime.ts b/web/src/runtime.ts
index 600a9b857f03..8546cab773ff 100644
--- a/web/src/runtime.ts
+++ b/web/src/runtime.ts
@@ -174,6 +174,7 @@ class RuntimeContext implements Disposable {
   applyRepetitionPenalty: PackedFunc;
   applyPresenceAndFrequencyPenalty: PackedFunc;
   applySoftmaxWithTemperature: PackedFunc;
+  concatEmbeddings: PackedFunc | undefined;
 
   private autoDisposeScope: Array<Array<Disposable | undefined>> = [];
 
@@ -199,6 +200,11 @@ class RuntimeContext implements Disposable {
     this.applyRepetitionPenalty = getGlobalFunc("vm.builtin.apply_repetition_penalty");
     this.applyPresenceAndFrequencyPenalty = getGlobalFunc("vm.builtin.apply_presence_and_frequency_penalty");
     this.applySoftmaxWithTemperature = getGlobalFunc("vm.builtin.apply_softmax_with_temperature");
+    try {
+      this.concatEmbeddings = getGlobalFunc("tvmjs.runtime.ConcatEmbeddings");
+    } catch {
+      // TODO: remove soon. Older artifacts do not have this, try-catch for backward compatibility.
+    }
   }
 
   dispose(): void {
@@ -223,6 +229,7 @@ class RuntimeContext implements Disposable {
     this.applyRepetitionPenalty.dispose();
     this.applyPresenceAndFrequencyPenalty.dispose();
     this.applySoftmaxWithTemperature.dispose();
+    this.concatEmbeddings?.dispose();
   }
 
   beginScope(): void {
@@ -575,7 +582,10 @@ export class NDArray implements Disposable {
    * @param data The source data array.
    * @returns this
    */
-  copyFrom(data: NDArray | Array<number> | Float32Array): this {
+  copyFrom(
+    data: NDArray | Array<number> | Float32Array | Float64Array |
+      Int32Array | Int8Array | Uint8Array | Uint8ClampedArray
+  ): this {
     if (data instanceof NDArray) {
       this.lib.checkCall(
         (this.lib.exports.TVMArrayCopyFromTo as ctypes.FTVMArrayCopyFromTo)(
@@ -608,6 +618,8 @@ export class NDArray implements Disposable {
         buffer = Int8Array.from(data).buffer;
       } else if (this.dtype === "uint8") {
         buffer = Uint8Array.from(data).buffer;
+      } else if (this.dtype === "uint32") {
+        buffer = Uint32Array.from(data).buffer;
       } else {
         throw new Error("Unsupported data type " + this.dtype);
       }
@@ -1906,6 +1918,30 @@ export class Instance implements Disposable {
     return this.ctx.arrayConcat(...listOfArrays) as TVMArray;
   }
 
+  /**
+   * Join a sequence of NDArrays that represent embeddings.
+   * @param inputs A list of embeddings in NDArrays, each array i has shape (m_i, hidden_size).
+   * @returns An NDArray of shape (\sum_{i} {m}, hidden_size)
+   */
+  concatEmbeddings(embeddings: Array<NDArray>): NDArray {
+    // 1. Check shape validity
+    const hidden_size = embeddings[0].shape[1];
+    embeddings.forEach((input) => {
+      if (input.shape.length !== 2 || input.shape[1] !== hidden_size) {
+        throw new Error("Expect embeddings to concatenate have shape (m_i, hidden_size).");
+      }
+    })
+
+    // 2. Call global func
+    if (this.ctx.concatEmbeddings === undefined) {
+      throw new Error(
+        "Global function tvmjs.runtime.ConcatEmbeddings was " +
+        "not found, but called concatEmbeddings."
+      );
+    }
+    return this.ctx.concatEmbeddings(...embeddings) as NDArray;
+  }
+
   /**
    * Create a {@link TVMString} that can be consumed by runtime.
    *