diff --git a/tfjs-tflite/README.md b/tfjs-tflite/README.md
index e3ee1638cca..391c00b2806 100644
--- a/tfjs-tflite/README.md
+++ b/tfjs-tflite/README.md
@@ -93,10 +93,9 @@ if SIMD is not enabled). Without multi-threading support, certain models might
 not achieve the best performance. See [here][cross origin setup steps] for the
 high-level steps to set up the cross-origin isolation.
 
-Setting the number of threads when calling `loadTFLiteModel` can also help with
-the performance. In most cases, the threads count should be the same as the
-number of physical cores, which is half of `navigator.hardwareConcurrency` on
-many x86-64 processors.
+By default, the runtime uses the number of physical cores as the thread count.
+You can tune this number by setting the `numThreads` option when loading the
+TFLite model:
 
 ```js
 const tfliteModel = await tflite.loadTFLiteModel(
@@ -104,6 +103,25 @@ const tfliteModel = await tflite.loadTFLiteModel(
     {numThreads: navigator.hardwareConcurrency / 2});
 ```
 
+# Profiling
+
+Profiling can be enabled by setting the `enableProfiling` option to true when
+loading the TFLite model:
+
+```js
+const tfliteModel = await tflite.loadTFLiteModel(
+    'path/to/your/my_model.tflite',
+    {enableProfiling: true});
+```
+
+Once it is enabled, the runtime will record per-op latency data when the
+`predict` method is called. The profiling results can be retrieved in two ways:
+
+- `tfliteModel.getProfilingResults()`: this method will return an array of
+  `{nodeType, nodeName, execTimeInMs}`.
+- `tfliteModel.getProfilingSummary()`: this method will return a human-readable
+  profiling result summary that looks like [this][profiling summary].
+
 # Development
 
 ## Building
@@ -130,3 +148,4 @@ $ yarn build-npm
 [xnnpack]: https://github.com/google/XNNPACK
 [xnnpack doc]: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/delegates/xnnpack/README.md#limitations-and-supported-operators
 [cross origin setup steps]: https://github.com/tensorflow/tfjs/tree/master/tfjs-backend-wasm#setting-up-cross-origin-isolation
+[profiling summary]: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/tools/benchmark/README.md#profiling-model-operators
diff --git a/tfjs-tflite/scripts/download-tflite-web-api.sh b/tfjs-tflite/scripts/download-tflite-web-api.sh
index 53609852375..4a2fadd2237 100755
--- a/tfjs-tflite/scripts/download-tflite-web-api.sh
+++ b/tfjs-tflite/scripts/download-tflite-web-api.sh
@@ -22,7 +22,7 @@ set -e
 OUTPUT_DIR="$1"
 
 # The default version.
-CURRENT_VERSION=0.0.5
+CURRENT_VERSION=0.0.6
 
 # Get the version from the second parameter.
 # Default to the value in CURRENT_VERSION.
diff --git a/tfjs-tflite/src/tflite_model.ts b/tfjs-tflite/src/tflite_model.ts
index 6dadd4649f4..f45379babdc 100644
--- a/tfjs-tflite/src/tflite_model.ts
+++ b/tfjs-tflite/src/tflite_model.ts
@@ -17,9 +17,8 @@
 
 import {DataType, InferenceModel, ModelPredictConfig, ModelTensorInfo, NamedTensorMap, tensor, Tensor} from '@tensorflow/tfjs-core';
 
-import {getDefaultNumThreads} from './tflite_task_library_client/common';
 import * as tfliteWebAPIClient from './tflite_web_api_client';
-import {TFLiteDataType, TFLiteWebModelRunner, TFLiteWebModelRunnerOptions, TFLiteWebModelRunnerTensorInfo} from './types/tflite_web_model_runner';
+import {ProfileItem, TFLiteDataType, TFLiteWebModelRunner, TFLiteWebModelRunnerOptions, TFLiteWebModelRunnerTensorInfo} from './types/tflite_web_model_runner';
 
 const TFHUB_SEARCH_PARAM = '?lite-format=tflite';
 
@@ -178,6 +177,14 @@ export class TFLiteModel implements InferenceModel {
     throw new Error('execute() of TFLiteModel is not supported yet.');
   }
 
+  getProfilingResults(): ProfileItem[] {
+    return this.modelRunner.getProfilingResults();
+  }
+
+  getProfilingSummary(): string {
+    return this.modelRunner.getProfilingSummary();
+  }
+
   private setModelInputFromTensor(
       modelInput: TFLiteWebModelRunnerTensorInfo, tensor: Tensor) {
     // String and complex tensors are not supported.
@@ -317,17 +324,9 @@ export async function loadTFLiteModel(
     model = `${model}${TFHUB_SEARCH_PARAM}`;
   }
 
-  // Process options.
-  const curOptions: TFLiteWebModelRunnerOptions = {};
-  if (options && options.numThreads !== undefined) {
-    curOptions.numThreads = options.numThreads;
-  } else {
-    curOptions.numThreads = await getDefaultNumThreads();
-  }
-
   const tfliteModelRunner =
       await tfliteWebAPIClient.tfweb.TFLiteWebModelRunner.create(
-          model, curOptions);
+          model, options);
   return new TFLiteModel(tfliteModelRunner);
 }
 
diff --git a/tfjs-tflite/src/tflite_model_test.ts b/tfjs-tflite/src/tflite_model_test.ts
index 30c9e3fd43f..e7d566013d0 100644
--- a/tfjs-tflite/src/tflite_model_test.ts
+++ b/tfjs-tflite/src/tflite_model_test.ts
@@ -19,7 +19,7 @@ import * as tf from '@tensorflow/tfjs-core';
 import {DataType, NamedTensorMap} from '@tensorflow/tfjs-core';
 
 import {TFLiteModel} from './tflite_model';
-import {TFLiteDataType, TFLiteWebModelRunner, TFLiteWebModelRunnerOptions, TFLiteWebModelRunnerTensorInfo} from './types/tflite_web_model_runner';
+import {ProfileItem, TFLiteDataType, TFLiteWebModelRunner, TFLiteWebModelRunnerOptions, TFLiteWebModelRunnerTensorInfo} from './types/tflite_web_model_runner';
 
 // A mock TFLiteWebModelRunner that doubles the data from input tensors to
 // output tensors during inference.
@@ -61,6 +61,14 @@ class MockModelRunner implements TFLiteWebModelRunner {
 
   cleanUp() {}
 
+  getProfilingResults(): ProfileItem[] {
+    return [];
+  }
+
+  getProfilingSummary(): string {
+    return '';
+  }
+
   private getTensorInfos(firstTensorType: TFLiteDataType = 'int32'):
       TFLiteWebModelRunnerTensorInfo[] {
     const shape0 = [1, 2, 3];
diff --git a/tfjs-tflite/src/types/tflite_web_model_runner.ts b/tfjs-tflite/src/types/tflite_web_model_runner.ts
index 820348b5f51..8060973d7f3 100644
--- a/tfjs-tflite/src/types/tflite_web_model_runner.ts
+++ b/tfjs-tflite/src/types/tflite_web_model_runner.ts
@@ -50,6 +50,31 @@ export declare interface TFLiteWebModelRunner extends BaseTaskLibrary {
    * @return Whether the inference is successful or not.
    */
   infer(): boolean;
+
+  /**
+   * Gets per-node profiling results.
+   *
+   * This is only useful when TFLiteWebModelRunnerOptions.enableProfiling is
+   * set to true.
+   */
+  getProfilingResults(): ProfileItem[];
+
+  /**
+   * Gets the profiling summary.
+   *
+   * This is only useful when TFLiteWebModelRunnerOptions.enableProfiling is
+   * set to true.
+   */
+  getProfilingSummary(): string;
+}
+
+export declare interface ProfileItem {
+  /** The type of the node, e.g. "CONV_2D". */
+  nodeType: string;
+  /** The name of the node, e.g. "MobilenetV1/MobilenetV1/Conv2d_0/Relu6". */
+  nodeName: string;
+  /** The execution time (in ms) of the node. */
+  nodeExecMs: number;
 }
 
 /** Options for TFLiteWebModelRunner. */
@@ -61,6 +86,21 @@ export declare interface TFLiteWebModelRunnerOptions {
    * not supported by user's browser.
    */
   numThreads?: number;
+  /**
+   * Whether to enable profiling.
+   *
+   * Default to false. After it is enabled, the profiling results can be
+   * retrieved by calling TFLiteWebModelRunner.getProfilingResults or
+   * TFLiteWebModelRunner.getProfilingSummary. See their comments for more
+   * details.
+   */
+  enableProfiling?: boolean;
+  /**
+   * Maximum nmber of entries that the profiler can keep.
+   *
+   * Default to 1024.
+   */
+  maxProfilingBufferEntries?: number;
 }
 
 /** Types of TFLite tensor data. */