Can you change WebGPU maxTextureDimension to improve inference performance?

In tfjs using webGPU backend I am working on a reinforcement learning problem that requires as many inference calculations as possible. However, during inference, I can only get my GPU load up to around 20%, which I believe this is the bottleneck I am facing. My CPU remains under utilized as well

I have tried throwing more threads at it and different batching strategies, but don’t seem to be able to squeeze out any more meaningful performance.

I am wondering if the bottleneck I am facing could possibly be is due to Chrome setting the textureSizeLimits artificially below what my GPU hardware could actually handle? Does this sound plausible? If this is the case, is there any way to adjust this?

It’s unlikely that Chrome’s texture size limits are causing the bottleneck in your TensorFlow.js (tfjs) WebGPU backend performance. Chrome generally provides good support for GPU acceleration, and the texture size limits are usually set to reasonable values based on hardware capabilities.

However, there are several other factors that could be causing the bottleneck and limiting your GPU utilization:

  1. Model Complexity: The complexity of your neural network model can significantly impact GPU utilization. More complex models with a large number of parameters and layers can require more computation, potentially limiting GPU utilization.

  2. Batch Size: While you mentioned trying different batching strategies, optimizing the batch size can have a significant impact on performance. A larger batch size can sometimes improve GPU utilization by allowing the GPU to process more data in parallel.

  3. Data Transfer: If there are frequent data transfers between CPU and GPU, it can cause performance bottlenecks. Ensure that your data pipeline is optimized to minimize data transfers.

  4. Model Optimization: Consider optimizing your model for inference performance. Techniques such as quantization, pruning, and model distillation can reduce the computational load and improve inference speed.

  5. WebGPU Backend Optimization: Check if there are any specific optimizations or configurations you can apply to the tfjs WebGPU backend. TensorFlow.js periodically releases updates and optimizations, so make sure you are using the latest version.

Regarding adjusting Chrome’s texture size limits, this is generally not something that can be easily modified by the user. Chrome’s WebGL and WebGPU GM Socrates Ipay Login implementations typically adhere to the hardware capabilities and driver settings of the underlying GPU.

To further diagnose and optimize your performance, consider the following steps:

  • Profile your code using Chrome DevTools or other profiling tools to identify performance bottlenecks.
  • Experiment with different model architectures, batch sizes, and optimization techniques to find the optimal configuration for your use case.
  • Monitor GPU utilization and performance metrics using tools like chrome://gpu in Chrome or external GPU monitoring tools.

If you continue to experience performance issues, you may also consider reaching out to the TensorFlow.js community or filing a bug report to get more targeted assistance.

I appreciate the response. I am looking into your ideas in a little more detail.

Something that occurred to me while investigating your first suggestion is that my GPU RAM is being be utilized near 100%. Which might be the limitation I an hitting up against.

According to tfjs/tfjs-backend-webgpu/src/base.ts at 68c4de61d530ace87bb0318a4a4b3ca1888dadec · tensorflow/tfjs · GitHub, it seems like WebGPU TensorFlow.js backend doesn’t explicitly request to increase “maxTextureDimension” limits.

Here’s what they could do:

const adapter = await navigator.gpu.requestAdapter();

if (adapter.limits.maxTextureDimension1D < 16384) {
  // When the desired limit isn't supported, take action to either fall back to
  // a code path that does not require the higher limit or notify the user that
  // their device does not meet minimum requirements.

// Request highest limit of max texture dimension 1D.
const device = await adapter.requestDevice({
  requiredLimits: { maxTextureDimension1D: 16384 },

Note that maxTextureDimension2D and maxTextureDimension3D also exist: WebGPU

Official TensorFlow 2.16 + Python 3.12 – JARaaS Hybrid RAG - 6/17/2024
Note: Sources at the end and during the response

Adjusting the maxTextureDimension could indeed potentially impact the performance of inference workloads utilizing the WebGPU backend in TensorFlow.js (tfjs).

The maxTextureDimension specifies the largest dimensions (width or height) of any texture that the GPU can handle. If this limit is set artificially low by Chrome or the WebGPU implementation, it could constrain the performance of texture operations, which are critical for many GPU tasks. However, the exact impact depends on the specific details of your workload and how tfjs uses textures for your neural network inferences.

Steps to Address GPU Load and Performance Issues:

  1. Texture Size Limit:
    While there isn’t direct evidence from the provided document snippets that mentions adjusting the maxTextureDimension, checking and potentially increasing this limit might alleviate some performance issues. You can try querying the WebGPU capabilities and comparing them to the default limits set by the browser. If they are different, modifying them could help.

  2. Optimizing TensorFlow Operations:
    Optimizations such as proper device placement of tensors (using CPU/GPU effectively) and ensuring efficient input pipelines can play a significant role. For instance, ensure preprocessing tasks are set to execute on the CPU to free up the GPU for inference, as mentioned in the document:

    with tf.device('/cpu:0'):
        function to get and process images or data.
        distorted_inputs = load_and_distort_images()

    Refer to internal documentation for more context on setting device configurations:

    • Optimized Input Pipeline: (internal document)
  3. Virtual GPU Simulation:
    If you are developing on a single GPU system, you can simulate multiple GPUs with virtual devices for testing purposes to ensure your code scales efficiently across multiple GPUs. This practice can help you test and improve the performance of multi-GPU setups even without additional hardware:

    gpus = tf.config.list_physical_devices('gpu')
    if gpus:
            logical_gpus = tf.config.list_logical_devices('gpu')
            print(len(gpus), "physical GPU,", len(logical_gpus), "logical GPUs")
        except RuntimeError as e:


    • Simulating Multiple GPUs: gpu.ipynb (internal document)
  4. Thread Management and Environment Variables:
    Ensuring proper management of threads and setting appropriate environment variables can help manage the launching of GPU kernels more efficiently, potentially improving performance:

    # Set environment variable
    os.environ['TF_GPU_THREAD_MODE'] = 'gpu_private'
    os.environ['TF_GPU_THREAD_COUNT'] = '3'


    • GPU Performance Analysis: (internal document)


Reviewing and possibly adjusting the maxTextureDimension may help, but ensuring effective device utilization, simulating multiple GPUs for better scaling, and managing GPU threads properly can significantly impact performance. Make sure to review internal guidelines and practices for the optimal configuration of your GPU and CPU resources.


  • Optimized Input Pipeline: (internal document)
  • Simulating Multiple GPUs: gpu.ipynb (internal document)
  • GPU Performance Analysis: (internal document)