AIDir.app
  • Hot AI Tools
  • New AI Tools
  • AI Tools Category
AIDir.app
AIDir.app

Save this website for future use! Free to use, no login required.

About

  • Blog

© 2025 • AIDir.app All rights reserved.

  • Privacy Policy
  • Terms of Service
Home
Model Benchmarking
Model Memory Utility

Model Memory Utility

Calculate memory needed to train AI models

You May Also Like

View All
🥇

DécouvrIR

Leaderboard of information retrieval models in French

11
♻

Converter

Convert and upload model files for Stable Diffusion

3
🧘

Zenml Server

Create and manage ML pipelines with ZenML Dashboard

1
🔥

OPEN-MOE-LLM-LEADERBOARD

Explore and submit models using the LLM Leaderboard

32
🏆

OR-Bench Leaderboard

Evaluate LLM over-refusal rates with OR-Bench

0
🥇

LLM Safety Leaderboard

View and submit machine learning model evaluations

91
🚀

DGEB

Display genomic embedding leaderboard

4
🏎

Export to ONNX

Export Hugging Face models to ONNX

68
🐠

Space That Creates Model Demo Space

Create demo spaces for models on Hugging Face

4
📜

Submission Portal

Evaluate and submit AI model results for Frugal AI Challenge

10
🚀

Intent Leaderboard V12

Display leaderboard for earthquake intent classification models

0
🌸

La Leaderboard

Evaluate open LLMs in the languages of LATAM and Spain.

71

What is Model Memory Utility ?

Model Memory Utility is a tool designed to help developers and researchers calculate the memory requirements for training AI models. It provides a straightforward way to estimate the memory needed based on model architecture, batch size, and optimizer settings. This utility is particularly useful for optimizing model training in environments with limited computational resources.

Features

• Model Architecture Support: Compatible with popular frameworks like TensorFlow, PyTorch, and others.
• Batch Size Calculation: Estimates memory usage based on different batch sizes.
• Optimizer Integration: Accounts for memory overhead from various optimizers.
• Offline Functionality: No internet connection required for calculations.
• Customizable Parameters: Allows users to input specific model configurations.
• Detailed Reports: Provides a breakdown of memory usage for different components.
• Cross-Platform Compatibility: Runs on multiple operating systems, including Windows, Linux, and macOS.

How to use Model Memory Utility ?

  1. Install the Utility: Download and install the Model Memory Utility from the official repository.
  2. Configure Model Settings: Input the architecture, batch size, and optimizer details.
  3. Run the Benchmark: Execute the utility to calculate memory requirements.
  4. Review the Report: Analyze the generated report to understand memory distribution and bottlenecks.
  5. Optimize Settings: Adjust parameters based on the report to reduce memory usage.

Frequently Asked Questions

What frameworks does Model Memory Utility support?
Model Memory Utility supports TensorFlow, PyTorch, and other popular deep learning frameworks.

Do I need to install any additional libraries to use the utility?
No, the utility is self-contained and does not require additional libraries beyond the installation package.

Can I customize the output format of the memory report?
Yes, the utility allows users to choose between CSV, JSON, or plain text formats for the memory report.

Recommended Category

View All
🗣️

Generate speech from text in multiple languages

🖼️

Image

📈

Predict stock market trends

🔍

Detect objects in an image

🎵

Generate music for a video

📊

Data Visualization

🚨

Anomaly Detection

🔖

Put a logo on an image

🤖

Chatbots

🌜

Transform a daytime scene into a night scene

📹

Track objects in video

🖼️

Image Captioning

🎬

Video Generation

✂️

Separate vocals from a music track

✂️

Remove background from a picture