Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
This innovative tool is designed for quantizing convolutional neural networks (CNNs). It allows for the transformation of both weights/biases and activations from 32-bit floating-point (FP32) to 8-bit integer (INT8) format, or even other bit depths. Utilizing this tool can greatly enhance inference performance and efficiency, all while preserving accuracy levels. It is compatible with various common layer types found in neural networks, such as convolution, pooling, fully-connected layers, and batch normalization, among others. Remarkably, the quantization process does not require the network to be retrained or the use of labeled datasets; only a single batch of images is sufficient. Depending on the neural network's size, the quantization can be completed in a matter of seconds to several minutes, facilitating quick updates to the model. Furthermore, this tool is specifically optimized for collaboration with DeePhi DPU and can generate the INT8 format model files necessary for DNNC integration. By streamlining the quantization process, developers can ensure their models remain efficient and robust in various applications.
Description
NVIDIA TensorRT is a comprehensive suite of APIs designed for efficient deep learning inference, which includes a runtime for inference and model optimization tools that ensure minimal latency and maximum throughput in production scenarios. Leveraging the CUDA parallel programming architecture, TensorRT enhances neural network models from all leading frameworks, adjusting them for reduced precision while maintaining high accuracy, and facilitating their deployment across a variety of platforms including hyperscale data centers, workstations, laptops, and edge devices. It utilizes advanced techniques like quantization, fusion of layers and tensors, and precise kernel tuning applicable to all NVIDIA GPU types, ranging from edge devices to powerful data centers. Additionally, the TensorRT ecosystem features TensorRT-LLM, an open-source library designed to accelerate and refine the inference capabilities of contemporary large language models on the NVIDIA AI platform, allowing developers to test and modify new LLMs efficiently through a user-friendly Python API. This innovative approach not only enhances performance but also encourages rapid experimentation and adaptation in the evolving landscape of AI applications.
API Access
Has API
API Access
Has API
Integrations
CUDA
Hugging Face
Kimi K2
Kimi K2.5
Kimi K2.6
MATLAB
NVIDIA AI Enterprise
NVIDIA Broadcast
NVIDIA Clara
NVIDIA DeepStream SDK
Integrations
CUDA
Hugging Face
Kimi K2
Kimi K2.5
Kimi K2.6
MATLAB
NVIDIA AI Enterprise
NVIDIA Broadcast
NVIDIA Clara
NVIDIA DeepStream SDK
Pricing Details
$0.90 per hour
Free Trial
Free Version
Pricing Details
Free
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
DeePhi Quantization Tool
Website
aws.amazon.com/marketplace/pp/prodview-bwtx6kzwg3gva
Vendor Details
Company Name
NVIDIA
Founded
1993
Country
United States
Website
developer.nvidia.com/tensorrt