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ease
features
design
support

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Description

Intel’s Gaudi software provides developers with an extensive array of tools, libraries, containers, model references, and documentation designed to facilitate the creation, migration, optimization, and deployment of AI models on Intel® Gaudi® accelerators. This platform streamlines each phase of AI development, encompassing training, fine-tuning, debugging, profiling, and enhancing performance for generative AI (GenAI) and large language models (LLMs) on Gaudi hardware, applicable in both data center and cloud settings. The software features current documentation that includes code samples, best practices, API references, and guides aimed at maximizing the efficiency of Gaudi solutions such as Gaudi 2 and Gaudi 3, while also ensuring compatibility with widely-used frameworks and tools for model portability and scalability. Users have access to performance metrics to evaluate training and inference benchmarks, can leverage community and support resources, and benefit from specialized containers and libraries designed for high-performance AI workloads. Furthermore, Intel's commitment to ongoing updates ensures that developers remain equipped with the latest advancements and optimizations for their AI projects.

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

Screenshots View All

Screenshots View All

Integrations

Hugging Face
IONOS Cloud GPU Servers
Intel Tiber AI Cloud
Kimi K2
Kimi K2.6
MATLAB
NVIDIA Clara
NVIDIA DRIVE
NVIDIA Morpheus
NVIDIA NIM
NVIDIA Riva Studio
NVIDIA virtual GPU
PyTorch
Python
RankGPT
RankLLM
Rosepetal AI
TensorFlow
Thunder Compute
Ultralytics

Integrations

Hugging Face
IONOS Cloud GPU Servers
Intel Tiber AI Cloud
Kimi K2
Kimi K2.6
MATLAB
NVIDIA Clara
NVIDIA DRIVE
NVIDIA Morpheus
NVIDIA NIM
NVIDIA Riva Studio
NVIDIA virtual GPU
PyTorch
Python
RankGPT
RankLLM
Rosepetal AI
TensorFlow
Thunder Compute
Ultralytics

Pricing Details

No price information available.
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

Intel

Founded

1968

Country

United States

Website

www.intel.com/content/www/us/en/developer/platform/gaudi/overview.html

Vendor Details

Company Name

NVIDIA

Founded

1993

Country

United States

Website

developer.nvidia.com/tensorrt

Product Features

Product Features

Alternatives

Alternatives

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OpenVINO

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OpenVINO Reviews

OpenVINO

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