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features
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support

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Description

The Intel® Distribution of OpenVINO™ toolkit serves as an open-source AI development resource that speeds up inference on various Intel hardware platforms. This toolkit is crafted to enhance AI workflows, enabling developers to implement refined deep learning models tailored for applications in computer vision, generative AI, and large language models (LLMs). Equipped with integrated model optimization tools, it guarantees elevated throughput and minimal latency while decreasing the model size without sacrificing accuracy. OpenVINO™ is an ideal choice for developers aiming to implement AI solutions in diverse settings, spanning from edge devices to cloud infrastructures, thereby assuring both scalability and peak performance across Intel architectures. Ultimately, its versatile design supports a wide range of AI applications, making it a valuable asset in modern AI development.

Description

VLLM is an advanced library tailored for the efficient inference and deployment of Large Language Models (LLMs). Initially created at the Sky Computing Lab at UC Berkeley, it has grown into a collaborative initiative enriched by contributions from both academic and industry sectors. The library excels in providing exceptional serving throughput by effectively handling attention key and value memory through its innovative PagedAttention mechanism. It accommodates continuous batching of incoming requests and employs optimized CUDA kernels, integrating technologies like FlashAttention and FlashInfer to significantly improve the speed of model execution. Furthermore, VLLM supports various quantization methods, including GPTQ, AWQ, INT4, INT8, and FP8, and incorporates speculative decoding features. Users enjoy a seamless experience by integrating easily with popular Hugging Face models and benefit from a variety of decoding algorithms, such as parallel sampling and beam search. Additionally, VLLM is designed to be compatible with a wide range of hardware, including NVIDIA GPUs, AMD CPUs and GPUs, and Intel CPUs, ensuring flexibility and accessibility for developers across different platforms. This broad compatibility makes VLLM a versatile choice for those looking to implement LLMs efficiently in diverse environments.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Hugging Face
PyTorch
Caffe
Cameralyze
Docker
EndoAim
Intel Geti
Intel Open Edge Platform
KServe
Keras
Kubernetes
LaunchX
NGINX
NVIDIA DRIVE
Nero AI
ONNX
OpenAI
PaddlePaddle
TensorFlow

Integrations

Hugging Face
PyTorch
Caffe
Cameralyze
Docker
EndoAim
Intel Geti
Intel Open Edge Platform
KServe
Keras
Kubernetes
LaunchX
NGINX
NVIDIA DRIVE
Nero AI
ONNX
OpenAI
PaddlePaddle
TensorFlow

Pricing Details

Free
Free Trial
Free Version

Pricing Details

No price information available.
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/tools/openvino-toolkit/overview.html

Vendor Details

Company Name

VLLM

Country

United States

Website

docs.vllm.ai/en/latest/

Product Features

Deep Learning

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

Product Features

Alternatives

Alternatives

OpenVINO Reviews

OpenVINO

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DeepSpeed Reviews

DeepSpeed

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