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What is VLLM?

VLLM is an innovative library specifically designed for the efficient inference and deployment of Large Language Models (LLMs). Originally developed at UC Berkeley's Sky Computing Lab, it has evolved into a collaborative project that benefits from input by both academia and industry. The library stands out for its remarkable serving throughput, achieved through its unique PagedAttention mechanism, which adeptly manages attention key and value memory. It supports continuous batching of incoming requests and utilizes optimized CUDA kernels, leveraging technologies such as FlashAttention and FlashInfer to enhance model execution speed significantly. In addition, VLLM accommodates several quantization techniques, including GPTQ, AWQ, INT4, INT8, and FP8, while also featuring speculative decoding capabilities. Users can effortlessly integrate VLLM with popular models from Hugging Face and take advantage of a diverse array of decoding algorithms, including parallel sampling and beam search. It is also engineered to work seamlessly across various hardware platforms, including NVIDIA GPUs, AMD CPUs and GPUs, and Intel CPUs, which assures developers of its flexibility and accessibility. This extensive hardware compatibility solidifies VLLM as a robust option for anyone aiming to implement LLMs efficiently in a variety of settings, further enhancing its appeal and usability in the field of machine learning.

What is Project G-Assist?

NVIDIA has launched Project G-Assist, an innovative AI assistant designed to enhance the gaming experience for GeForce RTX users by providing system optimizations, real-time diagnostics, and customizable peripheral controls through simple voice or text commands. Integrated into the NVIDIA app, G-Assist automatically adjusts game settings for optimal performance or visual fidelity, monitors and showcases key performance metrics like frame rates and system latency, and manages lighting effects on compatible devices from brands such as Logitech, Corsair, MSI, and Nanoleaf. Leveraging a locally-based Small Language Model (SLM), G-Assist ensures swift responses and operates efficiently without needing an internet connection. Users can easily engage G-Assist via the NVIDIA app overlay or by pressing Alt+G, utilizing the GeForce RTX GPU for AI inference tasks. Furthermore, developers and tech aficionados can expand G-Assist's capabilities through a community-driven plugin framework, which offers extensive resources and example plugins for inspiration. This progressive strategy not only empowers gamers but also cultivates a collaborative ecosystem for the continuous enhancement of the gaming experience, encouraging a shared journey of innovation and creativity in the gaming community.

Media

Media

Integrations Supported

Database Mart
Docker
Gemini
Gemini Enterprise
Hugging Face
KServe
Kubernetes
Logitech Capture
NGINX
NVIDIA DRIVE
OpenAI
PyTorch

Integrations Supported

Database Mart
Docker
Gemini
Gemini Enterprise
Hugging Face
KServe
Kubernetes
Logitech Capture
NGINX
NVIDIA DRIVE
OpenAI
PyTorch

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

VLLM

Company Location

United States

Company Website

docs.vllm.ai/en/latest/

Company Facts

Organization Name

NVIDIA

Date Founded

1993

Company Location

United States

Company Website

www.nvidia.com/en-us/software/nvidia-app/g-assist/

Categories and Features

Categories and Features

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