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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

Description

NVIDIA has introduced Project G-Assist, a revolutionary AI assistant aimed at improving the gaming experience for GeForce RTX users by offering system optimizations, real-time diagnostics, and customizable peripherals through straightforward voice or text commands. This feature is embedded within the NVIDIA app, allowing G-Assist to automatically modify game settings for the best performance or visual quality, track and display essential performance metrics such as frame rates and system latency, and control lighting effects on compatible devices from manufacturers like Logitech, Corsair, MSI, and Nanoleaf. Utilizing a locally operated Small Language Model (SLM), G-Assist guarantees quick responsiveness and functions without requiring an internet connection. Users can easily activate G-Assist either through the NVIDIA app overlay or by pressing Alt+G, leveraging the GeForce RTX GPU to carry out AI inference tasks. Additionally, developers and tech enthusiasts have the opportunity to enhance G-Assist's functionality via a community-focused plugin architecture, providing ample resources and example plugins for inspiration. This innovative approach not only empowers users but also fosters a collaborative environment for ongoing development and improvement of the gaming experience.

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 No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Database Mart No 
Docker No 
Gemini Yes 
Gemini Enterprise Yes 
Hugging Face No 
KServe No 
Kubernetes No 
Logitech Capture Yes 
NGINX No 
NVIDIA DRIVE No 
OpenAI No 
PyTorch No 
Thunder Compute No 
omp No 

Integrations

Database Mart Yes 
Docker Yes 
Gemini No 
Gemini Enterprise No 
Hugging Face Yes 
KServe Yes 
Kubernetes Yes 
Logitech Capture No 
NGINX Yes 
NVIDIA DRIVE Yes 
OpenAI Yes 
PyTorch Yes 
Thunder Compute Yes 
omp Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) Yes 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) Yes 
In Person Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

NVIDIA

Founded

1993

Country

United States

Website

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

Vendor Details

Company Name

vLLM

Country

United States

Website

vllm.ai

Product Features

Product Features

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