Average Ratings 0 Ratings
Average Ratings 0 Ratings
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
API Access
Has API
Integrations
Database Mart
Docker
Gemini
Gemini Enterprise
Hugging Face
KServe
Kubernetes
Logitech Capture
NGINX
NVIDIA DRIVE
Integrations
Database Mart
Docker
Gemini
Gemini Enterprise
Hugging Face
KServe
Kubernetes
Logitech Capture
NGINX
NVIDIA DRIVE
Pricing Details
No price information available.
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
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