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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 Mirai?

Mirai stands out as a sophisticated platform designed specifically for developers, focusing on on-device AI infrastructure that facilitates the conversion, optimization, and execution of machine learning models right on Apple devices, all while prioritizing performance and user privacy. With a streamlined workflow, teams can effectively convert and quantize models, evaluate their performance, distribute them, and perform local inference without any hassle. Tailored for Apple Silicon, Mirai aims to deliver near-zero latency and eliminate inference costs, ensuring that the processing of sensitive data remains entirely on the user's device for enhanced security. Its comprehensive SDK and inference engine empower developers to quickly embed AI capabilities into their applications, utilizing hardware-aware optimizations to fully harness the potential of the GPU and Neural Engine. Additionally, Mirai incorporates dynamic routing features that smartly decide on the optimal execution path for tasks, whether it be executing locally or accessing cloud resources, while considering important factors like latency, privacy, and workload requirements. This adaptability not only improves the overall user experience but also equips developers with the tools to craft more responsive and efficient applications that cater specifically to the needs of their users, ultimately driving innovation in the realm of on-device AI.

Media

Media

Integrations Supported

Database Mart
Docker
DocsGPT
Hugging Face
Kubernetes
Muse Spark Open
NGINX
Nemotron 3.5 Bolt
Nemotron 3.5 Super VL
PyTorch
Thunder Compute
omp

Integrations Supported

DeepSeek R1
Gemma 3
LFM-3B
Llama
Polaris
Qwen3
SmolLM2
gpt-oss-120b

API Availability

Has API

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided
Free Version

Supported Platforms

SaaS

Supported Platforms

SaaS
iPhone
iPad
Mac

Customer Service / Support

24 Hour Support
Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub
Online Training

Company Facts

Organization Name

vLLM

Company Location

United States

Company Website

vllm.ai

Company Facts

Organization Name

Mirai

Date Founded

2024

Company Location

United States

Company Website

trymirai.com

Categories and Features

AI Inference

Not specified

Categories and Features

AI Inference

Not specified

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