Ratings and Reviews 0 Ratings

Total
ease
features
design
support

This software has no reviews. Be the first to write a review.

Write a Review

Ratings and Reviews 0 Ratings

Total
ease
features
design
support

This software has no reviews. Be the first to write a review.

Write a Review

Alternatives to Consider

  • RunPod Reviews & Ratings
    205 Ratings
    Company Website
  • LM-Kit.NET Reviews & Ratings
    26 Ratings
    Company Website
  • Vertex AI Reviews & Ratings
    961 Ratings
    Company Website
  • Google AI Studio Reviews & Ratings
    11 Ratings
    Company Website
  • Attentive Reviews & Ratings
    1,435 Ratings
    Company Website
  • Curtain MonGuard Screen Watermark Reviews & Ratings
    7 Ratings
    Company Website
  • OptiSigns Reviews & Ratings
    7,880 Ratings
    Company Website
  • Vehicle Acquisition Network (VAN) Reviews & Ratings
    3 Ratings
    Company Website
  • TextUs Reviews & Ratings
    854 Ratings
    Company Website
  • Qloo Reviews & Ratings
    23 Ratings
    Company Website

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 LFM2.5?

Liquid AI's LFM2.5 marks a significant evolution in on-device AI foundation models, designed to optimize efficiency and performance for AI inference across edge devices, including smartphones, laptops, vehicles, IoT systems, and various embedded hardware, all while eliminating reliance on cloud computing. This upgraded version builds on the previous LFM2 framework by significantly increasing the scale of pretraining and enhancing the stages of reinforcement learning, leading to a collection of hybrid models that feature approximately 1.2 billion parameters and successfully balance adherence to instructions, reasoning capabilities, and multimodal functions for real-world applications. The LFM2.5 lineup includes various models, such as Base (for fine-tuning and personalization), Instruct (tailored for general-purpose instruction), Japanese-optimized, Vision-Language, and Audio-Language editions, all carefully designed for swift on-device inference, even under strict memory constraints. Additionally, these models are offered as open-weight alternatives, enabling easy deployment through platforms like llama.cpp, MLX, vLLM, and ONNX, which enhances flexibility for developers. With these advancements, LFM2.5 not only solidifies its position as a powerful solution for a wide range of AI-driven tasks but also demonstrates Liquid AI's commitment to pushing the boundaries of what is possible with on-device technology. The combination of scalability and versatility ensures that developers can harness the full potential of AI in practical, everyday scenarios.

Media

Media

Integrations Supported

Hugging Face
Amazon Bedrock
Database Mart
Docker
ElevenLabs
Gemma 3
Gemma 4
KServe
Kubernetes
LEAP
Llama
Llama 3.2
NGINX
NVIDIA DRIVE
OpenAI
PyTorch
Qwen3
Thunder Compute

Integrations Supported

Hugging Face
Amazon Bedrock
Database Mart
Docker
ElevenLabs
Gemma 3
Gemma 4
KServe
Kubernetes
LEAP
Llama
Llama 3.2
NGINX
NVIDIA DRIVE
OpenAI
PyTorch
Qwen3
Thunder Compute

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Free
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

vllm.ai

Company Facts

Organization Name

Liquid AI

Date Founded

2023

Company Location

United States

Company Website

www.liquid.ai/blog/introducing-lfm2-5-the-next-generation-of-on-device-ai

Categories and Features

Categories and Features

Popular Alternatives

Popular Alternatives

HunyuanOCR Reviews & Ratings

HunyuanOCR

Tencent
OpenVINO Reviews & Ratings

OpenVINO

Intel
MedGemma Reviews & Ratings

MedGemma

Google DeepMind