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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.

What is BaseRT?

BaseRT provides a powerful inference runtime for large language models, specifically tailored for Apple Silicon, enabling developers to effortlessly access models from Hugging Face, engage in local dialogue, or use an OpenAI-compatible API all through a single command-line interface. Boosted by expertly designed Metal kernels, BaseRT is engineered to excel in prefill and decoding efficiency on M-series Macs, with benchmark tests demonstrating performance that is up to 6.4 times faster in prefill tasks than llama.cpp, 3.9 times quicker than MLX, and achieving a decoding speed that surpasses competitors by 1.33 times. The BaseRT CLI is equipped to handle various tasks including model downloading, conversion, interactive chatting, serving functionalities, completion generation, benchmarking, inspection, and bundle signing. Its comprehensive server capabilities include chat interactions, text completions, embeddings, transcription services, tool calls, continuous batching, paged key-value caching, and prefix caching, while supporting models that process text, vision, and audio data. BaseRT utilizes a unique .base model format that features Q2–Q8 affine quantization, optional AWQ calibration, and signed bundles, and it can convert GGUF, Hugging Face, and MLX checkpoints seamlessly. In addition to these features, this groundbreaking runtime is specifically designed to harness the full potential of Apple Silicon, establishing itself as an indispensable resource for developers working in the AI domain. With its impressive efficiency and broad functionality, BaseRT stands out as a key innovation for the future of AI development on Apple platforms.

Media

Media

Integrations Supported

Gemma 3
Gemma 4
Hugging Face
Llama 3.2
Qwen3
Amazon Bedrock
ElevenLabs
LEAP
Llama
Llama 3.1
Mistral AI
OpenAI
Phi-3
Qwen3.5
Qwen3.6

Integrations Supported

Gemma 3
Gemma 4
Hugging Face
Llama 3.2
Qwen3
Amazon Bedrock
ElevenLabs
LEAP
Llama
Llama 3.1
Mistral AI
OpenAI
Phi-3
Qwen3.5
Qwen3.6

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Version
Free Trial Offered?

Pricing Information

Pricing not provided
Free Version
Free Trial Offered?

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

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

Company Facts

Organization Name

Base Compute

Date Founded

2026

Company Location

Australia

Company Website

www.basecompute.co/getbasert

Categories and Features

Categories and Features

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