LFM2

LFM2

WebsiteFreeLarge Language Models (LLMs)Multi-purpose Tools
LFM2 is a new class of Liquid Foundation Models that delivers state-of-the-art performance with 2x faster speed than competitors, designed specifically for efficient on-device AI deployment across various hardware platforms.
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https://www.liquid.ai/blog/liquid-foundation-models-v2-our-second-series-of-generative-ai-models?ref=producthunt
LFM2

Product Information

Updated:Aug 26, 2025

LFM2 Monthly Traffic Trends

LFM2 received 41.5k visits last month, demonstrating a Slight Decline of -10.3%. Based on our analysis, this trend aligns with typical market dynamics in the AI tools sector.
View history traffic

What is LFM2

LFM2 (Liquid Foundation Models 2) is the next generation of AI models developed by Liquid AI that sets new standards in quality, speed, and memory efficiency. Released as open-source models with different sizes (350M, 700M, and 1.2B parameters), LFM2 is built on a hybrid architecture combining convolution and attention mechanisms, specifically optimized for on-device deployment. The models support multiple tasks including text generation, vision-language processing, and multilingual capabilities while maintaining competitive performance against larger models.

Key Features of LFM2

LFM2 is a new class of Liquid Foundation Models designed specifically for on-device AI deployment, featuring a hybrid architecture that combines convolution and attention mechanisms. It achieves 2x faster decode and prefill performance than competitors on CPU, with 3x improved training efficiency over previous generations. The models are optimized for speed, memory efficiency, and quality while supporting multiple languages and tasks, making them ideal for edge computing and local AI processing.
Hybrid Architecture: Combines 16 blocks of convolution and attention mechanisms, with 10 double-gated short-range convolution blocks and 6 blocks of grouped query attention
Enhanced Performance: Delivers 2x faster decode and prefill performance on CPU compared to Qwen3, with 3x improvement in training efficiency
Memory Efficient: Maintains near-constant inference time and memory complexity even with long inputs, making it suitable for resource-constrained environments
Multilingual Capability: Supports multiple languages including Arabic, French, German, Spanish, Japanese, Korean, and Chinese with strong performance across various benchmarks

Use Cases of LFM2

Mobile Applications: Enables AI capabilities on smartphones and tablets with efficient local processing and low latency
Edge Computing: Powers AI applications in IoT devices, wearables, and embedded systems where cloud connectivity isn't always available
Enterprise Security: Provides private, on-premise AI processing for organizations requiring data sovereignty and security
Automotive Systems: Enables real-time AI processing in vehicles where quick response times and offline operation are crucial

Pros

Superior performance on edge devices with faster processing speed
Lower memory requirements compared to traditional models
Maintains privacy through local processing without cloud dependencies
Strong multilingual capabilities

Cons

Limited to smaller parameter sizes compared to cloud-based models
Commercial use requires licensing for companies with revenue over $10M
May not match the performance of larger cloud-based models in some complex tasks

How to Use LFM2

Access LFM2 Models: Visit Hugging Face to access the open-source LFM2 models available in three sizes: 350M, 700M, and 1.2B parameters
Check License Requirements: Review the open license (based on Apache 2.0) - free for academic/research use and commercial use for companies under $10M revenue. Larger companies need to contact [email protected] for commercial licensing
Choose Deployment Method: Select either llama.cpp for local CPU deployment or ExecuTorch for PyTorch ecosystem deployment. Both support different quantization schemes (8da4w for ExecuTorch, Q4_0 for llama.cpp)
Format Input Prompts: Use the chat template format: '<|startoftext|><|im_start|>system [system message]<|im_end|> <|im_start|>user [user message]<|im_end|> <|im_start|>assistant'
Apply Chat Template: Use the .apply_chat_template() function from Hugging Face transformers to properly format your inputs
Local Testing: Test the models privately and locally on your device using the chosen integration (llama.cpp recommended for CPU deployment)
Optional Fine-tuning: Use TRL (Transformer Reinforcement Learning) library if you need to fine-tune the models for specific use cases
Function Calling: For function calls, provide JSON function definitions between <|tool_list_start|> and <|tool_list_end|> special tokens in the system prompt

LFM2 FAQs

LFM2 is a new class of Liquid Foundation Models designed for on-device AI deployment, offering superior speed, memory efficiency, and quality. It's built on a hybrid architecture that delivers 200% faster decode and prefill performance than competitors like Qwen3 and Gemma 3 on CPU.

Analytics of LFM2 Website

LFM2 Traffic & Rankings
41.5K
Monthly Visits
#680347
Global Rank
#7399
Category Rank
Traffic Trends: Sep 2024-Jun 2025
LFM2 User Insights
00:00:48
Avg. Visit Duration
2.03
Pages Per Visit
44.03%
User Bounce Rate
Top Regions of LFM2

  1. US: 34.58%

  2. TH: 9.58%

  3. IN: 9.34%

  4. VN: 9.21%

  5. DE: 5.8%

  6. Others: 31.51%

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