Tagged "liquid-ai"
19 articles tagged liquid-ai, 26 March 2026 to 18 October 2026. Newest first.
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Liquid AI's d1-omni-600M Model Now Available as Ollama Pull
Liquid AI releases the d1-omni-600M model optimized for edge deployment, now accessible through Ollama, expanding the catalog of efficient models for local inference.
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Liquid AI Releases LFM2.5-24B-A2B: Optimized Model for Fast On-Device Agents
Liquid AI introduces LFM2.5-24B-A2B, a 24-billion-parameter model specifically designed for efficient on-device agent inference. The model targets the practical size/capability sweet spot for edge deployment with agentic reasoning capabilities.
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Liquid AI Releases LFM2.5-24B-A2B: A Decision Model Built for Fast On-Device Agents
Liquid AI has released LFM2.5-24B-A2B, a 24B parameter model optimised for on-device agent deployment with only 2B active parameters during inference. This model is designed to deliver both speed and capability for local LLM inference on consumer hardware.
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Liquid AI's LFM2.5-24B-A2B Enables Agent Tasks on Laptop Hardware
Liquid AI releases LFM2.5-24B-A2B, a 24B parameter model optimised for running agentic workflows directly on consumer laptops without cloud dependencies. The model outperforms larger alternatives like Gemma 4 26B and Qwen 3.6 35B while maintaining efficient local inference.
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llama.cpp Build b10581 Adds DSpark Support for Faster Local Inference
The latest llama.cpp release includes native support for DSpark model optimization, enabling users to run DSpark-optimized models like LFM2.5 with maximum efficiency. This update extends llama.cpp's lead as the fastest local inference engine.
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Liquid AI Releases DSpark Version of Compact LFM2.5 Models with Up to 2.67x Speedup
Liquid AI releases optimized DSpark variants of their LFM2.5 models, achieving up to 2.67x inference speedup. These compact models are designed for on-device and edge deployment scenarios where latency and resource constraints are critical.
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Liquid AI Releases LFM2.5-DSpark Draft Models with 3.18x Faster Decoding
Liquid AI introduces speculative decoding models that achieve up to 3.18x faster inference without changing model outputs, significantly improving local LLM performance.
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Liquid AI Releases LFM2.5 Q4_0 Checkpoints from Quantization-Aware Distillation
Liquid AI publishes LFM2.5 Q4_0 quantized checkpoints trained with quantization-aware distillation, enabling efficient local inference with maintained model quality. This approach combines distillation and quantization for optimal compression.
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Liquid AI Releases LFM2.5-VL-3B: Compact Vision-Language Model for Edge Inference
Liquid AI unveiled LFM2.5-VL-3B, a 3 billion parameter vision-language model designed for on-device deployment with capabilities for screen reading, object grounding, and tool calling without server dependencies.
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LFM2.5-VL-3B: Lightweight Vision-Language Model Optimized for Edge Deployment
Liquid AI releases LFM2.5-VL-3B, a 3B parameter vision-language model designed for on-device inference with support for UI recognition and OCR. The model delivers efficient multimodal capabilities suitable for resource-constrained edge environments.
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MacPaw and Liquid AI: Complete On-Device AI Stack for macOS
MacPaw has partnered with Liquid AI to deliver a comprehensive on-device AI stack that runs entirely on Mac hardware, eliminating cloud dependencies and ensuring data privacy for Apple users. The implementation showcases optimized inference leveraging Apple Silicon capabilities.
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MacPaw Partners With Liquid AI to Deploy On-Device AI Across Mac Ecosystem
MacPaw and Liquid AI announce a partnership to integrate on-device AI capabilities into MacPaw's Mac assistant product, bringing local inference to millions of Mac users with privacy-focused deployment.
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Liquid AI LFM2.5-2.6B: Open-Weights Agentic Model With 128K Context and Tool Calling
Liquid AI releases an open-weights agentic model optimized for on-device deployment with 128K context window, tool calling capabilities, and support for extremely low-resource edge hardware.
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Liquid AI Releases LFM2.5-2.6B: Powerful Agentic Model for Raspberry Pi and Edge Devices
Liquid AI's new LFM2.5-2.6B model brings agentic AI capabilities to resource-constrained devices like Raspberry Pi, featuring 128K context window and tool calling without requiring GPUs or cloud infrastructure.
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LFM2.5-2.6B: On-Device Agentic Model With 128K Context and Tool Calling
Detailed technical analysis of Liquid AI's LFM2.5-2.6B with open weights, demonstrating how 128K context and tool-calling capabilities are achievable in a 2.6B parameter model optimized for local inference.
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Liquid AI Ships LFM2.5-230M with Broad Framework Support for On-Device Inference
Liquid AI released LFM2.5-230M, a compact language model optimized for local deployment across llama.cpp, MLX, vLLM, SGLang, and ONNX. This multi-framework support enables seamless on-device inference across diverse hardware and deployment scenarios.
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Liquid AI Launches Edge-Focused LFM2.5 Model to Power On-Device AI Agents
Liquid AI has released the LFM2.5 model specifically optimized for edge deployment and on-device AI agents. This new model represents a significant development for practitioners looking to run capable language models locally with reduced resource requirements.
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Liquid AI Unveils Edge-Focused LFM2.5 Model for On-Device AI Agents
Liquid AI has introduced the LFM2.5 model specifically designed for edge deployment and local AI agents, offering optimized performance for resource-constrained environments.
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Liquid AI's LFM2-24B Achieves 50 Tokens/Second in Web Browser via WebGPU
Liquid AI has demonstrated their LFM2-24B mixture-of-experts model running at 50 tokens/second in a web browser on M4 Max hardware using WebGPU. The 8B variant achieves over 100 tokens/second, showcasing practical edge inference in browser environments.