AI latency
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Liquid AI’s LFM2.5: Compact On-Device Models Released
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Liquid Ai has introduced LFM2.5, a series of compact on-device foundation models designed to enhance the performance of agentic applications by offering higher quality, reduced latency, and broader modality support within the ~1 billion parameter range. Building on the LFM2 architecture, LFM2.5 scales pretraining from 10 trillion to 28 trillion tokens and incorporates expanded reinforcement learning post-training to improve instruction-following capabilities. This release includes five open-weight model instances derived from a single architecture, including a general-purpose instruct model, a Japanese-optimized chat model, a vision-language model, a native audio-language model for speech input and output, and base checkpoints for extensive customization. This matters as it enables more efficient and versatile on-device AI applications, broadening the scope and accessibility of AI technology.
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Efficient AI with Chain-of-Draft on Amazon Bedrock
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As organizations scale their generative AI implementations, balancing quality, cost, and latency becomes a complex challenge. Traditional prompting methods like Chain-of-Thought (CoT) often increase token usage and latency, impacting efficiency. Chain-of-Draft (CoD) is introduced as a more efficient alternative, reducing verbosity by limiting reasoning steps to five words or less, which mirrors concise human problem-solving patterns. Implemented using Amazon Bedrock and AWS Lambda, CoD achieves significant efficiency gains, reducing token usage by up to 75% and latency by over 78%, while maintaining accuracy levels comparable to CoT. This matters as CoD offers a pathway to more cost-effective and faster AI model interactions, crucial for real-time applications and large-scale deployments.