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Liquid AI's LFM3-1.7B model fits in 1GB, claims benchmark win over Qwen3.5-9B

By PulseAugur Editorial · [1 sources] ·

Liquid AI has released LFM3-1.7B, a new 1.73 billion parameter text model that can fit into 1 GB of memory and process up to 256K tokens of context. While Liquid AI's own benchmarks suggest LFM3-1.7B outperforms Qwen3.5-9B on average, this claim is contingent on dropping four specific benchmarks, after which Qwen3.5-9B leads. The model is designed for tool-calling within agent harnesses and is available for local execution via Ollama, llama.cpp, LM Studio, and MLX. AI

IMPACT Enables running capable LLMs on low-resource devices, potentially expanding local AI applications.

RANK_REASON The item describes a new model release and its technical specifications, including benchmark performance claims. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Liquid AI's LFM3-1.7B model fits in 1GB, claims benchmark win over Qwen3.5-9B

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The item describes a new model release and its technical specifications, including benchmark performance claims. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Alan West ·

    How to Run LFM3-1.7B Locally in 1 GB: The Qwen3.5-9B Win Rests on 4 Benchmarks

    <p>Liquid AI's own benchmark table puts LFM3-1.7B, its new 1.7B text model, ahead of Qwen3.5-9B on average. It fits in about 1 GB at 4-bit, reads 256K tokens of context, and was post-trained for tool calling inside agent harnesses. The same model card tells you not to use it for …

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