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LFM3-1.7B: What's New, Hardware Needed, How to Run It Free

LFM3-1.7B offers a 256K token window in a 1 GB footprint, ideal for local agents and fast fine-tuning. See how its benchmarks compare to larger models and…

What the video says

LFM3-1.7B packs a 256K token window into about 1 GB of RAM. It’s built for local agents and can run on a phone or laptop.

LFM3-1.7B is a text-only language model from LiquidAI. The architecture mixes gated short convolutions, sliding-window attention and global attention across 24 layers. The model is released under the LFM Open License v1.0.

According to LiquidAI, the model is post-trained for agentic use cases like Hermes Agent, Pi and OpenClaw, and it comes with a 256 K token context window. At 4-bit precision it needs only about 1 GB of memory, so it fits on any phone or laptop. The design encourages quick domain-specific fine-tuning on a single GPU, making it easy to adapt to new formats or tools.

The model card lists several benchmark results. In the LiveClaw agent test LFM3-1.7B scores 45.60, beating larger models like Qwen3.5-9B. For tool-use tasks it reaches 71.39 on BFCL V4. In coding, LiveCodeBench v6 shows a 73.46 score, and on instruction following IFEval it hits 96.66. These numbers indicate strong performance for a 1.7 B parameter model.

LFM3-1.7B shines in agentic applications, tool calling and structured output tasks, and it runs faster than comparable models on edge devices. However, the model card warns that it is not recommended for agentic coding or complex multi-step reasoning because of its small size.

If you run AI at home, need a model that fits on a phone or laptop, or want to fine-tune quickly for a specific domain, LFM3-1.7B is a good fit. It works on modest GPUs like an RTX 4060 and can be run locally with GGUF files in llama.cpp, Ollama or LM Studio.

What do you need to run it at home? At 4-bit it takes about 4 gigabytes of memory. The smallest home machine that holds it fully is an RTX 4060. On an RTX 3060 12 GB with 32 gigabytes of RAM it writes about 199 tokens per second. On an RTX 4090 with 64 gigabytes of RAM it writes about 410 tokens per second.

There is no free API for it yet. The weights are open: you can download them from Hugging Face, under the license written in its model card. Arena has not ranked it yet: we add the score as soon as the votes arrive.

Compare LFM3 1.7B with every free model, and check what your own GPU can run, on FreeLLMArena.

The data

  • Size: 1.7B parameters · context 256K · memory at 4-bit about 4 GB.
  • RTX 3060 12 GB + 32 GB RAM: about 199 tokens/s
  • RTX 4090 + 64 GB RAM: about 410 tokens/s
  • RTX 5090 + 64 GB RAM: about 552 tokens/s
  • Mac M4 Max 128 GB: about 162 tokens/s
  • NVIDIA DGX Spark 128 GB: about 158 tokens/s
  • Ryzen AI Max+ 395 128 GB: about 150 tokens/s
  • Mac Studio M3 Ultra 512 GB: about 190 tokens/s
  • Model card: https://huggingface.co/LiquidAI/LFM3-1.7B
  • Every free model, compared: https://freellmarena.com

Hardware

Memory at 4-bit with an 8K context, from our simulator. To check your own setup, use the Run at home simulator.

As an Amazon Associate, FreeLLMArena earns from qualifying purchases; the links do not change the numbers.

Every number in this post comes from the FreeLLMArena data on 28 Oct 2026: Arena votes, Hugging Face sizes and our speed simulator. Download the video and its subtitles.

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