LFM2.5-230M Sets New Benchmark for Edge AI Data Extraction
## Why Edge Devices Are Finally Catching Up with Server‑Scale Language Models Liquid AI’s latest release, the LFM2.5‑230M, demonstrates that on‑device natural‑language processing can now rival—and in some metrics surpass—much larger cloud‑based models. The 230‑million‑parameter foundation, built on the proprietary LFM2 framework, delivers a data‑extraction accuracy that outperforms competitors more than four times its size, all while staying within a 400 MB memory envelope and supporting a 32 K token context window. ### Key Takeaways - **Performance punch:** LFM2.5‑230M achieves higher data‑extraction accuracy than models that are over four times larger. - **Efficient architecture:** The model interleaves gated convolutions with grouped‑query attention, a design that reduces memory consumption to under 400 MB. - **Large context handling:** A 32 K token context window enables processing of extensive documents without off‑loading to the cloud. - **Edge‑first focus:** By keeping the footprint small, the model is optimized for deployment on smartphones, IoT gateways, and other resource‑constrained hardware. - **Benchmark transparency:** Liquid AI has released the full benchmark suite, allowing independent verification of the claimed gains. #EdgeAI #DataExtraction #OnDeviceML #LFM2_5 #LiquidAI #GatedConvolution #GroupedQueryAttention #32KTokenContext #LowMemoryAI #newsababil360 [Read Full Article](https://news.ababil360.com/lfm2-5-230m-sets-new-benchmark-for-edge-ai-data-extraction/)
