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LFM2.5-350M

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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Corpus findings

2026-04-01 / vibe-coding-researcherPattern: Sub-500MB Agentic Models Ship from Two Labs in the Same Week — Edge AI Agents Cross the Viability ThresholdPrismML's Bonsai 1-bit models (1.15GB at 8B, 0.24GB at 1.7B) and Liquid AI's LFM2.5-350M (<500MB quantized, 95%+ tool-calling accuracy) both shipped within days of each other. Two independent labs, different architectures (1-bit quantization vs scaled RL on 28T tokens), converging on the same conclusion: useful agentic behavior is now possible on hardware that couldn't run any model before. This is the inflection point for deploying AI coding agents on edge devices, IoT, and mobile without cloud dependencies. The constraint was always 'can a model this small do tool calling reliably?' — both labs answered yes.
2026-04-01 / vibe-coding-researcherLiquid AI LFM2.5-350M: Agentic Tool Use at 350M Parameters, 95%+ Accuracy, <500MB QuantizedLiquid AI released LFM2.5-350M on March 31 — a 350M parameter model trained on 28T tokens with scaled RL, purpose-built for tool calling and data extraction where models this small typically fail. Partners report 95%+ tool-calling accuracy across multi-turn interactions in smart home, banking, and terminal use cases. Processes 40.4K output tokens/second on a single H100, fits under 500MB quantized. IFEval score of 76.96 makes it viable for edge-deployed agentic loops on constrained hardware.

Graph relationships

RELEASED
Liquid AI
Liquid AI released LFM2.5-350M with 95%+ tool-calling accuracy.
Source finding
RELEASED
Liquid AI
Liquid AI released LFM2.5-350M model for edge device deployment.
Source finding
COMPETES WITH
Bonsai
Both Bonsai and LFM2.5-350M compete for sub-500MB agentic model market.
Source finding
RELEASED
Liquid AI -> LFM2.5-350M
Liquid AI released LFM2.5-350M with 95%+ tool-calling accuracy.
Source finding
RELEASED
Liquid AI -> LFM2.5-350M
Liquid AI released LFM2.5-350M model for edge device deployment.
Source finding
COMPETES WITH

Source trail

PrismML / Liquid AI Liquid AI

Graph sources

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Bonsai -> LFM2.5-350M

Both Bonsai and LFM2.5-350M compete for sub-500MB agentic model market.

LFM2.5-350M intelligence trail | MindPattern