August 10, 2026
Small model, giant comment war
LFM2.5 2.6B model competitive with 4x larger models
Tiny AI claims it can punch way above its weight — and the comments got messy fast
TLDR: Liquid AI says its new tiny model can do jobs usually handled by much larger AI systems and run on everyday computers. Commenters immediately split into two camps: believers saying small models are finally getting serious, and skeptics calling the hype overblown and the real-world results underwhelming.
A new small AI from Liquid AI is being hyped as a pocket-sized overachiever: just 2.6 billion parameters, yet supposedly able to keep up with models about four times bigger on tasks like following instructions, using tools, and handling multi-step requests. The company is also boasting that it runs fast on consumer hardware, including Apple laptops and regular CPUs, while using less than 2.5 GB of memory. In plain English: this is being pitched as an AI you might actually run on your own machine instead of renting a giant one in the cloud.
But the real fireworks are in the comments. One camp is deeply unimpressed, with one user flatly declaring these models have “never worked well for me in practice,” while another went straight for the jugular and argued it’s not even competitive with a slightly larger rival. That sparked the classic AI comment-section showdown: benchmark believers vs. “I tried it and it flopped” realists.
Then came the dreamers. One commenter basically said, forget coding — the fun part is imagining swarms of cheap local agents simulating crowds, markets, ecosystems, and game characters. It’s the kind of sci-fi-adjacent vision that makes half the internet say “wow” and the other half say “sure, Jan.” Even the simple question, “Will this work on i3/i5 laptops?” added to the vibe: people aren’t just debating performance, they’re trying to figure out whether this tiny AI is a revolution for normal computers or just another flashy lab flex.
Key Points
- •LFM2.5-2.6B is a 2.69B-parameter hybrid text-only model in Liquid AI’s LFM2.5 family, designed for on-device deployment.
- •The model builds on the LFM2 architecture, adds a 131,072-token context window, and is post-trained for agentic workloads.
- •The article says the model is competitive with models four times larger on tool use, instruction following, and multi-step agentic tasks.
- •Reported local inference performance is 220 tok/s on an Apple M5 Max and 113 tok/s on an AMD Ryzen CPU using under 2.5 GB of memory.
- •The release is available in native, GGUF, ONNX, and MLX formats, with support for tool calling, ChatML-like prompting, and deployment across CPU, cloud, edge, mobile, and Apple Silicon environments.