LFM2.5-24B-A2B targets local agent workloads with a small active parameter count. Its full model, context and runtime still need to fit; reported speed on named hardware is not a universal device guarantee.
What LFM2.5-24B-A2B actually reports
Liquid AI's October 13 announcement describes 24B total parameters, 2B active and a 131,072-token context window. It identifies a reasoning-only post-trained model and a separate Base release. The architecture combines short convolutions, attention and routed experts.
What this changes for the reader
For a local assistant, faster token generation only matters when the entire task is useful. Our suggested evaluation includes how much reasoning output the runtime produces, whether tool arguments are valid and how long a human needs to verify the final work. Keep generated-token speed separate from time to an accepted result.
A practical evaluation before adoption
Choose a local task that can be checked without granting broad permissions: classify a few public files or propose a patch in a disposable project. Pin model, quantization and context, then compare correct outputs under the same budget. Record memory with ordinary applications open and include failed tool calls in the log.
- Identify Base or post-trained weights.
- Budget all stored parameters and context.
- Measure accepted task completion, not only decode speed.
Limits and availability to keep in view
The announcement's hardware results are vendor measurements. We have not reproduced them or established a minimum device configuration. Check the model card, runtime support and actual license before embedding it in a product.
Where to go next
Read our local versus hosted task costs for the adjacent workflow decision. Keep this project's conditions separate from other models' results. If a dependency, artifact or service limit is unclear, leave it unresolved in the comparison rather than filling the gap with an assumption.
Frequently asked questions
Does 2B active make this a 2B total model?
No. Liquid AI describes 24B total parameters with 2B active per token.
Is the post-trained model the same as Base?
No. The announcement distinguishes the reasoning post-trained model from Base.
Has FyreLinkz independently tested LFM2.5-24B-A2B?
No. This article reports the linked primary source and proposes an editorial evaluation; it contains no independent performance benchmark.
Sources & further reading
Primary sources checked Oct 18, 2026. Vendor statements are attributed; editorial advice is our own.
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