Computer-use agents need checks before every consequential action
One model response can contain several desktop actions. The integrating application decides how to execute the sequence and where a human decision is required.
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LFM2.5-24B-A2B: what to check before running a local agent
Liquid AI’s October release gives local-agent builders another model to evaluate. Check the checkpoint, workload and usage conditions before interpreting its benchmark scores.
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AI-assisted editorial review · Sources checked · Recheck due
The takeaways
- Liquid AI published the release on 13 October 2026.
- The model card specifies 24.0B total parameters and 2.45B active parameters.
- A useful comparison measures completed work and correction effort on your machine.
What is available
Liquid AI’s release announcement rounds the active parameter count to 2B. The model card gives 2.45B, alongside a 131,072-token context window, and links native, GGUF and MLX checkpoints. These formats give readers concrete starting points for different inference environments.
Sources: Liquid AI: Run fast, private agents on-device with LFM2.5-24B-A2B Liquid AI LFM2.5-24B-A2B model card
Read the conditions beside the claims
The developer’s results vary by task, with several coding and knowledge comparisons favoring other models. Its reported edge-speed measurements use llama.cpp, Q4_K_M quantization and a 4K-token input. The advertised maximum context is a separate specification.
The LFM Open License v1.0 includes a commercial-use restriction tied to a US$10 million annual-revenue threshold. Read its entity definition and full terms before deciding whether your intended use is covered.
Sources: Liquid AI: Run fast, private agents on-device with LFM2.5-24B-A2B Liquid AI LFM2.5-24B-A2B model card LFM Open License v1.0
Editorial interpretation
A first trial that could change a decision
Our proposed trial starts with a small, synthetic support inbox. Ask the candidate to turn each request into a draft work item containing the customer identifier, requested action and missing information. Provide a local lookup tool and a destination that stores drafts. Keep sending and account changes outside this trial.
Prepare the expected fields before running the model. Include a straightforward request, a missing customer identifier, contradictory instructions and a request that should remain unresolved. A plausible draft for the wrong customer is a failed result. An appropriate request for clarification can be successful when the input is incomplete.
Run the same cases with the model you already use. Record the checkpoint, inference software, context settings, final artifact, elapsed time and your correction minutes. Decide in advance which mistakes rule a candidate out. This prevents a fast response from obscuring the work needed to repair it.
Editorial implications, not an independently measured result.
Editorial interpretation
Make the hardware decision after the task decision
For this proposed workflow, begin with short records and a modest context. Expand the input only when the job requires it. Treat download size, working memory and acceptable response time as separate checks. Active parameters describe one aspect of computation; they are not a complete memory budget.
A candidate earns further testing when it meets the task criteria within your operational constraints. If it fails, preserve the specific failure: wrong tool arguments, missing evidence, a malformed draft or excessive correction effort. Those observations give the next model comparison a useful starting point.
Editorial implications, not an independently measured result.
The comparison
The table scrolls horizontally on smaller screens.
| Decision | Record |
|---|---|
| Keep evaluating? | Correct drafts, unresolved cases and disqualifying errors |
| Fits this machine? | Checkpoint, settings, measured memory and elapsed time |
| Saves effort? | Review and correction minutes across every attempt |
Read alongside Liquid AI: Run fast, private agents on-device with LFM2.5-24B-A2B / Liquid AI LFM2.5-24B-A2B model card / LFM Open License v1.0.
Keep the question open
What remains unknown
- AgentDaily has not run this model. Its task accuracy, memory use and correction burden in the proposed trial are unmeasured.
Your next decision
Which repeatable job would justify trying this model, and what result would make you keep your current setup?
The evidence record
Primary sources
Source publication dates are reported as stated in the source record. An unstated publication date is not our check date.
- Liquid AI: Run fast, private agents on-device with LFM2.5-24B-A2B huggingface.co
- Source published
- Our check
- Liquid AI LFM2.5-24B-A2B model card huggingface.co
- Source published
- Not stated
- Our check
- LFM Open License v1.0 huggingface.co
- Source published
- Not stated
- Our check
Read critically. Check the original.
Capabilities described by a vendor or paper do not establish performance in your environment. Our editorial standards explain the distinction.