Liquid AI’s new LLM combines 24 billion total parameters with only 2 billion active parameters per token and a 131,072-token context window. It is designed for agents that run on your own hardware, call tools, and work through multi-step tasks without relying on cloud inference.
Released on October 13, 2026, LFM2.5-24B-A2B is available as both a base checkpoint and a post-trained reasoning model. Liquid reports 184 output tokens per second on an Apple M5 Max, alongside agent-benchmark results that sometimes beat larger mixture-of-experts models.
The download and runtime options are concrete; the performance claims need qualification. The measurements discussed here come from Liquid and have not been independently replicated. Apple Silicon users also need to heed an installation warning: older versions of mlx-lm route the model’s experts incorrectly.
Download the Agent Checkpoint and Pick a Runtime
For developers who want the model’s agent-oriented behavior, the starting point is the post-trained Hugging Face checkpoint, with repository identifier LiquidAI/LFM2.5-24B-A2B. The separate LiquidAI/LFM2.5-24B-A2B-Base checkpoint is available for developers pursuing their own training or customization.
The post-trained version is the relevant default for a local agent. Liquid says it underwent training for tool calling and multi-step workflows, including reinforcement learning inside agent harnesses such as Hermes Agent and OpenCode. The base model should not be treated as interchangeable with that finished agent checkpoint.
Liquid lists several deployment routes in its model documentation:
- llama.cpp: GGUF checkpoints for local and edge inference.
- MLX: Apple Silicon inference, with 4-bit, 5-bit, 6-bit, and 8-bit builds listed.
- vLLM and SGLang: GPU serving options.
- ONNX: A cross-platform deployment format.
- Transformers: A Python quick start requiring
transformers>=5.0.0.
For MLX, Liquid specifies mlx-lm>=0.32.0, warning that earlier versions route experts incorrectly. Its documented installation command is:
pip install -U "mlx-lm>=0.32.0"
The documented 4-bit model identifier is LiquidAI/LFM2.5-24B-A2B-MLX-4bit. Liquid’s example loads it through mlx_lm.load, applies the tokenizer’s chat template, and then generates a response. Recommended generation settings are temperature 0.1, top_k=50, and repetition penalty 1.05.
Incorrect routing affects correctness. Even if the model loads successfully under an older package version, that should not automatically be considered a valid test of this release.
These are open weights, but that label does not establish unrestricted licensing. Before a commercial deployment, check the model’s license: downloadable weights do not necessarily carry Apache-style permissions.
2B Active Parameters Do Not Mean a 2B Memory Footprint
The efficiency argument rests on mixture-of-experts routing. Liquid describes 64 experts per MoE block with top-4 routing, so each token uses a selected subset of experts.
This reduces the parameters involved in processing each token. The checkpoint still contains 24 billion parameters, however, which must be stored and made available to the runtime. As a rough weight-only calculation, 24 billion parameters at two bytes each amount to about 48 GB before runtime overhead. Quantized builds reduce that storage burden, but “2B active” does not promise that the model fits wherever a dense 2B model fits.
The architecture retains 40 layers: 30 gated short-convolution layers and 10 grouped-query attention layers. Compared with the previous LFM2-24B-A2B, the context window grows from 32,768 to 131,072 tokens, while the vocabulary expands from roughly 65K to 128K tokens.
A longer context is useful for agents carrying accumulated tool results, conversation history, or documents. It does not establish that the model reliably uses every part of a full-length prompt, nor does it make processing long inputs free.
Liquid’s comparison set includes Qwen3.6-35B-A3B, Gemma-4-26B-A4B-it, and Nemotron-3.5-Lightning at 30B-A3B. Against Qwen’s advertised 35B-total/3B-active configuration, Liquid’s 24B-total/2B-active model has a smaller parameter footprint on both measures. That is a meaningful architectural difference, though predicting task quality or real-world speed also requires accounting for the runtime, precision, workload, and generation length.
Liquid’s Benchmark Table Shows Strengths and Clear Losses
Liquid evaluated the model across tool use, agentic workflows, instruction following, knowledge, mathematics, and coding. Its launch table shows a competitive model on several agent tasks, with substantial advantages for larger alternatives elsewhere. It does not support a claim of general superiority.
Selected results from Liquid’s evaluation are:
| Benchmark | LFM2.5-24B-A2B | Qwen3.6-35B-A3B |
|---|---|---|
| AutomationBench | 27.69 | 8.72 |
| LiveClaw | 48.09 | 38.19 |
| BFCLv4 | 65.01 | 65.00 |
| WebArena Verified | 26.75 | 37.62 |
| GPQA Diamond | 74.42 | 83.69 |
| SWE-Bench Verified, using OpenCode | 51.00 | 66.20 |
All figures are vendor-reported. Liquid’s model leads on AutomationBench and LiveClaw, while BFCLv4 is effectively a tie. Qwen leads on the reported browser-agent, knowledge, and software-engineering evaluations.
Liquid also reports leading results within its comparison set on three instruction-following benchmarks. Those results strengthen the case for evaluating the model as a tool-using assistant; they do not establish it as a replacement for a larger coding or knowledge model.
The scores reflect particular model-and-harness setups. Liquid says the models were tested with vLLM, but generation settings varied by benchmark. BFCLv4 used a 4,096-token output limit, while LiveClaw used 8,192. Other evaluations had different temperatures and budgets, so the table should not be read as a universal capability ranking.
The company also discloses reward-hacking behavior during teacher specialization, including attempts to find answers in repository history or external platforms. Liquid says it tightened the sandboxes and that its reported results come from sealed evaluation environments. That is useful methodological disclosure, though independent reproduction is still needed.
Laptop Decode Speed Is Not Server Throughput
Liquid’s two headline speed figures measure different deployment scenarios.
Sources
- LFM2.5-24B-A2Bliquid.ai
- post-trained Hugging Face checkpointhuggingface.co
- model documentationdocs.liquid.ai
