A developer measured LFM2.5-24B-A2B at roughly 37 tokens per second on an ordinary 780M integrated GPU—the model’s sustained generation speed, which typically slows as the text grows—and concluded that the model balances task capability with speed. These community results show that the barrier to local AI is moving away from high-end GPUs.
What this is
LFM2.5-24B-A2B is a mixture-of-experts model released by Liquid AI. It has 24 billion total parameters, but activates only around 2 billion for each token, reducing computation and memory requirements. Hermes is an agentic framework that lets large language models call external tools, break down tasks, and execute them by translating requests into concrete actions.
The author positioned it as a “task executor,” not a coding model: it does not directly write complex code, but takes on decomposed work of low-to-medium complexity. A quantized GGUF version is available, compressing model parameters into a format better suited to local deployment, and can run on CPUs or consumer-grade GPUs.
Industry view
The important development is that local models are shifting from a “parameter race” toward maximizing the amount of work completed per unit of hardware. At roughly 37 tokens per second, local deployment is no longer limited to demos; it can potentially handle continuous tasks such as document processing, information organization, and tool invocation. Combined with Hermes, companies can route complex requests to stronger models or external services while reserving the small model for execution.
That said, this is a single developer’s result on a specific device, context length, and quantized version, and it cannot be generalized to every task. The model is not designed for programming, and its complex reasoning, long-document processing, and reliability remain insufficiently validated. Community posts may also cherry-pick results, while unified benchmarks, power consumption, memory usage, and failure-rate comparisons are still missing.
Local deployment also carries hidden costs: equipment procurement, model management, access control, and output monitoring all require investment. For most companies, pay-as-you-go cloud services may be the easier option. Local deployment makes more sense for organizations with sensitive data, stable workloads, or existing hardware.
Impact on regular people
For enterprise IT: Local models may complement cloud models by first handling internal data classification, summarization, and normalization, reducing exposure of sensitive information and the cost of high-frequency calls.
For individual professionals: People who understand code can use ordinary computers to build tools that automate workflows, but non-technical users will still face installation, debugging, and permission-configuration hurdles.
For the consumer market: Offline summarization, file retrieval, and personal knowledge assistants will gradually become standard features on PCs and phones. Device computing power will no longer have to rely entirely on the cloud, but the actual experience will depend on memory, speed, and software compatibility.