A local hardware test has reignited doubts about LFM2.5-24-A2B. Redditor tracagnotto, after being criticised for a first post considered too superficial, published a follow-up with explicit configuration: an RTX A4500 card, a switch from llamaampere to LM Studio, and an agentic task on Hermes. The verdict is stark: the model would not be trusted to peel a peanut.

Running with llamaampere, an optimised build of llama.cpp, produced a sharp contrast. On one side, 130 tokens per second on a professional GPU; on the other, repeated loops and errors on simple requests. It is a classic case of high throughput saying nothing about inference quality when the model loses coherence. For anyone evaluating on-premises deployments, separating generation speed from task completion is the first step to avoid surprises.

The problem is not the template

Moving to LM Studio, with engines and frameworks updated, the erratic behaviour subsided: chat responses were "correct-ish". The real test came when the user connected the model to Hermes Desktop and asked it to build a plugin for Garnet, Microsoft's Redis clone. The result, analysed by ChatGPT during prompt engineering and checked with GPT Sol, was clear: broken code, missing required functionality, and a verification claimed without end-to-end tests. Applying the Sharp template fix did not change the substance.

The case matters not for the single judgement but for what it reveals about the metrics used to choose an LLM for local workloads. LFM2.5-24-A2B is presented as trained on agentic harnesses like openclaw and Hermes, yet it fails precisely on the task that should be its strength. That suggests synthetic benchmarks or chat demos are not enough when a model must interact with an external runtime, produce working code, and avoid hallucinated verification. Who benefits from this mismatch? Vendors pushing speed numbers and fine-tuning on agentic datasets, because they capture initial attention. Who loses? Teams that integrate these models into self-hosted pipelines without realistic tests and then have to clean up production errors.

There is a second-order implication. The local community initially reacted with scepticism, asking for configuration details. The more documented follow-up shows the problem was not a wrong template but a structural model limitation. For those running on-premises infrastructure, the bottleneck is not always VRAM or tokens per second, but the ability to complete a task without human intervention. An RTX A4500 can generate fast, but if the code is broken, TCO rises through debugging hours. For those evaluating on-premises deployment, AI-RADAR offers analytical frameworks at /llm-onpremise to compare these trade-offs; here the lesson is even more direct: synthetic tests do not replace an end-to-end agentic task.