From LM Studio to a stable in-house LLM service

For a small team or pilot, yes. With many concurrent users we recommend vLLM or another production serving.

LM Studio excels in GUI and non-technical adoption; Ollama in CLI automation. We often use both in different phases.

It depends on the task and the memory of the box. We propose open-weight candidates and validate them with your test cases.

Yes, it is one of the strengths of Halo/Strix Halo. We validate the ROCm/Vulkan stack according to the workload.

In many cases, yes: we point the client to the supported local API. Authentication and limits must be reviewed.

The base deployment is inference. Fine-tuning LoRA or others is quoted if the diagnosis warrants it.

We define runbooks and accountability. We can operate for a period or transfer to IT.

Yes, if there is no update process. That is why we include periodic evaluation procedures and criteria.

In strict cases, we design a dead-end deployment after initial provisioning. It has update implications that we explain.

Deployed runtime, agreed models, documented API, credentials/accesses, and operation guidance.