Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Running your own AI model sounds exciting, but one question appears almost immediately: what kind of server do you actually need? A small language model can run on a personal workstation, while a large 70B parameter model may require enterprise-grade GPUs and expensive infrastructure. Choosing the wrong hardware can lead to wasted money, unnecessary complexity, or disappointing performance.

There's a version of the LLM safety conversation that stays comfortably abstract - AI alignment, existential risk, theoretical failure modes that matter at a scale most organizations will never reach. That conversation is important, but it's not the one most product and technology leaders need to be having right now. The one they need to be having is more immediate and considerably more practical: how the specific decisions made during llm training services directly shape whether the model you deploy is one your organization can actually stand behind.