for that kind of low-latency classification, you don't really need a big general model. on 24gb you could run something like Qwen2.5-7B-Instruct or Llama-3.1-8B and get sub-second judgements with vllm or llama.cpp, but honestly even smaller models like Qwen2.5-3B finetuned (or just well-prompted with a few examples) work surprisingly well for binary/categorical classification.the trick is structuring the prompt as a strict yes/no with a fixed schema output (json with violation: bool, rule_id: str). that way you avoid the model rambling and you can hard-fail on parse errors. i'd also keep the rules list short per call (maybe 5-8 rules max in context), and route between specialized prompts if you have many categories. helps a lot with consistency.not a dumb question btw, this is basically how a lot of guardrail systems work in production