GPU Cost Doctor

GPU Cost Doctor is an AI infrastructure diagnosis tool for teams building and deploying model workloads. Instead of only asking which model to use, it analyzes the workload shape that usually drives GPU waste: context length, batching, latency targets, memory pressure, runtime choice, KV cache risk, and missing benchmark evidence. A user describes their project, model, runtime, traffic target, context length, repository link, and config evidence. The app then generates a doctor’s report with deployment risks, detected project signals, recommended serving paths, and practical benchmark steps. It also supports fine-tuning readiness checks by looking for PEFT/LoRA signals, validation gaps, reproducibility gaps, and post-tuning serving requirements. GPU Cost Doctor generates useful artifacts such as a ROCm Dockerfile, vLLM serve command, benchmark script, deployment README, and LoRA fine-tuning starter command. The benchmark section helps validate recommendations using p50 latency, p95 latency, throughput, token speed, and benchmark history. The goal is to help AI teams move from prototype to production with evidence, not guesswork.