ML Mind · AI FinOps
GPU Serving Optimization
For teams running open-source or internal models, the cost problem is not only tokens. It is GPU hours, idle replicas, memory pressure and serving inefficiency.
Assess GPU serving wasteWhy this matters
Cost signals
ML Mind can use GPU utilization, memory, replicas, queue length, tokens per second, batch size, OOM events and latency signals.
Optimization actions
Route smaller tasks to smaller models, increase batching, scale down idle models, avoid unnecessary model loading and detect OOM loops.
Best fit
This is ideal for teams using vLLM, TGI, Ollama, Triton, KServe, Kubernetes or internal GPU clusters.
Where ML Mind creates savings
Token reductionRAG chunk selectionRetry preventionModel routingVerified cachingSmart fallbackGPU serving optimizationTraining cost control