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Green MLOps: Closed-Loop, Energy-Aware Inference with NVIDIA Triton, FastAPI, and Bio-Inspired Thresholding

Mustapha Hamdi, Mourad Jabou

TL;DR

Green MLOps proposes a closed-loop, bio-inspired control system to reduce the energy footprint of inference by gating requests with a decaying threshold over a dual-path serving stack (FastAPI+ORT for low-latency and Triton for batching). The method formalizes a cost J(x)=αL(x)+βE(x)+γC(x) and a threshold τ(t) to admit only high-utility, low-cost requests, drawing an analogy to protein folding to avoid wasteful energy incursions. Empirical results on DistilBERT and ResNet-18 show a 42% reduction in processing time with minimal accuracy loss, and the work clarifies the trade-offs between local and batching-based serving in production. The approach provides an auditable, energy-aware inference workflow that can guide Green MLOps deployments in real-world systems.

Abstract

Energy efficiency is a first-order concern in AI deployment, as long-running inference can exceed training in cumulative carbon impact. We propose a bio-inspired framework that maps protein-folding energy basins to inference cost landscapes and controls execution via a decaying, closed-loop threshold. A request is admitted only when the expected utility-to-energy trade-off is favorable (high confidence/utility at low marginal energy and congestion), biasing operation toward the first acceptable local basin rather than pursuing costly global minima. We evaluate DistilBERT and ResNet-18 served through FastAPI with ONNX Runtime and NVIDIA Triton on an RTX 4000 Ada GPU. Our ablation study reveals that the bio-controller reduces processing time by 42% compared to standard open-loop execution (0.50s vs 0.29s on A100 test set), with a minimal accuracy degradation (<0.5%). Furthermore, we establish the efficiency boundaries between lightweight local serving (ORT) and managed batching (Triton). The results connect biophysical energy models to Green MLOps and offer a practical, auditable basis for closed-loop energy-aware inference in production.

Green MLOps: Closed-Loop, Energy-Aware Inference with NVIDIA Triton, FastAPI, and Bio-Inspired Thresholding

TL;DR

Green MLOps proposes a closed-loop, bio-inspired control system to reduce the energy footprint of inference by gating requests with a decaying threshold over a dual-path serving stack (FastAPI+ORT for low-latency and Triton for batching). The method formalizes a cost J(x)=αL(x)+βE(x)+γC(x) and a threshold τ(t) to admit only high-utility, low-cost requests, drawing an analogy to protein folding to avoid wasteful energy incursions. Empirical results on DistilBERT and ResNet-18 show a 42% reduction in processing time with minimal accuracy loss, and the work clarifies the trade-offs between local and batching-based serving in production. The approach provides an auditable, energy-aware inference workflow that can guide Green MLOps deployments in real-world systems.

Abstract

Energy efficiency is a first-order concern in AI deployment, as long-running inference can exceed training in cumulative carbon impact. We propose a bio-inspired framework that maps protein-folding energy basins to inference cost landscapes and controls execution via a decaying, closed-loop threshold. A request is admitted only when the expected utility-to-energy trade-off is favorable (high confidence/utility at low marginal energy and congestion), biasing operation toward the first acceptable local basin rather than pursuing costly global minima. We evaluate DistilBERT and ResNet-18 served through FastAPI with ONNX Runtime and NVIDIA Triton on an RTX 4000 Ada GPU. Our ablation study reveals that the bio-controller reduces processing time by 42% compared to standard open-loop execution (0.50s vs 0.29s on A100 test set), with a minimal accuracy degradation (<0.5%). Furthermore, we establish the efficiency boundaries between lightweight local serving (ORT) and managed batching (Triton). The results connect biophysical energy models to Green MLOps and offer a practical, auditable basis for closed-loop energy-aware inference in production.
Paper Structure (22 sections, 5 equations, 5 figures, 3 tables)

This paper contains 22 sections, 5 equations, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Models and Serving Architecture
  4. Models
  5. Dual-path serving
  6. Instrumentation
  7. Closed-Loop Thresholding: Formulation
  8. The Closed-Loop Controller: Operation and Biological Analogy
  9. Experimental Methodology
  10. Results
  11. Summary table
  12. Throughput
  13. Latency--Energy trade-off
  14. Energy landscape sketch
  15. Impact of Bio-Inspired Thresholding (Ablation)
  16. ...and 7 more sections

Figures (5)

  • Figure 1: Closed-loop, dual-path serving architecture (controller, FastAPI+ORT path, Triton path) with feedback via MLflow and CodeCarbon updating $\tau(t)$.
  • Figure 2: Throughput comparison (req/s) for FastAPI vs. Triton by model.
  • Figure 3: Latency vs. energy; marker size encodes std-dev or throughput.
  • Figure 4: Bio-inspired energy landscape with decaying threshold $\tau(t)=\tau_{\infty}+(\tau_0-\tau_{\infty})e^{-kt}$. The controller selects a local stable basin and ignores the costly global minimum; dashed lines illustrate the evolving $\tau(t)$ and the admit region.
  • Figure 5: Real-world deployment: SmartDiag Radiology Dashboard powered by our Green MLOps stack. The controller manages multimodal inferences for tumor detection (red bounding box), balancing A100 energy consumption against diagnostic latency requirements.