# MARLIN: Achieving Sustainable Large Model Inference Services via Multi-Agent Game Reinforcement Learning

> The Google Research Team proposes the MARLIN framework, which simultaneously optimizes latency, carbon emissions, water consumption, and energy consumption for large model inference using multi-agent game reinforcement learning. While reducing Time to First Token (TTFT) by 18%, it achieves a 33% reduction in carbon emissions, a 43% decrease in water consumption, and an 11% saving in energy consumption.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-05-13T13:20:02.000Z
- 最近活动: 2026-05-14T02:47:34.206Z
- 热度: 126.5
- 关键词: LLM推理, 绿色AI, 强化学习, 多智能体系统, 数据中心, 碳排放, 可持续计算
- 页面链接: https://www.zingnex.cn/en/forum/thread/marlin
- Canonical: https://www.zingnex.cn/forum/thread/marlin
- Markdown 来源: floors_fallback

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## MARLIN Framework Overview: Achieving Sustainable LLM Inference Services via Multi-Agent Game Reinforcement Learning

The Google Research Team proposes the MARLIN framework, which simultaneously optimizes latency, carbon emissions, water consumption, and energy consumption for large model inference using multi-agent game reinforcement learning. While reducing Time to First Token (TTFT) by 18%, it achieves a 33% reduction in carbon emissions, a 43% decrease in water consumption, and an 11% saving in energy consumption, providing an innovative solution to the environmental cost problem in the LLM inference phase.

## Background: Environmental Cost Crisis in the LLM Inference Phase

LLM inference requests account for 90% of the energy consumption in the large model lifecycle, far exceeding the training phase. As models enter the production environment, the environmental footprint of inference services accumulates rapidly. Its hidden pollution comes from direct electricity consumption, data center cooling water, carbon emissions from power generation, and power transmission losses, which has become a core challenge that the industry urgently needs to solve.

## Core Design of MARLIN Framework: Multi-Agent Game Reinforcement Learning

MARLIN models inference scheduling as a multi-party game process, where different optimization objectives (latency, carbon emissions, water consumption, energy consumption) act as agents participating in the game. The framework innovatively optimizes four metrics simultaneously: TTFT, carbon emissions, water consumption, and energy cost. It finds the Pareto optimal solution through Nash equilibrium and uses reinforcement learning to adapt to real-time changes in power carbon intensity, water resource scarcity, and workload characteristics.

## Experimental Results: Significant Improvements in Both Performance and Greenness

In the evaluation of a real cloud data center environment, MARLIN achieved the following improvements compared to the current state-of-the-art framework:
| Metric | Improvement |
|--------|-------------|
| Time to First Token (TTFT) | 18% reduction |
| Carbon emissions | 33% reduction |
| Water consumption | 43% reduction |
| Energy cost | 11% saving |
These improvements did not sacrifice service quality, breaking the myth that "green computing must sacrifice performance."

## Key Technical Insights: Spatiotemporal Awareness and Multi-Agent Collaboration

The success of MARLIN comes from three key technical insights: 1. Spatiotemporal-aware scheduling: Captures the spatiotemporal heterogeneity of data center environmental impacts to achieve intelligent request routing; 2. Multi-agent collaboration: Shares information through communication mechanisms to avoid local optima; 3. Online learning adaptation: Can handle changing workloads and environmental conditions without offline retraining.

## Industry Significance and Future Outlook

MARLIN provides a sustainable development path for cloud service providers, helping enterprises reduce API costs and environmental footprints, and supporting the green transformation of the AI industry. In the future, it will expand to more environmental metrics (such as electronic waste), integrate renewable energy prediction scheduling, and explore distributed optimization in federated learning scenarios. The relevant code has been open-sourced.