Enterprise AI Analysis
EcoServe: Designing Carbon-Aware Al Inference Systems
The paper 'EcoServe: Designing Carbon-Aware Al Inference Systems' introduces a novel carbon-aware resource provision and scheduling framework for Large Language Model (LLM) serving systems. Motivated by observations from production deployments—GPUs dominating operational carbon while host systems drive embodied carbon, significant offline inference workloads, and hardware/workload heterogeneity—EcoServe applies four principles: Reduce, Reuse, Rightsize, and Recycle (4R). Through a cross-stack ILP formulation, EcoServe aims to lower total carbon emissions by up to 47% compared to performance, energy, and cost-optimized designs, while maintaining performance targets and SLOs. Key findings include leveraging idle CPUs for offline inference to amortize embodied carbon, dynamic GPU/CPU provisioning based on workload characteristics, minimizing unnecessary host resources, and extending hardware lifetimes to balance operational and embodied emissions. This approach shifts AI infrastructure design towards holistic carbon awareness without sacrificing performance.
Executive Impact: Key Metrics
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Reduction in total carbon emissions
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Carbon savings for offline & online serving
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Offline inference portion of serving capacity
Deep Analysis & Enterprise Applications
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Key Observations
EcoServe Principles (4R)
Implementation & Impact
50%+
Embodied carbon accounts for over 50% of total emissions with renewable energy
6%
Average CPU utilization in AI inference systems
Carbon Footprint Breakdown
GPUs dominate operational carbon
→
Host Systems (CPUs, Memory, Storage) dominate embodied carbon
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Offline batch inference up to 55% of capacity
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Workload/Hardware Heterogeneity
| Component | Operational Carbon Impact | Embodied Carbon Impact |
|---|---|---|
| GPUs |
|
|
| Host Systems (CPU, Memory, Storage) |
|
|
The 4R Framework: Reduce, Reuse, Rightsize, Recycle
EcoServe's core is built on four sustainability principles. These are applied across system operation dimensions to minimize carbon impact.
- Reduce: Eliminates unnecessary host memory and storage resources to reduce embodied carbon overhead.
- Reuse: Exploits underutilized host CPUs for offline inference to amortize embodied carbon across workload phases.
- Rightsize: Dynamically provisions GPUs and CPUs based on model size, execution phase, and workload demand to avoid over-provisioning.
- Recycle: Extends the lifetime of host processing systems while selectively upgrading accelerators to balance embodied and operational carbon.
EcoServe Design Flow
Hardware Specs
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LLM Model Characteristics
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Production Traces
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Carbon Intensity Data
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EcoServe Framework (4R Strategies)
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Optimized Scheduling & Resource Allocation
1.32x
Reduction in offline GPU provisioning needs with CPU reuse
| Strategy | Carbon Savings (Online) | Carbon Savings (Offline) |
|---|---|---|
| Reduce | 12.4-28.6% | Less significant (conservative DRAM matching) |
| Reuse | 25.4% (CPU for offline) | Significant (offline CPU decoding) |
| Rightsize | 15.2-30.3% (SLO slacks, load variability) | Significant (ILP for GPU disaggregation) |
| Recycle | 16.8% (homogeneous update baseline) | 16.8% (homogeneous update baseline) |
3.67x
Speedup for CPU-based inference over baseline llama.cpp
End-to-End Carbon Reduction
EcoServe's combined strategies demonstrate substantial total carbon savings across diverse workloads.
- EcoServe variants achieve significant carbon savings compared to performance-optimal configurations.
- On average, EcoServe yields 47% carbon savings by combining strategies.
- Performance degradation is minimal (within 3% for most variants) while achieving significant carbon reduction.
- Load-aware CPU reuse reduces offline GPU provisioning needs by up to 1.32x at peak demand.
Calculate Your Potential Carbon Savings
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Estimated Annual Carbon Savings (kgCO2e)
$-
Equivalent Operational Hours Reclaimed
- hours
Phased Approach to Carbon-Aware AI
Our structured roadmap ensures a smooth transition to an optimized, sustainable AI infrastructure.
Discovery & Assessment
Analyze existing infrastructure, current carbon footprint, and performance bottlenecks.
Strategy & Design
Develop a tailored EcoServe framework leveraging 4R principles, including hardware/software co-design.
Pilot & Integration
Implement EcoServe on a pilot scale, integrate with existing MLOps pipelines, and validate performance/carbon metrics.
Scale & Optimize
Full-scale deployment, continuous monitoring, and iterative optimization based on real-time data and carbon intensity.
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