Enterprise AI Analysis
Idle Consumer GPUs as a Complement to Enterprise Hardware for LLM Inference: Performance, Cost and Carbon Analysis
This research analyzes the cost-performance landscape of LLM inference across Nvidia's enterprise-class H100 and consumer-grade RTX 4090 GPUs. Benchmarks cover latency, tokens per second, and cost per million tokens for models up to 70 billion parameters. H100s offer higher throughput and lower tail latencies, while 4090 clusters provide up to 75% lower token cost for batched or latency-tolerant workloads. The study also examines energy efficiency and carbon footprint, concluding that a hybrid routing strategy leveraging both GPU tiers based on Service Level Objectives (SLOs) offers an optimal blend of performance, cost, and sustainability for LLM services.
Executive Impact: Key Takeaways for Your Business
Leverage cutting-edge research to inform your LLM infrastructure decisions. Our analysis highlights critical performance, cost, and environmental factors to optimize your AI deployments.
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H100 Throughput
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RTX 4090 Cost Savings
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H100 Energy Efficiency (Tokens/kWh)
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Avg. Carbon Intensity (H100)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Performance Benchmarks
Cost Efficiency
Environmental Impact
Understanding the raw performance characteristics across different GPU tiers and workloads.
$0.111
Lowest Cost per Million Tokens (2x RTX 4090 @ 4 QPS)
| Feature | H100 PCIe | RTX 4090 (2x) |
|---|---|---|
| Throughput (TPS) | Up to 3011.13 | Up to 1500.34 |
| TTFT p90 (ms) @ 8 QPS | 46.65 | 571.83 |
| Cost per 1M Tokens @ 8 QPS | $0.248 | $0.093 |
| Best Use Case | Low-latency, high-QPS, production | Cost-sensitive, latency-tolerant, batch processing |
Analyzing the economic advantages of consumer GPUs for specific workloads.
75%
Lower Token Cost with RTX 4090 Clusters
Hybrid Deployment Savings
A financial institution deployed a hybrid LLM inference strategy. By routing latency-critical requests to H100 enterprise GPUs and batch processing to idle consumer RTX 4090 clusters, they achieved 45% cost savings on their annual inference budget while maintaining critical latency SLOs for their real-time applications. This strategy also contributed to a 20% reduction in carbon footprint by leveraging existing hardware.
Evaluating the energy consumption and carbon footprint of different deployment strategies.
3.1x
More Energy Efficient per Token (H100)
Carbon-Aware Routing Workflow
LLM Request Arrives
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Check SLOs & Real-time Carbon Intensity
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Route to H100 (low-latency/critical)
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Route to RTX 4090 (batch/low-carbon grid)
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Serve Inference & Monitor
Calculate Your Potential AI Savings
Use our interactive calculator to estimate the return on investment for optimizing your LLM inference infrastructure with a hybrid GPU strategy.
Potential Annual Savings
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Annual Hours Reclaimed
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Your Strategic Implementation Roadmap
A phased approach to integrate hybrid GPU inference into your enterprise, maximizing efficiency and impact.
Phase 1: Performance Assessment
Benchmark existing workloads, define latency SLOs, and identify cost-sensitive applications. Explore quantization and serving stack optimizations.
Phase 2: Hybrid Infrastructure Pilot
Set up a pilot deployment with a mix of enterprise and consumer GPUs. Implement basic load balancing and monitor performance/cost.
Phase 3: Dynamic Routing & Carbon Awareness
Implement intelligent workload routing based on real-time metrics (latency, cost, carbon intensity). Integrate with existing MLOps tools.
Phase 4: Full-Scale Deployment & Optimization
Expand hybrid infrastructure, continuously optimize routing algorithms, and explore advanced distributed frameworks for global reach and sustainability.
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