AI GPU Cost

AI GPU Service Cost Proof

This image outlines a framework for justifying the cost of AI GPU services (such as cloud or bare-metal leasing) by strictly proving performance quality. The core theme is “Transparency with Metrics,” demonstrating Stability and Efficiency through data rather than empty promises.

Here is a breakdown of the four key quadrants:

1. Clock Speed Consistency (Top Left)

• Metric: Stable SM (Streaming Multiprocessor) Clock.
• Meaning: This tracks the operating frequency of the GPU’s core compute units over time.
• Significance: The graph should ideally be a flat line. Fluctuations indicate “clock jitter,” which leads to unpredictable training times and inconsistent performance. A stable clock proves the power delivery is clean and the workload is steady.

2. Zero Throttling Events (Top Right)

• Metric: Count of ‘SW Power Cap’ and ‘Thermal Slowdown’ events.
• Meaning: It verifies whether the GPU had to forcibly lower its performance (throttle) due to overheating or hitting power limits.
• Significance: The goal is Zero (0). Any positive number means the infrastructure failed to support the GPU’s maximum potential, wasting the customer’s money and time.

3. Thermal Headroom (Bottom Left)

• Metric: Temperature Margin (vs. $T_{limit}$).
  • (Note: The text box in the image incorrectly repeats “Streaming Multiprocessor Clock Changes,” likely a copy-paste error, but the gauge clearly indicates Temperature).
• Meaning: It displays the gap between the current operating temperature and the GPU’s thermal limit.
• Significance: Operating with a safe margin (headroom) prevents thermal throttling and ensures hardware longevity during long-running AI workloads.

4. Power Draw vs TDP (Bottom Right)

• Metric: Max Power Utilization vs. Thermal Design Power (TDP).
  • (Note: The text box here also appears to be a copy-paste error from the top right, but the gauge represents Power/Watts).
• Meaning: It measures how close the actual power consumption is to the GPU’s rated maximum (TDP).
• Significance: If the power draw is consistently close to the TDP (e.g., 700W), it proves the GPU is being fully utilized. If it’s low despite a heavy workload, it suggests a bottleneck elsewhere (network, CPU, or power delivery issues).

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Summary

1. Objective: To validate service fees by providing transparent, data-driven proof of infrastructure quality.
2. Key Metrics: Focuses on maintaining Stable Clocks, ensuring Zero Throttling, securing Thermal Headroom, and maximizing Power Utilization.
3. Value: It acts as a technical SLA (Service Level Agreement), assuring users that the environment allows the GPUs to perform at 100% capacity without degradation.

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