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).
Summary
- Objective: To validate service fees by providing transparent, data-driven proof of infrastructure quality.
- Key Metrics: Focuses on maintaining Stable Clocks, ensuring Zero Throttling, securing Thermal Headroom, and maximizing Power Utilization.
- 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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