Replace busy percent with useful output.

Video caption

Replace busy percent with useful output.

Quiet success and contended failure can share one badge.

Hardware diagnostics beside accepted outcomes.

Use counters to repair the gap.

#EricFieldNotes

Full written post / accessibility read

A high utilization badge is comforting because it is one number. But NVIDIA's profiling metrics separate SM, tensor, memory and interconnect activity. A GPU can be busy waiting on data or working on requests that miss the user's latency budget.

Suppose the afternoon peak has slow responses but the overnight batch keeps the GPU active. A daily average can look healthy while the customer fails exactly when they need the service. We need distributions by workload and load, not one fleet mean.

Panel one shows SM, tensor, memory and link activity over the interval. Panel two shows requests meeting quality and latency targets, p ninety-five completion, cancellations and cost. Link each customer workload to its own allocation and queue time.

When accepted output falls, use the device counters to find whether compute, memory, fabric or queue is limiting it. Do this because utilization only becomes useful when it helps explain and improve the delivered result.

#EricFieldNotes

Four-beat scene transcript

1. Replace busy percent with useful output.

A high utilization badge is comforting because it is one number. But NVIDIA's profiling metrics separate SM, tensor, memory and interconnect activity. A GPU can be busy waiting on data or working on requests that miss the user's latency budget.

Visual: The buyer needs results, not a hardware mood ring.

2. The average erases the painful minutes.

Suppose the afternoon peak has slow responses but the overnight batch keeps the GPU active. A daily average can look healthy while the customer fails exactly when they need the service. We need distributions by workload and load, not one fleet mean.

Visual: Quiet success and contended failure can share one badge.

3. Make two paired panels.

Panel one shows SM, tensor, memory and link activity over the interval. Panel two shows requests meeting quality and latency targets, p ninety-five completion, cancellations and cost. Link each customer workload to its own allocation and queue time.

Visual: Hardware diagnostics beside accepted outcomes.

4. Manage to completed work.

When accepted output falls, use the device counters to find whether compute, memory, fabric or queue is limiting it. Do this because utilization only becomes useful when it helps explain and improve the delivered result.

Visual: Use counters to repair the gap.

Research and claim limits

• NVIDIA DCGM profiling metrics (S121)

The examples identified as illustrative or simulated are design probes, not reported incidents. Vendor specifications do not establish workload performance.