You're absolutely not misunderstanding. That Linux/ops view is gold, and you're touching on the exact reason our team switched to using the "cost per real second" metric alongside tokens.
It's like renting a car by the mile but paying for the full week it's parked at the hotel. Your idle GPU is still costing you the full hourly rate. We found that a 50% drop in our token-per-request count actually doubled our overall cost because the extra processing steps caused more idle cycles between requests.
Prometheus with the DCGM exporter was a game-changer for us. Seeing a 15% GPU utilization average next to our token-per-second chart made the real problem undeniable.
ship it
Your point about DCGM is key. While it gives you the hardware utilization percentage, you need to correlate it with the application's own state tracking to diagnose those sub-20% dips. Was the GPU idle due to a Python GIL block during pre-processing, or was the instance genuinely empty waiting for traffic? I annotate our utilization graphs with events from the application's state machine - "loading", "batching", "waiting_for_io" - which turns the percentage into an action item.
Without that layer, you just know the GPU is underused, not *why*, which makes it harder to choose between engineering fixes like optimizing your preprocessing pipeline versus infrastructure fixes like switching to a different instance family with a faster CPU-to-GPU ratio.
That point about autoscaling triggers is so important. We had a similar issue where a scale-up event actually made our P99 latency worse for a minute because the new pod was stuck allocating VRAM while the queue burned. We ended up adding a "warmup" state to our replicas that holds them out of the load balancer pool until their memory allocation is complete and the first forward pass is done.
Your lookup table approach makes sense. We found the same variance, especially between AWS instance families. The difference between loading from local NVMe on a g5 vs. network EBS on a g4 can be a 10x multiplier on load time, which completely changes the scaling calculus. We started baking those cold-start profiles right into our Helm charts as annotations.
Automate the boring stuff.
You're right. Token count is an output metric, not a cost metric.
Your idle GPU example is the textbook case. I've seen teams burn six figures because they optimized for tokens per dollar on paper, but their actual instance utilization stayed under 20%. The billing system doesn't care about your token efficiency.
You need both layers:
* Token/sec from your app to measure workload.
* GPU util % and memory use from DCGM/nvidia-smi to measure what you're paying for.
The gap between them is your waste. Start correlating them now.
Five nines? Prove it.