Set the total facility power envelope, PUE, rack count, physical density and cooling. The explorer calculates how many accelerators can be active while keeping physical capacity, training throughput and inference throughput as separate questions.
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Each bar shows how many accelerators that constraint would support on its own. The publishable capacity is the smallest value, so available power is never mistaken for physical or cooling capacity.
Usable IT capacity: —
IT reserve: —
Total-power headroom: —
Total-envelope utilization: —
Maximum active per rack: —
IT load at that maximum: —
Electrical headroom per rack: —
Thermal headroom per rack: —
Active-fleet dense BF16 peak: —
Sustained compute at MFU: —
Sustained compute per day: —
Installed slots that can actually be used: —
Low utilization here does not imply poor efficiency. Power or cooling may simply bind before physical space does.
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Total facility power is divided by a representative PUE to approximate IT capacity. The operational reserve is then removed.
Each accelerator adds average GPU power plus associated IT. That footprint is checked independently against installed slots, rack electrical power and rack heat-removal capacity.
Training uses dense BF16 peak × MFU. Inference is calculated only when you enter measured sustained throughput from your stack; FLOPs are never converted into tokens/s.
PUE is an energy ratio and can change with load and climate. TDP is not average power. The associated-IT approximation is linear even though CPU, network and storage do not always scale that way. Thermal capacity must come from the actual rack and cooling design. MFU depends on workload and software. Inference throughput depends on model, precision, context, batching, runtime and SLO, which is why this explorer only aggregates a throughput measurement that you provide.