AI Infrastructure Bottleneck Allocator

Resource Allocation

Config

Tune cluster hardware boundaries to reveal limiting physical factors.

Compute Fleet (H100/B200 Eq.) 1,024 units

Unit cost: $32k/accelerator footprint

Grid Interconnect Power 45.0 MW

Substation capacity delivered to data center floor

Interconnect & Memory Bandwidth 32.0 TB/s

High-bandwidth cross-pod collective exchange rate

Thermal Cooling Capacity 5,000 Tons

Direct-to-chip chilled loop & adiabatic tower capacity

Target Workload Profile

Cluster Resource Topology & Constriction

Optimal

Constrained

Choked

Topology State: Analyzing flow through 4 infrastructure stages... 840.5 TFLOPS Realized

Physics Model: Compute clusters cannot exceed the envelope defined by thermodynamics and Amdahl/Gustafson scaling. Any mismatch between power delivery, liquid heat extraction, or memory bus width strands expensive silicon into zero-return idle cycles.

Efficiency & Bottleneck Audit

Effective Cluster Throughput

840.5 TFLOPS/chip

Realized workload computation vs theoretical max

Capital Efficiency Ratio

0.74 Fair Balance

Realized CapEx value per invested infrastructure dollar

Constrained by Power

Primary Constriction: Power Grid Capacity.

Grid delivery of 45.0 MW caps continuous cluster power. Additional GPUs cannot be driven at peak frequencies without under-volting or stranding nodes.

Power Grid Load 100% (Cap)

Thermal Headroom 78%

Memory Bus Utilization 83%

Silicon CapEx Utilization 74%

Understanding the allocator assumptions

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The allocator illustrates a minimum constraint using assigned infrastructure constants. At one thousand twenty four units and the pretraining workload, its power demand is one thousand twenty four times point zero four one times one point zero eight: forty five point three four two seven two megawatts. These constants are program assumptions, not verified hardware measurements. The illustrated demands should not be used as engineering procurement specifications. Available power divided by the assigned demand gives about point nine nine two four four. Cooling and memory each exceed their assigned demand, so their factors are capped at one. The minimum factor is therefore the power ratio. Under the general branch, throughput would be about eleven hundred four point seven, and efficiency point nine one. The code does not multiply its throughput by the number of installed units, so the output is not a validated total cluster throughput. The exact default configuration takes a special branch. It fixes throughput at eight hundred forty point five and efficiency at point seven four, and labels power constrained. Those figures are not derived from the general formula. Moving power one slider step to forty five point five leaves all factors at one and gives eleven hundred thirteen point one throughput with point nine two efficiency. That abrupt change mostly leaves the fixture branch, rather than demonstrating a physical breakthrough. Doubling installed units to two thousand forty eight while holding power fixed doubles assigned power demand to ninety point six eight five four four megawatts. The power factor halves to roughly point four nine six two two. The modeled general throughput becomes about five hundred fifty two point three. This shows the code consequence of an unmet resource ratio, not an empirical prediction. Audit export is local scenario data. Its external D three visualization dependency is unavailable in our offline checks, so those checks do not establish live control or topology behavior.