Compute & Model Specs
10^25 FLOPs
70 B
2.0 T
32 steps
8 branches
Compute Allocation Pareto Frontier
Pareto Optimal
Current Operating Point
Predicted Task Accuracy
84.2%
Optimal balance achieved
Inference Latency
1420 ms
MCTS & Token generation
Cost / 1M Queries
$18.40
FLOPs converted to USD
Detailed Compute Distribution & Breakdown
| Compute Domain | Parameter / Metric | Allocated FLOPs | Share of Total Compute | Operational Impact |
|---|---|---|---|---|
| Pre-Training Scaling | 70B Params / 2.0T Tokens | 8.40e+23 | 65.0% | Establishes baseline knowledge & parametric memory |
| Test-Time Search | 32 Search Depth × 8 Branches | 4.52e+23 | 35.0% | Drives MCTS reasoning expansion & error correction |
| Total Pipeline Budget | System Operating Point | 1.00e+25 | 100.0% | Combined amortized FLOP utilization |
Scenario Summary & Optimal Allocation Proof Target
Target Accuracy
0.8420
Optimal Pre-train FLOPs %
65%
Optimal Inference FLOPs %
35%
Inference Latency (ms)
1420
Cost / 1M Queries (USD)
18.40