NETUID 28 · BITTENSOR
SN28 SayGM
SayGM is an AI inference gateway providing API access to models from multiple providers.
Project description: SayGM's official website and documentation. This is not an independent performance verification.
01
BITTENSOR NETWORKNetwork Economics
Coverage2.83%Rolling 30D observation
Demand181.87 TAORolling 30D observation
Emissions6,429.7 TAORolling 30D observation
Burns386.37 TAORolling 30D observation
Coverage, Demand, Emissions and Burns belong to Bittensor network observations, not SayGM inference operations. Emissions and burns are not revenue.
Demand = inflow + burns; Coverage = Demand / Emissions × 100. Overlapping rolling windows are not added together. Network Demand is not SayGM customer revenue.
Historical network observations
NetUID history retains each observation's name. Earlier subnet identities and windows crossing a rename must not be attributed exclusively to SayGM.
History frozen report cutoff:
| Observation date | Name at observation | Coverage | Demand | Emissions | Burns | Canonical run |
|---|---|---|---|---|---|---|
| SayGM | 11.22% | 487.34 TAO | 4,342.1 TAO | 487.34 TAO | Success; complete canonical dataset | |
| SayGM | 11.23% | 489.09 TAO | 4,357.08 TAO | 489.09 TAO | Success; complete canonical dataset | |
| SayGM | 11.23% | 491.85 TAO | 4,381.12 TAO | 491.85 TAO | Success; complete canonical dataset | |
| SayGM | 11.3% | 499.35 TAO | 4,417.65 TAO | 499.35 TAO | Success; complete canonical dataset | |
| SayGM | 11.59% | 515.86 TAO | 4,449.12 TAO | 515.86 TAO | Success; complete canonical dataset | |
| SayGM | 11.69% | 521.82 TAO | 4,463.38 TAO | 521.82 TAO | Success; complete canonical dataset | |
| SayGM | 11.52% | 518.21 TAO | 4,496.63 TAO | 518.21 TAO | Success; complete canonical dataset | |
| SayGM | 11.35% | 511.58 TAO | 4,508.55 TAO | 511.58 TAO | Success; complete canonical dataset | |
| SayGM | 11.29% | 510.21 TAO | 4,517.21 TAO | 510.21 TAO | Success; complete canonical dataset | |
| SayGM | 10.89% | 492.68 TAO | 4,522.67 TAO | 492.68 TAO | Success; complete canonical dataset | |
| SayGM | 10.91% | 494.88 TAO | 4,537.56 TAO | 494.88 TAO | Success; complete canonical dataset | |
| SayGM | 11.1% | 505.15 TAO | 4,549.54 TAO | 505.15 TAO | Success; complete canonical dataset | |
| SayGM | 11.26% | 514.46 TAO | 4,569.02 TAO | 514.46 TAO | Success; complete canonical dataset | |
| SayGM | 11.37% | 521.14 TAO | 4,581.72 TAO | 521.14 TAO | Success; complete canonical dataset | |
| SayGM | 11.41% | 523.58 TAO | 4,589.51 TAO | 523.58 TAO | Success; complete canonical dataset | |
| SayGM | 11.59% | 531.71 TAO | 4,588.11 TAO | 531.71 TAO | Success; complete canonical dataset | |
| SayGM | 11.68% | 534.22 TAO | 4,574.77 TAO | 534.22 TAO | Success; complete canonical dataset | |
| SayGM | 11.51% | 527.82 TAO | 4,584.58 TAO | 527.82 TAO | Success; complete canonical dataset | |
| SayGM | 11.32% | 520.5 TAO | 4,599.41 TAO | 520.5 TAO | Success; complete canonical dataset | |
| SayGM | 11.2% | 516.92 TAO | 4,615.92 TAO | 516.92 TAO | Success; complete canonical dataset | |
| SayGM | 10.24% | 474.56 TAO | 4,636.6 TAO | 514.48 TAO | Success; complete canonical dataset | |
| SayGM | 10.01% | 466.41 TAO | 4,660.01 TAO | 506.33 TAO | Success; complete canonical dataset | |
| SayGM | 9.67% | 453.73 TAO | 4,691.17 TAO | 493.64 TAO | Success; complete canonical dataset | |
| SayGM | 9.41% | 445.47 TAO | 4,732.52 TAO | 485.39 TAO | Success; complete canonical dataset | |
| SayGM | 9.2% | 440.94 TAO | 4,794.46 TAO | 480.85 TAO | Success; complete canonical dataset | |
| SayGM | 8.99% | 437.13 TAO | 4,861.33 TAO | 477.04 TAO | Success; complete canonical dataset | |
| SayGM | 8.7% | 429.69 TAO | 4,939.61 TAO | 469.6 TAO | Success; complete canonical dataset | |
| SayGM | 10.86% | 544.85 TAO | 5,017.46 TAO | 624.69 TAO | Success; complete canonical dataset | |
| SayGM | 10.53% | 537.74 TAO | 5,105.84 TAO | 617.58 TAO | Success; complete canonical dataset | |
| SayGM | 10.17% | 529.94 TAO | 5,208.43 TAO | 609.78 TAO | Success; complete canonical dataset | |
| SayGM | 9.88% | 522.63 TAO | 5,291.77 TAO | 602.47 TAO | Success; complete canonical dataset | |
| SayGM | 9.47% | 511.85 TAO | 5,405.5 TAO | 591.69 TAO | Success; complete canonical dataset | |
| SayGM | 9.17% | 503.99 TAO | 5,496.98 TAO | 583.82 TAO | Success; complete canonical dataset | |
| SayGM | 8.77% | 488.11 TAO | 5,568.41 TAO | 567.95 TAO | Success; complete canonical dataset | |
| SayGM | 7.18% | 404.91 TAO | 5,638.2 TAO | 544.59 TAO | Success; complete canonical dataset | |
| SayGM | 6.86% | 391.23 TAO | 5,699.33 TAO | 530.91 TAO | Success; complete canonical dataset | |
| SayGM | 6.65% | 383.01 TAO | 5,760.36 TAO | 522.69 TAO | Success; complete canonical dataset | |
| SayGM | 6.32% | 368.34 TAO | 5,831.51 TAO | 508.02 TAO | Success; complete canonical dataset | |
| SayGM | 5.96% | 352.77 TAO | 5,917.67 TAO | 492.45 TAO | Success; complete canonical dataset | |
| SayGM | 5.64% | 338.53 TAO | 6,006.46 TAO | 478.21 TAO | Success; complete canonical dataset | |
| SayGM | 5.3% | 322.89 TAO | 6,089.02 TAO | 462.57 TAO | Success; complete canonical dataset | |
| SayGM | 3.91% | 241.51 TAO | 6,182.77 TAO | 446.01 TAO | Success; complete canonical dataset | |
| SayGM | 3.76% | 233.9 TAO | 6,223.29 TAO | 438.4 TAO | Success; complete canonical dataset | |
| SayGM | 3.43% | 216.19 TAO | 6,297.21 TAO | 420.7 TAO | Success; complete canonical dataset | |
| SayGM | 3.13% | 199.65 TAO | 6,374.1 TAO | 404.15 TAO | Success; complete canonical dataset | |
| SayGM | 2.83% | 181.87 TAO | 6,429.7 TAO | 386.37 TAO | Success; complete canonical dataset |