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Solyx AI Grid Benchmarks

PublishedarXiv:2608.06188 · 2026

Routing LLM Inference to the Cleanest Grid in Real Time

A. Bernhard, A. B. Yardimci — Solyx AI

Where a request runs decides how much carbon it burns, because the grid is far dirtier in some places and at some hours than others. We showed live, on real GPUs across several regions, that inference can be steered toward the cleaner grid without a single failed request. Replaying a full year of grid data, that steering cuts the emissions of the same workload by roughly half.
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PublishedarXiv:2606.15050 · 2026

Solyx AI Grid: Hardware-Telemetry-Aware Routing Across Geographically Distributed GPU Clusters

A. Bernhard, N. Katla — Solyx AI

Ten live signals — from the GPUs, the software and the network between sites — decide where each request runs. Measured against the standard approach on real hardware in three locations: about 1.6 to 1.75 times the useful work from the same fleet, a quarter off the slowest responses, and recovery from a failed site in about a second instead of four.
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1.56–1.75×
More usable capacity from the same GPU fleet
At tier-2 SLO, across all eight workload classes tested
99.57%
Long-prompt success rate vs 67.89% for round-robin
0.43% traffic reached misconfigured endpoint · round-robin sends 32.11%
3.2×
Faster failover than round-robin
1,247ms vs 4,226ms P99 reroute · broken site isolated automatically
0.2ms
Routing overhead per request
Minimal overhead. Maximum intelligence.

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