Free public benchmark
Which silicon runs your AI workload cheapest?
Paste a workload spec. graphx returns a ranked table of silicon × cloud provider × cost × latency × watts in under a millisecond.
- +9 – 51% bit-exact wins on 5 production MoE families
- Held-out Spearman ρ = +0.43 on MI300X
- p95 = 0.79 ms · 10,000+ req/s
Ranked silicon + cloud combinations
| Rank | Silicon | Cloud provider | Best tile | Predicted µs | Hourly $ | $/M inferences |
|---|
Want to validate this on YOUR pod with bit-exact correctness?
Public benchmark uses graphx's lean-predictor-v1 (held-out ρ = +0.43). A paid pilot runs the sidecar on your actual GPUs and measures real µs + bit-exact correctness + bootstrap CI on your live traffic for 90 days at $15-30K with a measured-savings success criterion.
Schedule a 30-min pilot review →How graphx saves money — 5 levers, one routing oracle
- Same silicon, better kernel — pick the optimal CK/Triton variant for your shape on the current GPU. 30-50% savings.
- Better silicon, same cloud — route to cheaper silicon within your existing AWS/Azure/GCP/OCI account. 20-40% savings.
- Right billing mode — match traffic pattern to dedicated vs. serverless. 50-80% on bursty traffic.
- Cross-cloud spot arbitrage — batch traffic routes to whichever cloud has cheapest spot right now. 30-60% on batch.
- Prompt caching — cache repeated prefixes for RAG and chatbot prompts. 90% on cached portion.
Customer drops the sidecar, graphx auto-deploys measured winners every 15 minutes, inference bill drops 30 – 60% over the first quarter.