AI & data centers

Save energy. Keep determinism. Ship thr you can defend.

The story for operators is not “we won a FLOPS contest.” It is measured electricity cost per unit of AI work, plus deterministic, auditable compute for multi-tenant and regulated paths — with honest throughput, not thr-only marketing.

The headline for your power budget

Facilities pay for watts and cooling. If your stack cannot show joules per token under sustained load, you are flying blind on the bill that actually constrains capacity.

0.63
Joules per token
7B-class · multi-run · public pack
~403
Tokens per second
Same protocol · seq=128
~254 W
Power while work runs
Full 28-layer stack
Public
Methods + traces
Energy is the product story for data centers. Determinism is how you trust the result across tenants, re-runs, and audits. Throughput matters — but thr without a joule number is only half a dashboard.

H100 NVL · full stack under sustained load · energy from measured power × time / tokens. Pack →

Two pillars (not a thr-only pitch)

1. Energy under load

  • Joules per token you can open in a public pack
  • Sustained work — not launch-tax microbenches as the hero
  • Power while the stack is actually running
  • Same method language the public uses to pressure providers

2. Determinism & audit

  • Receipt-oriented paths where trust matters
  • Free-ride / residual checks under load
  • Reproducible story for multi-tenant and regulated AI
  • Methods you can re-run — not “it looked about the same”

Where speed fits (honestly)

LuxiEdge is not “ignore thr.” Product measurements show solid prefill thr at realistic sequence lengths (~400 tok/s class on the public 7B-class pack). The positioning is:

What we optimize for

Energy cost of work + deterministic trust, with thr that holds up under the protocols we publish. Long-context memory scaling (O(N) vs O(N²)) when context is the silent capacity killer.

What we do not pretend

We do not claim we always beat Flash-class kernels on every short-seq thr race. On a published 12-layer head-to-head, a PyTorch + Flash baseline can win thr and J/token on that shape — and we show that loss. Differentiated axes: audit paths, memory scaling, and full-stack energy you can verify.

Operator takeaway: choose LuxiEdge when the bill and the audit trail matter as much as the leaderboard. If pure short-seq thr is the only KPI, say so in diligence — we will not invent a thr win we did not measure.

Why operators evaluate us

Power caps are real

Rack and site power — not TFLOPS slides — decide how much AI you can sell. J/token is how you plan.

Trust is multi-tenant

Deterministic paths and receipts reduce “it drifted” incidents across customers and re-runs.

Public pressure is coming

Communities and customers are asking about AI electricity. See We need your help — the same numbers work for operators and the public.

Proof before NDA

Headline metrics link to public packs. Commercial depth and source under NDA when you are ready.

Next step

  1. Read Proof — energy tables and packs first.
  2. Map your power cap and thr SLO to the public ladder (seq≥128 as product thr/energy).
  3. Technical discussion for on-prem / cloud evaluation and joint metering.