Your GPUs are fast. Your infrastructure isn’t.
The performance you’re leaving on the table isn’t in your model. It’s in your stack.
Platform
More from every GPU you already run.
Your models and serving pipelines stay as they are. Same weights, APIs, schedulers, and containers. Nothing upstream has to move.
Deep Variance is the runtime layer underneath: it recovers wasted memory, cuts repeat work on serving, and runs leaner on the chip. The diagram shows where it attaches.
Optimized in production
Install once.
Run more for less.
+65%
More memory
Fit bigger models, or serve more users at the same time.
6x
Inference speed
Real-time tuning unlocks more tokens and more requests per GPU.
-50%
Energy efficiency
Cut power draw per token. Lower cost, smaller fleet, same SLA.
Headline figures are upper-bound.Tested on supercomputing infra with 600× NVIDIA H100s.
Works with what you already run.
No new framework. No new scheduler. No code changes.
Frameworks
Silicon, shipping today
