GPU optimization for AI teams
Stop burning your AI compute budget.
Inefficient deployments quietly
inflate your AI spend.
We find it. We fix it.
Affiliations
Backed by
Early Traction
Explore
The problem
You run mostly blind. The bill keeps climbing.
Teams run models mostly blind. Dashboards show utilization, not why you're wasting capacity or what to fix.
So you add GPUs and pay for capacity that is only partly doing useful work.
How it works
Reclaim unused capacity on the GPUs you already pay for.
No code changes. No model changes.
Feniria sits on the fleet you already run and keeps turning paid but idle capacity into more output, at lower cost.
Production traffic stays up. A better setup is proven before it takes traffic. The loop keeps running as load changes. It is not a one-time tune.
- 01 Read the fleet
- 02 Find unused capacity
- 03 Prove a better setup
- 04 Cut over
- 05 Keep retuning
Results
Half the rent. More throughput.
Feniria found the fleet was over-provisioned. Same workload, 50% lower 24/7 instance rent, on cheaper hardware in the same family.
+9%
Inference throughput
More tokens per second on the cheaper instance.
−37%
End-to-end latency
Requests finish sooner, with no model or code changes.
−48%
p99 time to first token
Faster first response on production LLM inference.
A fit when
- 01 GPU bills keep climbing but dashboards look fine
- 02 LLM inference is already a real production load
- 03 LLM API bills are high enough that you're ready to run your own inference
What teams usually want to know
Frequently asked questions
What do you mean utilization looks fine?
Your dashboard probably shows busy GPUs. That is not the same as maxed out. Standard metrics track whether hardware is running, not whether that work is useful. Much of the fleet can look full while capacity sits idle.
How does Feniria do this without changing code or models?
Feniria sits on the deployment you already run. Combined observability is why it can reclaim unused capacity that a dashboard still calls busy. A better setup is proven before it takes traffic. Production stays up. No code changes. No model changes. The loop keeps running as load changes.
Does this only work in the cloud?
No. You keep the GPUs you already have. Feniria runs there, on cloud or on-prem. Same loop either way.
What happened on a real production deployment?
Feniria found the deployment over-provisioned. Same workload, cheaper box in the same family, 50% lower 24/7 rent. One AWS g6e.48xlarge went from $21,996 a month to $10,998. The serving numbers came off that cheaper box: p99 first token down 48%, throughput up 9%, end-to-end latency down 37%. The table is on the impact page.
What does a pilot take?
No upfront cost. A success fee only if we prove the improvement, taken from the savings. The pilot runs alongside live traffic. Findings first, then changes. A few weeks to prove the before-and-after.
When is Feniria a fit?
When GPU bills keep climbing but dashboards look fine. When LLM inference is already a real production load. Or when LLM API bills are high enough that you are ready to run your own inference.
Start fixing
We find the waste. We fix it.
Join our early adopters, with limited slots for teams ready to ship more with the GPUs they already have. Piloting now with design partners, on cloud and on-prem.