Inference optimization infrastructure
The Intelligence Layer for AI Inference
AI models are converging in quality; cost and latency are the real competitive edge. Pareton continuously discovers, validates, and deploys the optimal serving configuration for your workload.
Build in progress · Coming soon
How Pareton works
An optimization loop: your profile in, better engine out.
Customer profile
Step 01 / 06
You bring the setup. You define better.
Tell Pareton what you run in production — model, serving stack, workload profile, hardware — and the SLA gates you won't break. One success metric locks the goal, usually GPU-hours saved at SLA. That customer-approved profile is the yardstick for every candidate that follows.
Step 02 / 06
Contributors propose, you don't rewrite
Contributors (miners) submit small, reviewable patches against the current baseline — prefix caching, batch sizing, KV-cache allocation, kernels. Every candidate targets the same frozen profile, so proposals stay comparable.
Current baseline
vLLM + accepted patches
Contributor A
+enable prefix caching
−default cache policy
…
Contributor B
+dynamic batch sizing
−fixed batch size
…
Contributor C
+optimize KV allocation
−existing allocation path
…
Step 03 / 06
Automated validation gates everything
Before any benchmark runs, the candidate has to build and run, preserve output quality and API compatibility, satisfy the customer's constraints, and work across the required GPU environments. Invalid patches are rejected.
- Builds and runs
- Output quality preserved
- API compatibility preserved
- Customer constraints satisfied
- Works across required GPU environments
Invalid patches are rejected before benchmarking
Step 04 / 06
Baseline vs. patched, head to head
The patched engine and the current baseline run the exact same workload trace, on identical hardware, under the same SLA gates. The comparison is apples-to-apples by construction.
Current baseline
vLLM + accepted patches
Patched engine
baseline + candidate diff
| Metric | Baseline | Patched | Delta |
|---|---|---|---|
| GPU-hours | 100 | 93 | −7% |
| Throughput | 1.00× | 1.08× | +8% |
| p99 TTFT | 850 ms | 820 ms | −30 ms |
| Quality / SLA | Pass | Pass | — |
Same workload trace · same hardware · same SLA gates
Step 05 / 06
Promote on evidence, or move on
One binary call from measurements: did it improve the priority metric without breaking SLA? Yes — merge it and promote the new best baseline. No — discard it and test the next candidate. The baseline only moves forward.
Priority metric · GPU-hours at SLA
Did it improve the metric without breaking SLA?
Promote
Merge into the inference-engine repo. Becomes the new best baseline.
Reject
Didn't beat the metric, or broke SLA. Discard and test the next candidate.
Step 06 / 06
The loop compounds
Rounds repeat until the agreed success threshold is reached. Each accepted patch becomes the floor for the next, so gains compound instead of expiring.
The new baseline becomes the floor — the next round of patches targets it, until the agreed success threshold is reached.
Success threshold reached
- Verified optimized engine
- Before / after savings report
- Ready for deployment
01
Continuous search
The inference search space — kernels, batching, KV cache, quantization, scheduling — evolves faster than any isolated R&D team can track. Pareton benchmarks it continuously.
02
Deterministic validation
Every candidate configuration is stress-tested across GPU types, context lengths, and request patterns. Only improvements that hold universally reach the baseline.
03
Compounding baseline
Each validated improvement becomes the new floor for the next. Optimization stops being a per-company cost and becomes shared, accumulating infrastructure.
Inference demand is compounding faster than efficiency improves. The gap shows up in margin, serving latency, and duplicated optimization work. Pareton exists to close it, one validated configuration at a time.