Inference optimization
Models are converging in quality. Cost and latency are the edge. Pareton searches the serving config space until a better engine holds on the workload you already run.
Coming Soon · Bittensor Subnet 10
Priority
GPU-hours at SLA
Hard gate
p99 TTFT / ITL
Search space
Kernels, KV, batch, quant, and more
Baseline rule
Only moves forward
01 · Brief
Inference demand is compounding faster than efficiency improves. Pareton exists to close the gap, one validated configuration at a time.
02 · Method
We only keep a change if your latency cap still holds.
02 · Method
We only keep a change if your latency cap still holds.
Then we try again
Scroll to step through
That snapshot is the ruler. Every later change is judged against it, not against a public leaderboard.
The model you already serve.
The hardware you already own.
The limit you will not break.
We try to cut
GPU hours
We do not move
Your latency cap
01 · Setup
That snapshot is the ruler. Every later change is judged against it, not against a public leaderboard.
The model you already serve.
The hardware you already own.
The limit you will not break.
02 · Test
Same GPUs. Same traffic. Same latency cap. If it is not cheaper, it is discarded.
Today
Candidate
03 · Keep
Keep it, or throw it out. The starting point only gets better.
This is now what you run.
Try the next change.
03 · Why it holds
01
Every candidate is scored on the profile you approved: real traffic shape, production flags, and the hardware you actually serve on.
02
Improvements arrive as reviewable code and configuration diffs. You can see what changed, why it won, and what it did not touch.
03
Build, quality, compatibility, and cross-GPU gates run before the bench. A trick that only works on one trace does not ship.
04
Search continues after the first win. Each promotion raises the floor, so GPU-hours, throughput, and latency keep moving.
04 · Buyers
ML platform
You run vLLM, TensorRT-LLM, or SGLang in production. You need the kernel and KV-cache work without freezing a quarter of the team.
Infrastructure
Utilization and cost per token are the line. Pareton reports in GPU-hours at SLA, on the SKUs you already bought.
Technical eval
You will read the campaign, the patch, and the bench. The product is a numeric record of what was tried, what failed, what shipped.
If you already serve models under a real SLA, send the profile. We will tell you whether the search is worth running.
