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//GPU inference optimization

Find the cheapest safe config for your LLM inference

Self-hosted LLM serving has a huge, interacting config space — and the wrong choice quietly burns GPU budget or breaks latency under load. Langotime reads your traces, serving config and SLOs and returns the cheapest safe configuration, with the failure points predicted before anything touches production. Read-only.

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The problem

The serving config is a minefield you only see after it blows up

Batch sizes, parallelism, KV-cache, quantization, replica counts, GPU type — every serving knob interacts, and the cost-versus-latency frontier moves with your traffic and every model or runtime update. Dashboards show what already happened, not the consequence of the next change. Over-provision and you burn the most expensive capacity you can buy; trim too far and you breach latency or hit OOM under load.

//How Langotime does it

From request traces to a serving config you can trust

01

Connect

Pull request traces, serving config, model/runtime setup, GPU type and SLOs into one operational picture. Read-only.

02

Explain

Learn how your traffic and this config behave — where latency, queueing and OOM risk actually come from.

03

Simulate

Project config changes — batching, parallelism, replicas, GPU type — against cost and your latency/throughput SLOs, before production.

04

Recommend

Return the cheapest safe config and a diff, with the predicted failure points under load — a human approves before anything ships.

//Honest comparison

“Why not just…”

“TUNE IT BY HAND”The config space is huge and interacts; hand-tuning lags every traffic and model change.
“OVER-PROVISION TO BE SAFE”GPU is the most expensive thing to leave idle; the safety margin becomes wasted budget.
“WATCH THE DASHBOARD”It's post-hoc — it can't tell you what the next config change will do under load.
Why it's different

It predicts the consequence of a config change before you ship it

Langotime runs on a Time Series World Model — AI for metrics, not text. Instead of reacting to a dashboard, it predicts the cost and latency consequences of a candidate serving config, so the cheapest safe option is a decision, not a guess.

Read-only — no production write access to start
Predicted-vs-measured, not guesswork
Cost and latency/SLO tradeoffs made explicit
Failure points surfaced before they hit production
Works with your serving stack and the agents you already use
A human stays in the approval loop
//Who it's for

If your GPU bill and your latency SLOs are both real…

Teams running self-hosted LLM inference with meaningful GPU spend and latency SLOs — where a wrong serving config is either wasted money or a production incident, and the config space is too large to tune by hand.

Langotime is for you.

See the cheapest safe config on your own traffic.