GPU Optimization You Can Trust in Prod

Sedai doesn't just flag idle GPUs or show you dashboards. It continuously identifies waste, right-sizes workloads, and executes GPU optimizations safely, without disrupting your AI infrastructure.

Optimize GPU Infrastructure with Superintelligence

AI workloads are expensive to run and hard to tune. Sedai models true GPU utilization across your Kubernetes clusters, finds waste that standard metrics miss, and acts on it — automatically and safely.

GPU Workload, Node & Cluster Optimization

Static GPU allocations lead to massive waste. Sedai's proprietary utilization model continuously adapts to real workload behavior, keeping GPU usage optimized even as your AI infrastructure evolves.

Idle GPU Deallocation

Detect workloads with GPU resources allocated but not actively used. Sedai identifies unused allocations and automatically removes them, with clear cost impact shown before and after every change.

MIG Enablement and Packing

Identify NVIDIA GPU instances where Multi-Instance GPU (MIG) partitioning isn't enabled. Sedai recommends the right slice configurations and packs more workloads onto each physical GPU.

GPU Node Pool Optimization

Analyze how workloads are spread across GPU devices and consolidate them onto the minimum number of nodes. Free entire GPU devices, reduce node spend, and reclaim capacity you already own.

A Smarter Signal for True GPU Utilization

Most tools rely on standard utilization metrics, such as those reported by NVIDIA System Management Interface (nvidia-smi). However, those metrics only tell you whether a GPU is doing something, not whether it's doing something useful. A GPU can show 100% utilization while performing zero productive computation.

Sedai approaches this differently:

- Proprietary utilization model infers true GPU usage from multiple telemetry signals

- Models real workload behavior across compute, memory, and throughput dimensions

- Provides a first-class utilization score that drives every optimization decision

- Identifies waste that surface-level metrics consistently miss

GPU Cost & Capacity Intelligence

Most tools only show you where GPU spend goes. Sedai knows why it's happening and reduces it for you.

Actionable GPU Cost Visibility

See exactly where GPU spend lives across workloads, node pools, and clusters. Sedai turns cost drivers into actions for measurable, ongoing savings.

Free Capacity You Already Own

Before procuring new GPUs, reclaim the ones you have. Sedai continuously identifies underutilized devices and frees them for use, reducing procurement delays and queue times for AI teams.

Waste Detection at Every Layer

Find inefficiencies across workloads, nodes, and clusters. Sedai surfaces idle and over-allocated GPU capacity and removes it, safely and autonomously.

“By having Sedai in place, we’re not just saving money. We’re preventing would-be customer problems, before they become an issue.”

Matt Duren

VP of Engineering // KnowBe4

How Sedai Optimizes GPU Infrastructure Safely

Get safe, outcome-driven GPU optimization at scale, designed to act on real workload behavior, with safeguards built into every decision.

Sedai models how each workload uses GPU resources over time, understanding utilization patterns, peak demand windows, and the difference between idle and active allocation.

Every GPU optimization aligns with workload requirements, performance goals, and cost targets. Sedai never optimizes in isolation — it understands the full picture before acting.

All changes execute with validation and guardrails. Start with Datapilot recommendations, move to one-click Copilot execution, and progress to fully autonomous Autopilot — at your own pace.