Inference Optimizer

Your GPU clusters lose 30–40% capacity to overhead.

We recover it. The Inference Optimizer sits between your orchestrator and your inference runtime. Compiles dynamic glue code into fused execution plans. No model retraining required.

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The Problem

Orchestration overhead eats capacity

Dynamic glue code between your orchestrator and runtime — scheduling, batching, routing — silently consumes 30-40% of your GPU capacity. Your dashboards show the GPUs are busy. They're busy doing the wrong work.

Tail latency spikes under load

p99 latency becomes unpredictable as serving load increases. SLOs break not because your models are slow, but because the orchestration layer can't keep up.

You're paying for compute you can't use

Every percentage of capacity lost to overhead is a direct cost: more GPUs, more instances, more spend. The problem compounds at scale.

What the Inference Optimizer Does

The Inference Optimizer sits between your serving orchestrator and your GPU inference runtime. It compiles dynamic orchestration logic — scheduling, batching, routing decisions — into fused, deterministic execution plans. The result: your GPUs spend more time on inference and less time on overhead.

Recovers lost capacity

Without retraining models or replacing your serving framework

Stabilizes tail latency

Deterministic execution reduces p99 variance under variable load

Drops into existing stacks

Works with vLLM, Triton, and standard serving infrastructure

Where It Sits In Your Stack

The Inference Optimizer intercepts between orchestration and runtime. It doesn't replace either — it optimizes the handoff.

Orchestrator

Scheduling, routing, batching

Inference Optimizer

Fused execution plans

GPU Runtime

vLLM, Triton, etc.

Deterministic audit trail for every execution decision

Benchmark Results

+56%

Capacity uplift

vs. baseline in MLPerf-class server workloads

+43%

Request throughput increase

Measured in Triton Inference Server benchmarks

0

Recaptures & fallbacks

In dynamic serving tests under variable load

Inference Optimizer R

For regulated and deterministic deployment environments where every inference decision must be auditable. Same capacity recovery, with additional constraints for compliance-sensitive workloads.

  • •Deterministic execution plans with full replay capability
  • •Audit-grade evidence artifacts for incident review
  • •Designed for environments with strict compliance and SLO requirements

Integration & Deployment

What it requires

  • Standard serving infrastructure (vLLM, Triton, or equivalent)
  • Access to orchestration layer integration points
  • Deployment configuration for your workload profile

What it does NOT require

  • No model retraining
  • No serving framework replacement
  • No changes to your model artifacts or weights
Benchmark briefs available
Patented frameworks
Built for regulated environments
Architecture docs under NDA

Book a Technical Briefing

30 minutes. We'll show you where the Inference Optimizer fits in your stack and walk through benchmark data relevant to your workload.

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