The Inferex Platform

One platform to optimize, deploy, and monitor AI inference at any scale.

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Platform Overview

Three integrated layers. One unified control plane.

Kernel-Level Inference Optimization

The Inferex Optimizer applies hardware-specific kernel optimizations at the model operator level — not just at the framework level. We rewrite attention kernels, fuse operations, and exploit hardware-specific instruction sets.

The result: P99 latency drops from 58ms to under 8ms without changing a single line of your model code. Works with any PyTorch, TensorFlow, or ONNX model.

  • Automatic kernel fusion for attention and FFN layers
  • INT8/FP8 quantization with accuracy preservation
  • Flash Attention 3 integration for LLM workloads
  • Continuous batching for variable-length requests
P99 Latency 7.8ms ↓73%
GPU Utilization 91% ↑167%
Throughput 1.2M req/s ↑567%
Cost per 1M inferences $0.94 ↓78%

Horizontal Auto-Scaling Infrastructure

The Inferex Scaler manages a fleet of inference workers across any cloud provider or on-premise hardware. It uses load-aware routing, predictive scaling, and zero-downtime rolling deployments.

Predictive Scale-Out

Scales capacity before traffic spikes hit — not after. Uses time-series forecasting on your request patterns.

Multi-Cloud Fleet

Manage workers across AWS, GCP, Azure, and on-premise from a single control plane.

Load-Aware Routing

Routes requests to the least-loaded worker with sub-millisecond routing overhead.

Zero-Downtime Deploys

Rolling deployments with automatic traffic shifting and instant rollback on error spikes.

Real-Time Observability Dashboard

Every inference is tracked. P50/P95/P99 latency, throughput, error rates, and hardware utilization — all in real time with sub-second refresh. Alerting via Slack, PagerDuty, or webhook.

Latency Percentiles

P50/P95/P99 latency tracking per model, per endpoint, and per request type.

Smart Alerting

Anomaly detection triggers alerts before users notice degradation. Integrates with PagerDuty and Slack.

30-Day Audit Log

Every inference request logged with full metadata. Query with SQL. Export for compliance.

Hardware Telemetry

GPU/CPU utilization, memory pressure, thermal state — all surfaced in one view.

Works With Your Stack

Inferex integrates in minutes with any ML framework or serving infrastructure.

PyTorch TensorFlow ONNX TensorRT vLLM Triton

Performance Comparison

Measured on NVIDIA A100 80GB, Llama 3 70B, 512-token sequences, 1000 concurrent users.

Metric Without Inferex With Inferex
P99 Latency 58ms 8ms
Throughput 180k req/s 1.2M req/s
GPU Utilization 34% 91%
Cost per 1M Inferences $4.20 $0.94

Technical Architecture

A layered, hardware-agnostic design that fits into any production stack.

Inferex Technical Architecture

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