Cogniware Introduces Next-Generation Aurora Architecture for Its Inference Platform, Enabling AIOps for Every Organization

Internal benchmarks show 97 percent higher throughput and 68 percent lower GPU energy per output token

HERNDON, VA – October 20, 2026 — Cogniware today introduced Aurora, the next-generation architecture for its Cogniware Inference Platform (CIP). Aurora marks a substantial advance in the company’s mission to make AI more powerful, accessible, and cost-efficient. CIP delivers higher performance across a wide range of AI workloads, models, and hardware, simplifying how AI teams own, operate, and optimize their inference. The overall impact is lower AI operating costs and higher return on GPU investments.

Nearly twice the throughput at high concurrency

In our internal Cogniware benchmarks, we compared the Aurora-based CIP using vLLM v0.27.1 with a highly tuned standalone instance of the same vLLM version. Both configurations served Meta Llama 3.3 70B with FP8 on a system with eight NVIDIA H200 GPUs.

The benchmark tested a challenging mix of competing workloads: chat, code, long document, mid-size retrieval-augmented generation (RAG), churn, and long context. At 1,800 concurrent requests, Aurora-based CIP delivered the significant improvements significant improvements over standalone vLLM:

• 97% higher throughput (tokens per second)

• 56% lower median end-to-end latency (milliseconds)

• 29x faster time to first token (milliseconds)

• 68% lower GPU energy consumption per output token (joules)

CIP delivered strong results in internal benchmarks across GPU platforms, including NVIDIA and AMD. Those results validate the platform’s compute-independent architecture, giving AI teams more freedom to choose the infrastructure that fits their needs.

Aurora’s performance gains come from new AI workload orchestration and improved inference-aware routing, guided by AI models and deep awareness of cached data. The architecture also includes enhancements to CIP’s core caching and memory management algorithms.

New controls for AIOps

Aurora also expands CIP’s support for AIOps: the discipline of delivering high-quality inference on demand, at the lowest cost that meets each workload’s needs. Two new capabilities give AI teams greater control over production inference:

• A new AIOps interface gives operators visibility and control from inbound AI workloads through memory utilization. It provides a single control plane to govern, route, secure, measure, and optimize inference across models, inference engines, and GPU fleets.

• Multi-stage lifecycle management lets operators test configurations and validate performance before committing changes to production. Teams can fall back to a previous configuration when needed.

“Owning inference is a strategic decision for any company making AI central to its business strategy,” said Ambarish Desai, founder and CEO of Cogniware. “Innovators that build AIOps capabilities in-house can cut costs significantly while meeting the demands of their applications. Aurora gives those teams the performance and operational control to make that strategy work.”

For more information about the Cogniware Inference Platform, visit www.cogniware.ai.

About Cogniware

Cogniware helps enterprises and AI infrastructure providers own and optimize their inference. Its Cogniware Inference Platform combines performance optimization with a single control plane across AI models, inference engines, and compute infrastructure. Cogniware enables organizations to run inference in the cloud or on premises, with greater control over performance and cost.