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technologyAugust 1, 2026
Optimizing Low-Latency AI Inference Pipelines at Scale
How Racoona AI architects high-throughput vLLM clusters with 18ms time-to-first-token latency.
By Racoona AI Engineering
Executive Summary
As enterprise AI adoption matures, the primary operational bottleneck transitions from model training to production inference orchestration. High time-to-first-token (TTFT) and GPU memory starvation degrade user experience in real-time applications.
Architecture Overview
At Racoona AI, we utilize containerized vLLM clusters paired with custom continuous batching routing engines.
Key performance metrics achieved in production:
- TTFT (Time-to-First-Token): 18ms
- Throughput: 142 tokens / second per H100 node
- GPU Memory Utilization: 92% continuous memory density
Forward Deployed Engineering (FDE) Strategy
“We rebuild legacy infrastructure without spending time.” Our embedded FDE teams deploy turnkey inference gateways using OpenAPI specs and gRPC connections to interface seamlessly with existing legacy ERP and CRM databases.
#AI#vLLM#Inference#Cloud Architecture