NeuralSpeed: Functional Telemetry and Protocol-Level Token Reduction Benchmark on High-Concurrency LLM Inference Pipelines"

# NeuralSpeed: A Protocol-Level Second-Request Acceleration Gateway for High-Concurrency LLM Inference## Empirical Evaluation of Token Reduction, Latency Dynamics, and Protocol Compatibility in Multi-Turn Agentic and Enterprise Workflows **Author / Creator:** Vasil Manchev **Publication Type:** Preprint **Repository & Prior Art Archive:** Zenodo (CERN InvenioRDM) **License:** Creative Commons Attribution 4.0 International (CC-BY-4.0) ### AbstractAs Large Language Model (LLM) deployments transition from simple single-turn conversational chatbots to autonomous multi-agent loops, enterprise automation workflows, and high-concurrency microservices, inference costs and generation latencies have become the primary bottleneck of modern computational infrastructure. Current production pipelines repeatedly transmit and regenerate extensive computational context across consecutive interactions, incurring redundant provider billing and quadratic attention overhead on every subsequent turn. This paper introduces **NeuralSpeed**, an infrastructure-level AI Cloud Gateway that implements a protocol-transparent **Second-Request Acceleration Engine**. Operating as a universal drop-in proxy adhering strictly to standard HTTP/REST LLM protocols (such as `/v1/chat/completions`), NeuralSpeed eliminates redundant generation cycles without requiring model fine-tuning, weight quantization, or client-side SDK modifications. Through empirical evaluation conducted on dedicated tensor hardware (Groq LPU systems), ultra-scale cloud architectures (501-Billion parameter models), and enterprise multi-turn workflows, we demonstrate that:1. **Request #1 (Calibration Baseline):** Executes with zero semantic drift and 100% protocol transparency.2. **Requests #2+ (Accelerated Inference):** Consistently achieves **50% to 80% output token reduction** (empirically measuring between 70.3% and 78.8% in sustained agent loops and structured enterprise workloads).3. **Latency Dynamics:** Decreases round-trip inference latency by up to **3.5x**, while concurrently expanding hardware rate-limit headroom.4. **Structural Fidelity:** Preserves 100% syntactic validity, execution integrity, and contract compliance across all tested production domains. > **Intellectual Property Notice:** The internal architectural logic, state coordinate mappings, and proprietary acceleration algorithms of NeuralSpeed are trade secrets and are intentionally not disclosed in this document. This publication establishes formal prior art regarding the protocol-level interface, functional mechanics, zero-drift verification, and hardware performance metrics.