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SmartScaler on NVIDIA B200: Fastest Path to High-Throughput LLM Inference

This performance report demonstrates how Avesha SmartScaler—our RL-based autoscaling engine—dramatically outperforms traditional HPA when serving LLM inference workloads on NVIDIA HGX B200 systems. Using a production-grade setup with Llama-3.1 70B FP8 on Supermicro B200 nodes, SmartScaler consistently scales earlier, processes significantly more tokens during bursts, and keeps queues near-zero even under aggressive load patterns. By predicting traffic, understanding GPU-level signals, and estimating true pod capacity, SmartScaler unlocks up to 3× higher instantaneous throughput, lower latency, and far more efficient GPU utilization compared to reactive autoscalers. This document provides the full methodology, architecture references, scaling model details, and side-by-side benchmarking results for SmartScaler vs. HPA on B200.