A Deep Dive into LLM Inference Latencies

Large language model deployment is becoming a necessity for modern applications, with inference optimization playing a central role in shaping both user experience and cost. Latency spans GPU efficiency, network routing, and autoscaling, making it a complex but rewarding area to improve. At Hathora, we’re drawing on lessons from ultra-low latency gaming infrastructure as we begin our journey toward becoming a competitive inference provider. Techniques like speculative decoding, prefill–decode disaggregation, and edge-aware deployment highlight opportunities to reduce end-to-end inference times. To ground this work, we profiled leading open models in vLLM to better understand real bottlenecks and the optimizations that deliver measurable gains. So, let’s dive into the analysis of node-bound inference latencies. All benchmarks were conducted on the NVIDIA H100 80GB PCIe variant. Note: The SXM5 variant offers 3.35 TB/s bandwidth via HBM3, 16,896 CUDA cores, and a peak TDP 700W (double the

Large language model deployment is becoming a necessity for modern applications, with inference optimization playing a central role in shaping both user experience and cost. Latency spans GPU efficiency, network routing, and autoscaling, making it a complex but rewarding area to improve. At Hathora, we’re drawing on lessons from ultra-low latency gaming infrastructure as we begin our journey toward becoming a competitive inference provider. Techniques like speculative decoding, prefill–decode disaggregation, and edge-aware deployment highlight opportunities to reduce end-to-end inference times

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• LIME YAO