g5g vs g6 for LLM Serving: the Same Code, and 3.7x the Throughput

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LLM serving throughput comparison on AWS instances, directly relevant to cloud infrastructure and AI.

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Why It Matters

For platform engineers optimizing LLM inference costs on AWS, this demonstrates that GPU architecture (Ada vs Turing) dominates throughput far more than host CPU or instance size, and that standard monitoring masks the real bottleneck — dtype conversion — which only shows up in GPU kernel profiling.

Key Takeaways

Profile GPU kernel composition before scaling inference instances; the g6 family's L4 GPU eliminates the dtype conversion tax that silently consumes 87% of decode time on older T4G hardware.

g5g vs g6 for LLM Serving: the Same Code, and 3.7x the Throughput

Summary

Serving google/gemma-4-E2B-it on a g6.2xlarge (NVIDIA L4, Ada) delivers 48.5 tok/s decode throughput versus 12.9 tok/s on a g5g.2xlarge (T4G, Turing) — a 3.7x gain from the same byte-identical JAX code and weights. Profiling reveals 87% of g5g decode time is spent on dtype conversion (54.1%) and fp32 gemvx (32.8%), while the L4's native FP8/FP16 support eliminates these overheads entirely, achieving near-roofline HBM bandwidth utilization.

Author

xbill