AWS g6 Delivers 3.7x LLM Throughput Over g5g Due to GPU Architecture Gap

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A developer benchmarked AWS g5g.2xlarge and g6.2xlarge instances serving Google's Gemma-4-E2B-it model using identical code and weights, finding the g6 achieved roughly 3.7 times higher decode throughput. The g5g pairs a Graviton2 host with an NVIDIA T4G (Turing, SM 7.5), while the g6 uses an x86_64 host with an NVIDIA L4 (Ada, SM 8.9) and significantly more GPU memory. Profiling revealed that 87% of decode time on the g5g was consumed by dtype conversion and an fp32 fallback path, because the Turing architecture lacks native bfloat16 support and silently emulates it. The g6's L4 GPU, which natively supports bfloat16, ran near its full memory-bandwidth roofline, while the g5g operated at only 26% of its own. The findings highlight that a mismatched compute dtype causes no errors but silently degrades throughput, making architecture-aware dtype selection critical when deploying LLMs on GPU instances.

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