AWS G7 instances beat G5, G6 in LLM inference benchmarks

AWS's benchmark post targets a practical decision: which GPU instance to pick for LLM inference on SageMaker AI. With generative AI moving from experimentation to production, infrastructure costs directly impact the bottom line, making instance choice critical. The post's first test compares a coding model across G5, G6, and G7, while the second uses an automated recommendation tool to evaluate a different model across G6, G6e, and G7.

A key structural advantage is G7's native support for Blackwell's 4-bit floating-point format, which older generations run without hardware acceleration. G7 achieves its gains with fewer GPUs and less memory—two GPUs with 64 GB versus four with 96 GB on G5/G6—highlighting the generational jump in efficiency.

The stakes are clear for AWS customers: sticking with G5/G6 may mean paying more for less performance, but the gains are workload-dependent. G7 is currently limited to two AWS regions, and the best configuration varies by input length. The test results are a baseline; teams should benchmark their own workloads before committing.