Nvidia’s GB300 FP4 submission still trails Google’s TPUv7 on cost efficiency

Nvidia has spent years as the undisputed heavyweight of AI hardware. On one closely watched inference benchmark, it is now the one chasing. According to SemiAnalysis, Nvidia’s GB300 FP4 submission remains 1.64 times worse in performance per dollar than Google’s TPUv7. That gap persisted even after Nvidia provided feedback and put engineering work into improving its results. The numbers behind the gap The SemiAnalysis report, published on October 19, 2026, focused on agentic inference benchmarks used for external customers. On the performance per dollar measure, Google’s TPUv7 submissions came in between 1.42 and 1.67 times better than Nvidia’s GB300 FP4. Nvidia’s first response to the benchmark did not close that gap, leaving it 1.64 times behind. The format detail makes the result more striking. Nvidia’s entry used FP4, while Google’s TPUv7 ran on FP8. FP4 uses four bits and FP8 uses eight bits to represent each number during computation, so FP4 is the leaner, more aggressive format designed to squeeze extra throughput out of the hardware. Nvidia brought the lighter-weight tool and still came out behind on cost efficiency. On paper, the GB300 NVL72 delivers up to 15 PFLOPS of dens...