Qwen 3.5 397B-A17B · GPU comparison

Qwen 3.5 397B-A17B — GB200 NVL72 vs MI300X

Head-to-head AI inference benchmark comparison of GB200 NVL72 (NVIDIA Blackwell) and MI300X (AMD CDNA 3) on Qwen 3.5 397B-A17B. Latency, throughput, and cost across LLM workloads. Use the chart controls below to switch sequences, precisions, and metrics — same interactions as the main inference chart.

Near the low end of the 36–66 tok/s/user interactivity band, at 43 tok/s/user on Qwen 3.5 397B-A17B: GB200 NVL72 runs 12698 tok/s/GPU at $0.05/M tokens, MI300X runs 765 at $0.41/M. GB200 NVL72 is 738% cheaper per token; GB200 NVL72 delivers 1559% more tok/s/GPU.

Setting 51 tok/s/user as the target on Qwen 3.5 397B-A17B, GB200 NVL72 produces 12313 tok/s/GPU ($0.05 per million tokens) and MI300X produces 560 ($0.55). GB200 NVL72 is 1008% cheaper per token; GB200 NVL72 delivers 2101% more tok/s/GPU.

At 59 tok/s/user interactivity on Qwen 3.5 397B-A17B, GB200 NVL72 delivers 11730 tok/s/GPU at $0.05 per million tokens; MI300X delivers 394 tok/s/GPU at $0.80. GB200 NVL72 is 1437% cheaper per token; GB200 NVL72 delivers 2879% more tok/s/GPU at this point. (Numbers reflect the default 8k/1k · fp8 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)

View performance-per-dollar view →

Interpolated from real benchmark data. Edit target interactivity values below to compare at different operating points.
Metric
Interactivity (tok/s/user)
Interactivity (tok/s/user)
Interactivity (tok/s/user)
Throughput (tok/s/gpu)
GB200 NVL72:12697.9MI300X:765.3
GB200 NVL72:12313.4MI300X:559.5
GB200 NVL72:11730.0MI300X:393.8
Cost ($/M tok)
GB200 NVL72:$0.048MI300X:$0.405
GB200 NVL72:$0.050MI300X:$0.553
GB200 NVL72:$0.052MI300X:$0.804
tok/s/MW
GB200 NVL72:6790311MI300X:550571
GB200 NVL72:6584710MI300X:402536
GB200 NVL72:6272718MI300X:283284
Concurrency
GB200 NVL72:~2304MI300X:~17
GB200 NVL72:~1975MI300X:~11
GB200 NVL72:~1447MI300X:~6

Inference Performance

Inference performance metrics across different models, hardware configurations, and serving parameters.

Vendor:
Aggregation:
Spec Decoding: