Qwen 3.5 397B-A17B · GPU comparison

Qwen 3.5 397B-A17B — GB200 NVL72 vs MI355X

Head-to-head AI inference benchmark comparison of GB200 NVL72 (NVIDIA Blackwell) and MI355X (AMD CDNA 4) 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.

At 64 tok/s/user interactivity on Qwen 3.5 397B-A17B, GB200 NVL72 delivers 1239 tok/s/GPU at $0.51 per million tokens; MI355X delivers 2131 tok/s/GPU at $0.19. MI355X is 163% cheaper per token; MI355X delivers 72% more tok/s/GPU at this point.

GB200 NVL72 posts 464 tok/s/GPU for $1.30 per million tokens at 101 tok/s/user on Qwen 3.5 397B-A17B; MI355X posts 1218 tok/s/GPU for $0.33. MI355X is 293% cheaper per token; MI355X delivers 163% more tok/s/GPU.

Throughput at 137 tok/s/user on Qwen 3.5 397B-A17B: GB200 NVL72 hits 206 tok/s/GPU, MI355X hits 873. Per-million costs land at $2.98 and $0.47 respectively. MI355X is 529% cheaper per token; MI355X delivers 324% more tok/s/GPU. (Numbers reflect the default 1k/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 NVL72:1239.3MI355X:2131.3
GB200 NVL72:463.7MI355X:1217.8
GB200 NVL72:205.8MI355X:872.6
Cost ($/M tok)
GB200 NVL72:$0.506MI355X:$0.193
GB200 NVL72:$1.297MI355X:$0.330
GB200 NVL72:$2.983MI355X:$0.475
tok/s/MW
GB200 NVL72:590129MI355X:804277
GB200 NVL72:220809MI355X:459536
GB200 NVL72:98013MI355X:329279
Concurrency
GB200 NVL72:~113MI355X:~68
GB200 NVL72:~20MI355X:~25
GB200 NVL72:~7MI355X:~13

Inference Performance

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

Vendor:
Aggregation:
Spec Decoding: