gpt-oss 120B · Performance per Dollar

gpt-oss 120B — GB200 NVL72 vs MI355X Performance per Dollar

Cost per million tokens of GB200 NVL72 (NVIDIA Blackwell) versus MI355X (AMD CDNA 4) on gpt-oss 120B. Owning-hyperscaler TCO normalized by output tokens — performance per dollar across LLM workloads. Pick the more cost-efficient SKU at every target interactivity level. Use the chart controls below to switch sequences, precisions, and metrics — same interactions as the main inference chart.

GB200 NVL72: $0.03 per million tokens. MI355X: $0.06. Both at 132 tok/s/user on gpt-oss 120B, with GB200 NVL72 65% cheaper.

Around the middle of the 71–317 tok/s/user interactivity band — at 194 tok/s/user — GB200 NVL72 runs $0.06 per million tokens on gpt-oss 120B while MI355X runs $0.13. GB200 NVL72 is the cheaper choice by 141%.

On gpt-oss 120B at 256 tok/s/user, the per-million math comes out to $0.11 for GB200 NVL72 and $0.26 for MI355X; GB200 NVL72 delivers 141% more output per dollar. (Numbers reflect the default 1k/1k · fp4 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)

GPU pricing (owning hyperscaler): GB200 NVL72 $2.21/GPU/hr · MI355X $1.48/GPU/hr. Source: SemiAnalysis Market August 2025 Pricing Surveys & AI Cloud TCO Model.

View full latency + throughput comparison →

gpt-oss 120B: GB200 NVL72 versus MI355X cost per million tokens at matched interactivity levels
GB200 NVL72 versus MI355X cost per million tokens for this comparison's canonical default workload. Lower cost indicates better performance per dollar.
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)
Dollar per Million Tokens
GB200 NVL72:$0.034MI355X:$0.055
GB200 NVL72:$0.056MI355X:$0.135
GB200 NVL72:$0.107MI355X:$0.259
Concurrency
GB200 NVL72:~1178MI355X:~29
GB200 NVL72:~436MI355X:~8
GB200 NVL72:~60MI355X:~7

Inference Performance

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