DeepSeek R1 · Performance per Dollar

DeepSeek R1 — GB200 NVL72 vs MI355X Performance per Dollar

Cost per million tokens of GB200 NVL72 (NVIDIA Blackwell) versus MI355X (AMD CDNA 4) on DeepSeek R1. 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.

Push DeepSeek R1 to 74 tok/s/user and GB200 NVL72 lands at $0.08 per million tokens against MI355X's $0.23 — GB200 NVL72 pulls ahead by 196%.

GB200 NVL72: $0.38 per million tokens. MI355X: $1.00. Both at 131 tok/s/user on DeepSeek R1, with GB200 NVL72 161% cheaper.

Toward the upper edge of the 18–244 tok/s/user interactivity band — at 188 tok/s/user — GB200 NVL72 runs $1.05 per million tokens on DeepSeek R1 while MI355X runs $2.28. GB200 NVL72 is the cheaper choice by 118%. (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 →

DeepSeek R1: 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:$0.079MI355X:$0.233
GB200 NVL72:$0.383MI355X:$0.997
GB200 NVL72:$1.047MI355X:$2.283
Concurrency
GB200 NVL72:~2211MI355X:~254
GB200 NVL72:~311MI355X:~22
GB200 NVL72:~96MI355X:~4

Inference Performance

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