DeepSeek R1 · Performance per Dollar

DeepSeek R1 — GB300 NVL72 vs MI325X Performance per Dollar

Cost per million tokens of GB300 NVL72 (NVIDIA Blackwell) versus MI325X (AMD CDNA 3) 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 39 tok/s/user and GB300 NVL72 lands at $0.09 per million tokens against MI325X's $1.03 — GB300 NVL72 pulls ahead by 997%.

GB300 NVL72: $0.11 per million tokens. MI325X: $1.57. Both at 50 tok/s/user on DeepSeek R1, with GB300 NVL72 1364% cheaper.

Toward the upper edge of the 29–70 tok/s/user interactivity band — at 60 tok/s/user — GB300 NVL72 runs $0.12 per million tokens on DeepSeek R1 while MI325X runs $2.67. GB300 NVL72 is the cheaper choice by 2133%. (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.)

GPU pricing (owning hyperscaler): GB300 NVL72 $2.65/GPU/hr · MI325X $1.28/GPU/hr. Source: SemiAnalysis Market August 2025 Pricing Surveys & AI Cloud TCO Model.

View full latency + throughput comparison →

DeepSeek R1: GB300 NVL72 versus MI325X cost per million tokens at matched interactivity levels
GB300 NVL72 versus MI325X 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
GB300 NVL72:$0.094MI325X:$1.027
GB300 NVL72:$0.107MI325X:$1.566
GB300 NVL72:$0.120MI325X:$2.672
Concurrency
GB300 NVL72:~2990MI325X:~39
GB300 NVL72:~1960MI325X:~19
GB300 NVL72:~1352MI325X:~9

Inference Performance

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