DeepSeek V4 Pro 1.6T — B300 vs MI355X Performance per Dollar
Cost per million tokens of B300 (NVIDIA Blackwell) versus MI355X (AMD CDNA 4) on DeepSeek V4 Pro 1.6T. 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 V4 Pro 1.6T to 36 tok/s/user and B300 lands at $0.12 per million tokens against MI355X's $0.29 — B300 pulls ahead by 153%.
B300: $0.91 per million tokens. MI355X: $1.33. Both at 67 tok/s/user on DeepSeek V4 Pro 1.6T, with B300 46% cheaper.
Toward the upper edge of the 6–127 tok/s/user interactivity band — at 97 tok/s/user — B300 runs $1.32 per million tokens on DeepSeek V4 Pro 1.6T while MI355X runs $2.06. B300 is the cheaper choice by 55%. (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.)
Chip pricing (owning hyperscaler): B300 $2.26/chip/hr · MI355X $1.50/chip/hr. Source: SemiAnalysis Market July 2026 Pricing Surveys & AI Cloud TCO Model.
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Dollar per Million Tokens | B300:$0.115MI355X:$0.292 | B300:$0.910MI355X:$1.327 | B300:$1.325MI355X:$2.059 |
| Concurrency | B300:~907MI355X:~176 | B300:~21MI355X:~20 | B300:~10MI355X:~9 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.