Llama 3.3 70B — B200 vs MI355X Performance per Dollar
Cost per million tokens of B200 (NVIDIA Blackwell) versus MI355X (AMD CDNA 4) on Llama 3.3 70B. 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.
On Llama 3.3 70B at 54 tok/s/user, the per-million math comes out to $0.08 for B200 and $0.18 for MI355X; B200 delivers 115% more output per dollar.
At 75 tok/s/user on Llama 3.3 70B, B200 costs $0.12 per million tokens; MI355X costs $0.26. B200 is 117% more cost-efficient at this operating point.
B200 edges MI355X at 96 tok/s/user on Llama 3.3 70B — $0.16 per million tokens versus $0.55, a 242% cost-per-token gap. (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): B200 $1.95/GPU/hr · MI355X $1.48/GPU/hr. Source: SemiAnalysis Market August 2025 Pricing Surveys & AI Cloud TCO Model.
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Dollar per Million Tokens | B200:$0.085MI355X:$0.182 | B200:$0.120MI355X:$0.261 | B200:$0.160MI355X:$0.548 |
| Concurrency | B200:~128MI355X:~50 | B200:~128MI355X:~46 | B200:~77MI355X:~16 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.