Llama 3.3 70B — B200 vs MI300X Performance per Dollar
Cost per million tokens of B200 (NVIDIA Blackwell) versus MI300X (AMD CDNA 3) 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.
B200 edges MI300X at 53 tok/s/user on Llama 3.3 70B — $0.07 per million tokens versus $0.22, a 194% cost-per-token gap.
Push Llama 3.3 70B to 73 tok/s/user and B200 lands at $0.10 per million tokens against MI300X's $0.41 — B200 pulls ahead by 294%.
B200: $0.14 per million tokens. MI300X: $0.71. Both at 93 tok/s/user on Llama 3.3 70B, with B200 420% cheaper. (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.)
Chip pricing (owning hyperscaler): B200 $1.73/chip/hr · MI300X $0.95/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 | B200:$0.074MI300X:$0.218 | B200:$0.103MI300X:$0.407 | B200:$0.136MI300X:$0.706 |
| Concurrency | B200:~128MI300X:~50 | B200:~128MI300X:~32 | B200:~86MI300X:~17 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.