Llama 3.3 70B — H200 vs MI355X Performance per Dollar
Cost per million tokens of H200 (NVIDIA Hopper) 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 $0.11 for H200 and $0.18 for MI355X; H200 delivers 62% more output per dollar.
At 75 tok/s/user on Llama 3.3 70B, H200 costs $0.16 per million tokens; MI355X costs $0.26. H200 is 61% more cost-efficient at this operating point.
H200 edges MI355X at 96 tok/s/user on Llama 3.3 70B — $0.26 per million tokens versus $0.56, a 114% 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.)
Chip pricing (owning hyperscaler): H200 $1.22/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 | H200:$0.113MI355X:$0.184 | H200:$0.164MI355X:$0.265 | H200:$0.260MI355X:$0.555 |
| Concurrency | H200:~64MI355X:~50 | H200:~63MI355X:~46 | H200:~29MI355X:~16 |
Inference Performance
Inference performance metrics across different models, hardware configurations, and serving parameters.