Head-to-head AI inference benchmark comparison of H100 (NVIDIA Hopper) and MI355X (AMD CDNA 4) on Llama 3.3 70B. Latency, throughput, and cost across LLM workloads. Use the chart controls below to switch sequences, precisions, and metrics β same interactions as the main inference chart.
H100 / MI355X on Llama 3.3 70B at 38 tok/s/user: 1848 / 4213 tok/s/chip, $0.18 / $0.10 per million tokens. MI355X is 78% cheaper per token; MI355X delivers 128% more tok/s/chip.
Around the middle of the 19β97 tok/s/user interactivity band, at 58 tok/s/user on Llama 3.3 70B: H100 runs 1171 tok/s/chip at $0.28/M tokens, MI355X runs 2735 at $0.15/M. MI355X is 82% cheaper per token; MI355X delivers 133% more tok/s/chip.
Setting 78 tok/s/user as the target on Llama 3.3 70B, H100 produces 877 tok/s/chip ($0.37 per million tokens) and MI355X produces 1324 ($0.31). MI355X is 18% cheaper per token; MI355X delivers 51% more tok/s/chip. (Numbers reflect the default 8k/1k Β· fp8 selection for this URL β table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | H100:1847.6MI355X:4212.9 | H100:1171.4MI355X:2735.2 | H100:877.4MI355X:1323.7 |
| Cost ($/M tok) | H100:$0.176MI355X:$0.099 | H100:$0.277MI355X:$0.152 | H100:$0.370MI355X:$0.315 |
| tok/s/MW | H100:1348638MI355X:2015740 | H100:855073MI355X:1308691 | H100:640410MI355X:633355 |
| Concurrency | H100:~20MI355X:~19 | H100:~16MI355X:~25 | H100:~8MI355X:~8 |
Agentic inference metrics from the AgentX scenario and fixed-sequence inference metrics across models, hardware configurations, and serving parameters.
TCO $/chip/hr:
Source: SemiAnalysis Market July 2026 Pricing Surveys & AI Cloud TCO Model
Note: Disaggregated inference configurations (e.g., MoRI SGLang, Dynamo TRTLLM) calculate cost per million tokens per decode chip or per prefill chip, rather than per total chip count. This makes direct token cost comparison with aggregated configs not an apples-to-apples comparison.
Note: Disaggregated inference configurations (e.g., MoRI SGLang, Dynamo TRTLLM) calculate input throughput per decode chip or per prefill chip, rather than per total chip count. This makes direct input throughput comparison with aggregated configs not an apples-to-apples comparison.
Note: Disaggregated inference configurations (e.g., MoRI SGLang, Dynamo TRTLLM) calculate output throughput per decode chip or per prefill chip, rather than per total chip count. This makes direct output throughput comparison with aggregated configs not an apples-to-apples comparison.
Note: Disaggregated inference configurations (e.g., MoRI SGLang, Dynamo TRTLLM) calculate power per decode chip or per prefill chip, rather than per total chip count. This makes direct power comparison with aggregated configs not an apples-to-apples comparison.
Note: Disaggregated inference configurations (e.g., MoRI SGLang, Dynamo TRTLLM) calculate Joules per decode chip or per prefill chip, rather than per total chip count. This makes direct Joules per token comparison with aggregated configs not an apples-to-apples comparison.
Matching measurements exist, but their chip series are hidden.
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Llama 3.3 70B Instruct 8K / 1K Total Tokens per $1 TCO vs. Interactivity