Head-to-head AI inference benchmark comparison of H100 (NVIDIA Hopper) and MI300X (AMD CDNA 3) on gpt-oss 120B. Latency, throughput, and cost across LLM workloads. Use the chart controls below to switch sequences, precisions, and metrics — same interactions as the main inference chart.
Setting 89 tok/s/user as the target on gpt-oss 120B, H100 produces 6413 tok/s/chip ($0.05 per million tokens) and MI300X produces 5144 ($0.05). H100 is 1% cheaper per token; H100 delivers 25% more tok/s/chip.
At 142 tok/s/user interactivity on gpt-oss 120B, H100 delivers 4180 tok/s/chip at $0.08 per million tokens; MI300X delivers 3406 tok/s/chip at $0.08. Cost per token is essentially tied; H100 delivers 23% more tok/s/chip at this point.
H100 posts 2714 tok/s/chip for $0.12 per million tokens at 194 tok/s/user on gpt-oss 120B; MI300X posts 1102 tok/s/chip for $0.24. H100 is 100% cheaper per token; H100 delivers 146% more tok/s/chip. (Numbers reflect the default 8k/1k · fp4 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | H100:6412.7MI300X:5143.9 | H100:4180.1MI300X:3405.7 | H100:2714.1MI300X:1101.8 |
| Cost ($/M tok) | H100:$0.051MI300X:$0.051 | H100:$0.078MI300X:$0.077 | H100:$0.120MI300X:$0.240 |
| tok/s/MW | H100:4680812MI300X:3700621 | H100:3051141MI300X:2450139 | H100:1981084MI300X:792671 |
| Concurrency | H100:~32MI300X:~13 | H100:~14MI300X:~5 | H100:~6MI300X:~7 |
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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gpt-oss 120B 8K / 1K Total Tokens per $1 TCO vs. Interactivity