Head-to-head AI inference benchmark comparison of GB200 NVL72 (NVIDIA Blackwell) 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 β same interactions as the main inference chart.
At 113 tok/s/user interactivity on gpt-oss 120B, GB200 NVL72 delivers 42816 tok/s/chip at $0.01 per million tokens; MI300X delivers 4178 tok/s/chip at $0.06. GB200 NVL72 is 423% cheaper per token; GB200 NVL72 delivers 925% more tok/s/chip at this point.
GB200 NVL72 posts 33220 tok/s/chip for $0.02 per million tokens at 157 tok/s/user on gpt-oss 120B; MI300X posts 2916 tok/s/chip for $0.09. GB200 NVL72 is 482% cheaper per token; GB200 NVL72 delivers 1039% more tok/s/chip.
Throughput at 202 tok/s/user on gpt-oss 120B: GB200 NVL72 hits 26214 tok/s/chip, MI300X hits 996. Per-million costs land at $0.02 and $0.26 respectively. GB200 NVL72 is 1244% cheaper per token; GB200 NVL72 delivers 2531% more tok/s/chip. (Numbers reflect the default 8k/1k Β· fp4 selection for this URL β table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | GB200 NVL72:42815.7MI300X:4178.4 | GB200 NVL72:33220.5MI300X:2916.1 | GB200 NVL72:26214.2MI300X:996.3 |
| Cost ($/M tok) | GB200 NVL72:$0.012MI300X:$0.063 | GB200 NVL72:$0.016MI300X:$0.090 | GB200 NVL72:$0.020MI300X:$0.265 |
| tok/s/MW | GB200 NVL72:22896080MI300X:3006041 | GB200 NVL72:17764960MI300X:2097905 | GB200 NVL72:14018263MI300X:716772 |
| Concurrency | GB200 NVL72:~375MI300X:~9 | GB200 NVL72:~317MI300X:~4 | GB200 NVL72:~90MI300X:~5 |
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