Head-to-head AI inference benchmark comparison of GB300 NVL72 (NVIDIA Blackwell) and MI300X (AMD CDNA 3) on Kimi K2.5/K2.6/K2.7-Code 1T. 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.
At 63 tok/s/user on Kimi K2.5/K2.6/K2.7-Code 1T, GB300 NVL72 delivers 10203 tok/s/chip at $0.06 per million tokens; MI300X hasn't been benchmarked at this target.
GB300 NVL72 hits 1685 tok/s/chip for $0.38 per million tokens at 103 tok/s/user on Kimi K2.5/K2.6/K2.7-Code 1T. No MI300X data at this operating point.
GB300 NVL72: 731 tok/s/chip, $0.88 per million tokens at 143 tok/s/user on Kimi K2.5/K2.6/K2.7-Code 1T. MI300X is unmeasured here. (Numbers reflect the default 8k/1k · fp4 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | GB300 NVL72:10202.8MI300X:— | GB300 NVL72:1684.6MI300X:— | GB300 NVL72:731.1MI300X:— |
| Cost ($/M tok) | GB300 NVL72:$0.063MI300X:— | GB300 NVL72:$0.381MI300X:— | GB300 NVL72:$0.878MI300X:— |
| tok/s/MW | GB300 NVL72:4812653MI300X:— | GB300 NVL72:794621MI300X:— | GB300 NVL72:344856MI300X:— |
| Concurrency | GB300 NVL72:~1202MI300X:— | GB300 NVL72:~68MI300X:— | GB300 NVL72:~26MI300X:— |
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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Kimi K2.5/2.6/2.7-Code 1T 8K / 1K Total Tokens per $1 TCO vs. Interactivity