Head-to-head AI inference benchmark comparison of GB300 NVL72 (NVIDIA Blackwell) and Vera Rubin NVL72 (NVIDIA Vera Rubin) on DeepSeekv4 Pro 0813 1.6T. 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.
AgentX replays real coding-agent sessions rather than fixed-length prompts, so context grows turn over turn and most of each request is served from cache instead of being recomputed. That turns the comparison into a systems question: KV transfer between nodes, prefix-aware routing, and cache capacity all move the curve alongside raw chip throughput. Fixed-sequence workloads stay the clean baseline for kernel and silicon performance, so the two scenarios answer different questions about the same hardware. Learn more about AgentX →
GB300 NVL72 posts 29219 tok/s/chip for $0.02 per million tokens at 127 tok/s/user on DeepSeekv4 Pro 0813 1.6T; Vera Rubin NVL72 posts 258818 tok/s/chip for $0.00. Vera Rubin NVL72 is 467% cheaper per token; Vera Rubin NVL72 delivers 786% more tok/s/chip.
Throughput at 179 tok/s/user on DeepSeekv4 Pro 0813 1.6T: GB300 NVL72 hits 13491 tok/s/chip, Vera Rubin NVL72 hits 159290. Per-million costs land at $0.05 and $0.01 respectively. Vera Rubin NVL72 is 656% cheaper per token; Vera Rubin NVL72 delivers 1081% more tok/s/chip.
GB300 NVL72 / Vera Rubin NVL72 on DeepSeekv4 Pro 0813 1.6T at 232 tok/s/user: 4462 / 5734 tok/s/chip, $0.14 / $0.17 per million tokens. GB300 NVL72 is 22% cheaper per token; Vera Rubin NVL72 delivers 29% more tok/s/chip. (Numbers reflect the default agentic-traces · fp4 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | GB300 NVL72:29218.9Vera Rubin NVL72:258817.8 | GB300 NVL72:13491.0Vera Rubin NVL72:159289.6 | GB300 NVL72:4461.6Vera Rubin NVL72:5733.8 |
| Cost ($/M tok) | GB300 NVL72:$0.022Vera Rubin NVL72:$0.004 | GB300 NVL72:$0.048Vera Rubin NVL72:$0.006 | GB300 NVL72:$0.144Vera Rubin NVL72:$0.175 |
| tok/s/MW | GB300 NVL72:13782488Vera Rubin NVL72:78429629 | GB300 NVL72:6363695Vera Rubin NVL72:48269568 | GB300 NVL72:2104551Vera Rubin NVL72:1737527 |
| Concurrency | GB300 NVL72:~166Vera Rubin NVL72:~2771 | GB300 NVL72:~46Vera Rubin NVL72:~1548 | GB300 NVL72:~6Vera Rubin NVL72:~24 |
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.
Shift+Scroll to zoom • Drag to pan • Double-click to reset • Click a point to pin tooltip
DeepSeek V4 Pro 0813 1.6T 8K / 1K Total Tokens per $1 TCO vs. Interactivity