Head-to-head AI inference benchmark comparison of B300 (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 →
B300 posts 11887 tok/s/chip for $0.05 per million tokens at 128 tok/s/user on DeepSeekv4 Pro 0813 1.6T; Vera Rubin NVL72 posts 257744 tok/s/chip for $0.00. Vera Rubin NVL72 is 1257% cheaper per token; Vera Rubin NVL72 delivers 2068% more tok/s/chip.
Throughput at 182 tok/s/user on DeepSeekv4 Pro 0813 1.6T: B300 hits 5017 tok/s/chip, Vera Rubin NVL72 hits 150759. Per-million costs land at $0.13 and $0.01 respectively. Vera Rubin NVL72 is 1781% cheaper per token; Vera Rubin NVL72 delivers 2905% more tok/s/chip.
B300 / Vera Rubin NVL72 on DeepSeekv4 Pro 0813 1.6T at 235 tok/s/user: 3178 / 5406 tok/s/chip, $0.20 / $0.19 per million tokens. Vera Rubin NVL72 is 6% cheaper per token; Vera Rubin NVL72 delivers 70% more tok/s/chip. (Numbers reflect the default agentic-traces · fp4 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | B300:11886.9Vera Rubin NVL72:257744.0 | B300:5017.0Vera Rubin NVL72:150759.3 | B300:3178.3Vera Rubin NVL72:5406.3 |
| Cost ($/M tok) | B300:$0.053Vera Rubin NVL72:$0.004 | B300:$0.125Vera Rubin NVL72:$0.007 | B300:$0.198Vera Rubin NVL72:$0.185 |
| tok/s/MW | B300:6256282Vera Rubin NVL72:78104254 | B300:2640540Vera Rubin NVL72:45684648 | B300:1672807Vera Rubin NVL72:1638287 |
| Concurrency | B300:~8Vera Rubin NVL72:~2760 | B300:~7Vera Rubin NVL72:~1413 | B300:~3Vera Rubin NVL72:~23 |
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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DeepSeek V4 Pro 0813 1.6T 8K / 1K Total Tokens per $1 TCO vs. Interactivity