Head-to-head AI inference benchmark comparison of B300 (NVIDIA Blackwell) and GB200 NVL72 (NVIDIA Blackwell) on DeepSeekv4 Pro 0813 1.6T. 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.
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 β
At 111 tok/s/user interactivity on DeepSeekv4 Pro 0813 1.6T, B300 delivers 15703 tok/s/chip at $0.04 per million tokens; GB200 NVL72 delivers 30098 tok/s/chip at $0.02. GB200 NVL72 is 133% cheaper per token; GB200 NVL72 delivers 92% more tok/s/chip at this point.
B300 posts 5691 tok/s/chip for $0.11 per million tokens at 169 tok/s/user on DeepSeekv4 Pro 0813 1.6T; GB200 NVL72 posts 13108 tok/s/chip for $0.04. GB200 NVL72 is 180% cheaper per token; GB200 NVL72 delivers 130% more tok/s/chip.
Throughput at 227 tok/s/user on DeepSeekv4 Pro 0813 1.6T: B300 hits 3501 tok/s/chip, GB200 NVL72 hits 4252. Per-million costs land at $0.18 and $0.12 respectively. GB200 NVL72 is 48% cheaper per token; GB200 NVL72 delivers 21% more tok/s/chip. (Numbers reflect the default agentic-traces Β· fp4 selection for this URL β table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | B300:15703.3GB200 NVL72:30098.0 | B300:5690.9GB200 NVL72:13108.1 | B300:3501.0GB200 NVL72:4252.2 |
| Cost ($/M tok) | B300:$0.040GB200 NVL72:$0.017 | B300:$0.110GB200 NVL72:$0.039 | B300:$0.179GB200 NVL72:$0.122 |
| tok/s/MW | B300:8264915GB200 NVL72:16095213 | B300:2995196GB200 NVL72:7009654 | B300:1842655GB200 NVL72:2273893 |
| Concurrency | B300:~12GB200 NVL72:~330 | B300:~8GB200 NVL72:~43 | B300:~4GB200 NVL72:~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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DeepSeek V4 Pro 0813 1.6T 8K / 1K Total Tokens per $1 TCO vs. Interactivity