Head-to-head AI inference benchmark comparison of B300 (NVIDIA Blackwell) and GB300 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 — 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 / GB300 NVL72 on DeepSeekv4 Pro 0813 1.6T at 70 tok/s/user: 17011 / 72827 tok/s/chip, $0.04 / $0.01 per million tokens. GB300 NVL72 is 319% cheaper per token; GB300 NVL72 delivers 328% more tok/s/chip.
Around the middle of the 30–191 tok/s/user interactivity band, at 111 tok/s/user on DeepSeekv4 Pro 0813 1.6T: B300 runs 8554 tok/s/chip at $0.07/M tokens, GB300 NVL72 runs 22928 at $0.03/M. GB300 NVL72 is 162% cheaper per token; GB300 NVL72 delivers 168% more tok/s/chip.
Setting 151 tok/s/user as the target on DeepSeekv4 Pro 0813 1.6T, B300 produces 5174 tok/s/chip ($0.12 per million tokens) and GB300 NVL72 produces 3579 ($0.18). B300 is 48% cheaper per token; B300 delivers 45% more tok/s/chip. (Numbers reflect the default agentic-traces · fp4 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | B300:17010.5GB300 NVL72:72827.2 | B300:8553.9GB300 NVL72:22928.0 | B300:5173.5GB300 NVL72:3579.1 |
| Cost ($/M tok) | B300:$0.037GB300 NVL72:$0.009 | B300:$0.073GB300 NVL72:$0.028 | B300:$0.121GB300 NVL72:$0.179 |
| tok/s/MW | B300:8952911GB300 NVL72:34352469 | B300:4502033GB300 NVL72:10815099 | B300:2722910GB300 NVL72:1688257 |
| Concurrency | B300:~12GB300 NVL72:~530 | B300:~7GB300 NVL72:~172 | B300:~2GB300 NVL72:~1 |
Agentic inference metrics from the AgentX scenario and fixed-sequence inference metrics across models, hardware configurations, and serving parameters.
TCO $/chip/hr: VR200: 3.61H100: 1.17H200: 1.22B200: 1.73B300: 2.26GB200: 1.86GB300: 2.31MI300X: 0.95MI325X: 1.1MI355X: 1.5RTX6000PRO: 0.68Jalapeño (Teacup): 1.47
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