DeepSeekv4 Pro 0813 1.6T — B200 vs GB300 NVL72
Head-to-head AI inference benchmark comparison of B200 (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 →
Throughput at 69 tok/s/user on DeepSeekv4 Pro 0813 1.6T: B200 hits 11397 tok/s/chip, GB300 NVL72 hits 67741. Per-million costs land at $0.04 and $0.01 respectively. GB300 NVL72 is 345% cheaper per token; GB300 NVL72 delivers 494% more tok/s/chip.
B200 / GB300 NVL72 on DeepSeekv4 Pro 0813 1.6T at 109 tok/s/user: 5994 / 6564 tok/s/chip, $0.08 / $0.10 per million tokens. B200 is 22% cheaper per token; GB300 NVL72 delivers 10% more tok/s/chip.
Toward the upper edge of the 30–187 tok/s/user interactivity band, at 148 tok/s/user on DeepSeekv4 Pro 0813 1.6T: B200 runs 3343 tok/s/chip at $0.14/M tokens, GB300 NVL72 runs 3615 at $0.18/M. B200 is 23% cheaper per token; GB300 NVL72 delivers 8% more tok/s/chip. (Numbers reflect the default agentic-traces · fp4 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | B200:11396.6GB300 NVL72:67741.2 | B200:5993.7GB300 NVL72:6564.4 | B200:3343.3GB300 NVL72:3615.2 |
| Cost ($/M tok) | B200:$0.042GB300 NVL72:$0.009 | B200:$0.080GB300 NVL72:$0.098 | B200:$0.144GB300 NVL72:$0.177 |
| tok/s/MW | B200:6664662GB300 NVL72:31953399 | B200:3505081GB300 NVL72:3096415 | B200:1955148GB300 NVL72:1705296 |
| Concurrency | B200:~19GB300 NVL72:~550 | B200:~8GB300 NVL72:~5 | B200:~4GB300 NVL72:~2 |
Total Tokens per $1 USD (Owning - Neocloud Giant) vs. Interactivity
DeepSeek V4 Pro 0813 1.6T • FP4 • 8K / 1K • Source: SemiAnalysis InferenceX™
DeepSeek V4 Pro 0813 1.6T • FP4 • 8K / 1K • Source: SemiAnalysis InferenceX™TCO $/chip/hr: VR200: 3.61H100: 1.55H200: 1.59B200: 2.07B300: 2.52GB200: 2.26GB300: 2.79MI300X: 1.16MI325X: 1.32MI355X: 2.09RTX6000PRO: 0.75Jalapeño (Teacup): 1.56
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.
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Interactivity is the rate at which a single user receives generated tokens while the model streams its answer — how quickly new words appear on screen. Higher values feel snappier; operators trade it against batch throughput.
How many total tokens (input + output) one US dollar of infrastructure spend buys, priced with the all-in hourly ownership cost of a Neocloud Giant operator. It is the reciprocal of cost per token, so higher means cheaper.
Formula: tok/$ = (total tok/s/chip × 3,600) ÷ all-in cost per chip-hour ($)