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DeepSeek R1 · Chip comparison

DeepSeek R1 — GB200 NVL72 vs GB300 NVL72

Head-to-head AI inference benchmark comparison of GB200 NVL72 (NVIDIA Blackwell) and GB300 NVL72 (NVIDIA Blackwell) on DeepSeek R1. 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.

Setting 93 tok/s/user as the target on DeepSeek R1, GB200 NVL72 produces 11532 tok/s/chip ($0.04 per million tokens) and GB300 NVL72 produces 12534 ($0.05). GB200 NVL72 is 14% cheaper per token; GB300 NVL72 delivers 9% more tok/s/chip.

At 162 tok/s/user interactivity on DeepSeek R1, GB200 NVL72 delivers 4192 tok/s/chip at $0.12 per million tokens; GB300 NVL72 delivers 5394 tok/s/chip at $0.12. GB300 NVL72 is 4% cheaper per token; GB300 NVL72 delivers 29% more tok/s/chip at this point.

GB200 NVL72 posts 1012 tok/s/chip for $0.51 per million tokens at 231 tok/s/user on DeepSeek R1; GB300 NVL72 posts 1065 tok/s/chip for $0.60. GB200 NVL72 is 18% cheaper per token; GB300 NVL72 delivers 5% more tok/s/chip. (Numbers reflect the default 8k/1k · fp4 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)

View performance-per-dollar view →

Interpolated from real benchmark data. Edit target interactivity values below to compare at different operating points.
Metric
Interactivity (tok/s/user)
Interactivity (tok/s/user)
Interactivity (tok/s/user)
Throughput (tok/s/chip)
GB200 NVL72:11531.8GB300 NVL72:12534.4
GB200 NVL72:4192.5GB300 NVL72:5393.8
GB200 NVL72:1012.2GB300 NVL72:1065.2
Cost ($/M tok)
GB200 NVL72:$0.045GB300 NVL72:$0.051
GB200 NVL72:$0.123GB300 NVL72:$0.119
GB200 NVL72:$0.510GB300 NVL72:$0.602
tok/s/MW
GB200 NVL72:6166725GB300 NVL72:5912464
GB200 NVL72:2241975GB300 NVL72:2544264
GB200 NVL72:541259GB300 NVL72:502459
Concurrency
GB200 NVL72:~913GB300 NVL72:~715
GB200 NVL72:~178GB300 NVL72:~217
GB200 NVL72:~19GB300 NVL72:~26

Inference Performance

Agentic inference metrics from the AgentX scenario and fixed-sequence inference metrics across models, hardware configurations, and serving parameters.

Total Tokens per $1 USD (Owning - Neocloud Giant) vs. Interactivity

DeepSeek R1 0528 671B • 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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