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DeepSeek R1 · Chip comparison

DeepSeek R1 — GB300 NVL72 vs H100

Head-to-head AI inference benchmark comparison of GB300 NVL72 (NVIDIA Blackwell) and H100 (NVIDIA Hopper) 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.

At 45 tok/s/user interactivity on DeepSeek R1, GB300 NVL72 delivers 8703 tok/s/chip at $0.07 per million tokens; H100 delivers 739 tok/s/chip at $0.44. GB300 NVL72 is 496% cheaper per token; GB300 NVL72 delivers 1077% more tok/s/chip at this point.

GB300 NVL72 posts 7167 tok/s/chip for $0.09 per million tokens at 71 tok/s/user on DeepSeek R1; H100 posts 281 tok/s/chip for $1.16. GB300 NVL72 is 1193% cheaper per token; GB300 NVL72 delivers 2454% more tok/s/chip.

Throughput at 97 tok/s/user on DeepSeek R1: GB300 NVL72 hits 5528 tok/s/chip, H100 hits 154. Per-million costs land at $0.12 and $2.11 respectively. GB300 NVL72 is 1714% cheaper per token; GB300 NVL72 delivers 3482% more tok/s/chip. (Numbers reflect the default 8k/1k · fp8 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)
GB300 NVL72:8703.0H100:739.5
GB300 NVL72:7167.1H100:280.6
GB300 NVL72:5527.9H100:154.3
Cost ($/M tok)
GB300 NVL72:$0.074H100:$0.439
GB300 NVL72:$0.090H100:$1.158
GB300 NVL72:$0.116H100:$2.106
tok/s/MW
GB300 NVL72:4105187H100:539778
GB300 NVL72:3380715H100:204852
GB300 NVL72:2607497H100:112644
Concurrency
GB300 NVL72:~1229H100:~132
GB300 NVL72:~732H100:~41
GB300 NVL72:~437H100:~17

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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