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DeepSeek R1 · Chip comparison

DeepSeek R1 — B300 vs GB300 NVL72

Head-to-head AI inference benchmark comparison of B300 (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.

At 73 tok/s/user interactivity on DeepSeek R1, B300 delivers 3748 tok/s/chip at $0.17 per million tokens; GB300 NVL72 delivers 6969 tok/s/chip at $0.09. GB300 NVL72 is 82% cheaper per token; GB300 NVL72 delivers 86% more tok/s/chip at this point.

B300 posts 1016 tok/s/chip for $0.62 per million tokens at 126 tok/s/user on DeepSeek R1; GB300 NVL72 posts 3763 tok/s/chip for $0.17. GB300 NVL72 is 262% cheaper per token; GB300 NVL72 delivers 270% more tok/s/chip.

Throughput at 178 tok/s/user on DeepSeek R1: B300 hits 719 tok/s/chip, GB300 NVL72 hits 545. Per-million costs land at $0.87 and $1.18 respectively. B300 is 35% cheaper per token; B300 delivers 32% 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)
B300:3747.8GB300 NVL72:6968.6
B300:1015.7GB300 NVL72:3762.8
B300:718.9GB300 NVL72:544.7
Cost ($/M tok)
B300:$0.168GB300 NVL72:$0.092
B300:$0.618GB300 NVL72:$0.171
B300:$0.873GB300 NVL72:$1.178
tok/s/MW
B300:1972518GB300 NVL72:3287059
B300:534576GB300 NVL72:1774885
B300:378384GB300 NVL72:256953
Concurrency
B300:~88GB300 NVL72:~679
B300:~8GB300 NVL72:~253
B300:~9GB300 NVL72:~20

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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