DeepSeekv4 Pro 0813 1.6T — B300 vs GB300 NVL72
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 68 tok/s/user: 15868 / 69703 tok/s/chip, $0.04 / $0.01 per million tokens. GB300 NVL72 is 330% cheaper per token; GB300 NVL72 delivers 339% more tok/s/chip.
Around the middle of the 30–181 tok/s/user interactivity band, at 106 tok/s/user on DeepSeekv4 Pro 0813 1.6T: B300 runs 9445 tok/s/chip at $0.07/M tokens, GB300 NVL72 runs 6713 at $0.10/M. B300 is 44% cheaper per token; B300 delivers 41% more tok/s/chip.
Setting 144 tok/s/user as the target on DeepSeekv4 Pro 0813 1.6T, B300 produces 5520 tok/s/chip ($0.11 per million tokens) and GB300 NVL72 produces 3658 ($0.18). B300 is 54% cheaper per token; B300 delivers 51% more tok/s/chip. (Numbers reflect the default agentic-traces · fp4 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | B300:15868.3GB300 NVL72:69702.9 | B300:9444.8GB300 NVL72:6712.6 | B300:5519.8GB300 NVL72:3658.0 |
| Cost ($/M tok) | B300:$0.040GB300 NVL72:$0.009 | B300:$0.066GB300 NVL72:$0.096 | B300:$0.114GB300 NVL72:$0.175 |
| tok/s/MW | B300:8351724GB300 NVL72:32878737 | B300:4970949GB300 NVL72:3166300 | B300:2905169GB300 NVL72:1725449 |
| Concurrency | B300:~13GB300 NVL72:~533 | B300:~7GB300 NVL72:~5 | B300:~3GB300 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 ($)