Head-to-head AI inference benchmark comparison of GB300 NVL72 (NVIDIA Blackwell) and RTX PRO 6000 (NVIDIA Blackwell) on Qwen 3.5 397B-A17B. Latency, throughput, and cost across LLM workloads. Use the chart controls below to switch sequences, precisions, and metrics β 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 β
At 196 tok/s/user on Qwen 3.5 397B-A17B, GB300 NVL72 delivers 36317 tok/s/chip at $0.02 per million tokens; RTX PRO 6000 hasn't been benchmarked at this target.
GB300 NVL72 hits 22164 tok/s/chip for $0.03 per million tokens at 325 tok/s/user on Qwen 3.5 397B-A17B. No RTX PRO 6000 data at this operating point.
GB300 NVL72: 11946 tok/s/chip, $0.05 per million tokens at 453 tok/s/user on Qwen 3.5 397B-A17B. RTX PRO 6000 is unmeasured here. (Numbers reflect the default agentic-traces Β· fp8 selection for this URL β table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | GB300 NVL72:36317.0RTX PRO 6000:β | GB300 NVL72:22163.8RTX PRO 6000:β | GB300 NVL72:11945.9RTX PRO 6000:β |
| Cost ($/M tok) | GB300 NVL72:$0.018RTX PRO 6000:β | GB300 NVL72:$0.029RTX PRO 6000:β | GB300 NVL72:$0.054RTX PRO 6000:β |
| tok/s/MW | GB300 NVL72:17130645RTX PRO 6000:β | GB300 NVL72:10454646RTX PRO 6000:β | GB300 NVL72:5634843RTX PRO 6000:β |
| Concurrency | GB300 NVL72:~176RTX PRO 6000:β | GB300 NVL72:~99RTX PRO 6000:β | GB300 NVL72:~42RTX PRO 6000:β |
Agentic inference metrics from the AgentX scenario and fixed-sequence inference metrics across models, hardware configurations, and serving parameters.
TCO $/chip/hr:
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.
Matching measurements exist, but their chip series are hidden.
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Qwen3.5 397B 8K / 1K Total Tokens per $1 TCO vs. Interactivity