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MiniMax M3 428B Β· Chip comparison

MiniMax M3 428B β€” GB200 NVL72 vs Vera Rubin NVL72

Head-to-head AI inference benchmark comparison of GB200 NVL72 (NVIDIA Blackwell) and Vera Rubin NVL72 (NVIDIA Vera Rubin) on MiniMax M3 428B. 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†’

GB200 NVL72 posts 19558 tok/s/chip for $0.03 per million tokens at 173 tok/s/user on MiniMax M3 428B; Vera Rubin NVL72 posts 104132 tok/s/chip for $0.01. Vera Rubin NVL72 is 174% cheaper per token; Vera Rubin NVL72 delivers 432% more tok/s/chip.

Throughput at 260 tok/s/user on MiniMax M3 428B: GB200 NVL72 hits 13288 tok/s/chip, Vera Rubin NVL72 hits 66780. Per-million costs land at $0.04 and $0.02 respectively. Vera Rubin NVL72 is 159% cheaper per token; Vera Rubin NVL72 delivers 403% more tok/s/chip.

GB200 NVL72 / Vera Rubin NVL72 on MiniMax M3 428B at 346 tok/s/user: 7297 / 43256 tok/s/chip, $0.07 / $0.02 per million tokens. Vera Rubin NVL72 is 205% cheaper per token; Vera Rubin NVL72 delivers 493% more tok/s/chip. (Numbers reflect the default agentic-traces Β· fp4 selection for this URL β€” table and chart below update if you change sequence, precision, or model in the controls.)

View performance-per-dollar view β†’

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 NVL72:19557.6Vera Rubin NVL72:104132.1
GB200 NVL72:13287.6Vera Rubin NVL72:66780.0
GB200 NVL72:7297.2Vera Rubin NVL72:43255.7
Cost ($/M tok)
GB200 NVL72:$0.026Vera Rubin NVL72:$0.010
GB200 NVL72:$0.039Vera Rubin NVL72:$0.015
GB200 NVL72:$0.071Vera Rubin NVL72:$0.023
tok/s/MW
GB200 NVL72:10458588Vera Rubin NVL72:31555182
GB200 NVL72:7105683Vera Rubin NVL72:20236353
GB200 NVL72:3902258Vera Rubin NVL72:13107773
Concurrency
GB200 NVL72:~13Vera Rubin NVL72:~34
GB200 NVL72:~6Vera Rubin NVL72:~20
GB200 NVL72:~1Vera Rubin NVL72:~33

Inference Performance

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

Benchmark Config
8K / 1K (deprecated)
Chart Config
Interactivity
Compare history

MiniMax M3 428B 8K / 1K Total Tokens per $1 TCO vs. Interactivity

Cost Tier:
Owning at Large Hyperscaler Volume
Source:
SemiAnalysis InferenceXβ„’

TCO $/chip/hr: 3.61 1.17 1.22 1.73 2.26 1.86 2.31 0.95 1.1 1.5 0.68 1.27 1.03

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.

No data available

No measurements to plot for this selection. Review the benchmark controls above or adjust quick filters.

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