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MiniMax M3 428B · Chip comparison

MiniMax M3 428B — H100 vs MI300X

Head-to-head AI inference benchmark comparison of H100 (NVIDIA Hopper) and MI300X (AMD CDNA 3) on MiniMax M3 428B. 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 →

At 13 tok/s/user interactivity on MiniMax M3 428B, H100 delivers 667 tok/s/chip at $0.49 per million tokens; MI300X delivers 2691 tok/s/chip at $0.10. MI300X is 397% cheaper per token; MI300X delivers 303% more tok/s/chip at this point.

H100 posts 650 tok/s/chip for $0.50 per million tokens at 14 tok/s/user on MiniMax M3 428B; MI300X posts 2662 tok/s/chip for $0.10. MI300X is 405% cheaper per token; MI300X delivers 310% more tok/s/chip.

Throughput at 16 tok/s/user on MiniMax M3 428B: H100 hits 603 tok/s/chip, MI300X hits 2565. Per-million costs land at $0.54 and $0.10 respectively. MI300X is 424% cheaper per token; MI300X delivers 325% more tok/s/chip. (Numbers reflect the default agentic-traces · 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)
H100:667.4MI300X:2691.5
H100:649.7MI300X:2661.8
H100:603.1MI300X:2564.8
Cost ($/M tok)
H100:$0.487MI300X:$0.098
H100:$0.500MI300X:$0.099
H100:$0.539MI300X:$0.103
tok/s/MW
H100:487170MI300X:1936319
H100:474220MI300X:1914951
H100:440225MI300X:1845170
Concurrency
H100:~2MI300X:~10
H100:~2MI300X:~9
H100:~1MI300X:~9

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

MiniMax M3 428B • 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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