MiniMax M3 428B — MI300X vs MI325X
Head-to-head AI inference benchmark comparison of MI300X (AMD CDNA 3) and MI325X (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 →
MI300X / MI325X on MiniMax M3 428B at 16 tok/s/user: 2565 / 3250 tok/s/chip, $0.10 / $0.09 per million tokens. MI325X is 9% cheaper per token; MI325X delivers 27% more tok/s/chip.
Around the middle of the 12–30 tok/s/user interactivity band, at 21 tok/s/user on MiniMax M3 428B: MI300X runs 1198 tok/s/chip at $0.22/M tokens, MI325X runs 1166 at $0.26/M. MI300X is 19% cheaper per token; MI300X delivers 3% more tok/s/chip.
Setting 25 tok/s/user as the target on MiniMax M3 428B, MI300X produces 1021 tok/s/chip ($0.26 per million tokens) and MI325X produces 1036 ($0.29). MI300X is 14% cheaper per token; MI325X delivers 1% more tok/s/chip. (Numbers reflect the default agentic-traces · fp8 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | MI300X:2564.8MI325X:3249.7 | MI300X:1198.5MI325X:1166.5 | MI300X:1021.4MI325X:1035.9 |
| Cost ($/M tok) | MI300X:$0.103MI325X:$0.094 | MI300X:$0.220MI325X:$0.262 | MI300X:$0.258MI325X:$0.295 |
| tok/s/MW | MI300X:1845170MI325X:1922918 | MI300X:862229MI325X:690230 | MI300X:734799MI325X:612986 |
| Concurrency | MI300X:~9MI325X:~13 | MI300X:~4MI325X:~4 | MI300X:~3MI325X:~3 |
Total Tokens per $1 USD (Owning - Neocloud Giant) vs. Interactivity
MiniMax M3 428B • FP4 • 8K / 1K • Source: SemiAnalysis InferenceX™
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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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 ($)