MiniMax M3 428B — B300 vs MI355X
Head-to-head AI inference benchmark comparison of B300 (NVIDIA Blackwell) and MI355X (AMD CDNA 4) 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 →
Near the low end of the 4–398 tok/s/user interactivity band, at 102 tok/s/user on MiniMax M3 428B: B300 runs 69247 tok/s/chip at $0.01/M tokens, MI355X runs 5025 at $0.08/M. B300 is 815% cheaper per token; B300 delivers 1278% more tok/s/chip.
Setting 201 tok/s/user as the target on MiniMax M3 428B, B300 produces 22544 tok/s/chip ($0.03 per million tokens) and MI355X produces 5025 ($0.08). B300 is 198% cheaper per token; B300 delivers 349% more tok/s/chip.
At 300 tok/s/user interactivity on MiniMax M3 428B, B300 delivers 10464 tok/s/chip at $0.06 per million tokens; MI355X delivers 5025 tok/s/chip at $0.08. B300 is 38% cheaper per token; B300 delivers 108% more tok/s/chip at this point. (Numbers reflect the default agentic-traces · fp4 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | B300:69246.7MI355X:5024.8 | B300:22544.4MI355X:5024.8 | B300:10463.6MI355X:5024.8 |
| Cost ($/M tok) | B300:$0.009MI355X:$0.083 | B300:$0.028MI355X:$0.083 | B300:$0.060MI355X:$0.083 |
| tok/s/MW | B300:36445629MI355X:2404233 | B300:11865455MI355X:2404233 | B300:5507176MI355X:2404233 |
| Concurrency | B300:~22MI355X:~2 | B300:~15MI355X:~2 | B300:~6MI355X:~2 |
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 ($)
1 The ATOM engine is promising, however it has yet to serve production tokens. It is still in its infant stage.