Head-to-head AI inference benchmark comparison of GB300 NVL72 (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 100–336 tok/s/user interactivity band, at 159 tok/s/user on MiniMax M3 428B: GB300 NVL72 runs 32568 tok/s/chip at $0.02/M tokens, MI355X runs 41218 at $0.01/M. MI355X is 95% cheaper per token; MI355X delivers 27% more tok/s/chip.
Setting 218 tok/s/user as the target on MiniMax M3 428B, GB300 NVL72 produces 21832 tok/s/chip ($0.03 per million tokens) and MI355X produces 23481 ($0.02). MI355X is 66% cheaper per token; MI355X delivers 8% more tok/s/chip.
At 277 tok/s/user interactivity on MiniMax M3 428B, GB300 NVL72 delivers 13103 tok/s/chip at $0.05 per million tokens; MI355X delivers 17613 tok/s/chip at $0.02. MI355X is 107% cheaper per token; MI355X delivers 34% more tok/s/chip at this point. (Numbers reflect the default agentic-traces · fp4 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | GB300 NVL72:32567.6MI355X:41217.7 | GB300 NVL72:21832.5MI355X:23480.8 | GB300 NVL72:13103.4MI355X:17612.7 |
| Cost ($/M tok) | GB300 NVL72:$0.020MI355X:$0.010 | GB300 NVL72:$0.029MI355X:$0.018 | GB300 NVL72:$0.049MI355X:$0.024 |
| tok/s/MW | GB300 NVL72:15362054MI355X:19721403 | GB300 NVL72:10298334MI355X:11234854 | GB300 NVL72:6180856MI355X:8427111 |
| Concurrency | GB300 NVL72:~29MI355X:~22 | GB300 NVL72:~20MI355X:~15 | GB300 NVL72:~24MI355X:~10 |
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.
Shift+Scroll to zoom • Drag to pan • Double-click to reset • Click a point to pin tooltip
1 The ATOM engine is promising, however it has yet to serve production tokens. It is still in its infant stage.
MiniMax M3 428B 8K / 1K Total Tokens per $1 TCO vs. Interactivity