Head-to-head AI inference benchmark comparison of GB200 NVL72 (NVIDIA Blackwell) and MI355X (AMD CDNA 4) on GLM 5.3 744B. 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 →
GB200 NVL72 posts 13440 tok/s/chip for $0.04 per million tokens at 130 tok/s/user on GLM 5.3 744B; MI355X posts 9605 tok/s/chip for $0.04. GB200 NVL72 is 13% cheaper per token; GB200 NVL72 delivers 40% more tok/s/chip.
Throughput at 168 tok/s/user on GLM 5.3 744B: GB200 NVL72 hits 10544 tok/s/chip, MI355X hits 6886. Per-million costs land at $0.05 and $0.06 respectively. GB200 NVL72 is 23% cheaper per token; GB200 NVL72 delivers 53% more tok/s/chip.
GB200 NVL72 / MI355X on GLM 5.3 744B at 206 tok/s/user: 7128 / 3293 tok/s/chip, $0.07 / $0.13 per million tokens. GB200 NVL72 is 75% cheaper per token; GB200 NVL72 delivers 116% more tok/s/chip. (Numbers reflect the default agentic-traces · fp4 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | GB200 NVL72:13440.1MI355X:9604.7 | GB200 NVL72:10544.0MI355X:6886.1 | GB200 NVL72:7128.4MI355X:3293.4 |
| Cost ($/M tok) | GB200 NVL72:$0.038MI355X:$0.043 | GB200 NVL72:$0.049MI355X:$0.061 | GB200 NVL72:$0.072MI355X:$0.127 |
| tok/s/MW | GB200 NVL72:7187207MI355X:4595559 | GB200 NVL72:5638526MI355X:3294768 | GB200 NVL72:3812000MI355X:1575792 |
| Concurrency | GB200 NVL72:~77MI355X:~6 | GB200 NVL72:~48MI355X:~4 | GB200 NVL72:~8MI355X:~4 |
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.
No measurements to plot for this selection. Review the benchmark controls above or adjust quick filters.
Shift+Scroll to zoom • Drag to pan • Double-click to reset • Click a point to pin tooltip
GLM5.2/GLM5.3 744B 8K / 1K Total Tokens per $1 TCO vs. Interactivity