Head-to-head AI inference benchmark comparison of GB300 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 β 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 β
Near the low end of the 84β273 tok/s/user interactivity band, at 131 tok/s/user on GLM 5.3 744B: GB300 NVL72 runs 17084 tok/s/chip at $0.04/M tokens, MI355X runs 10013 at $0.04/M. GB300 NVL72 is 11% cheaper per token; GB300 NVL72 delivers 71% more tok/s/chip.
Setting 178 tok/s/user as the target on GLM 5.3 744B, GB300 NVL72 produces 9392 tok/s/chip ($0.07 per million tokens) and MI355X produces 5491 ($0.08). GB300 NVL72 is 11% cheaper per token; GB300 NVL72 delivers 71% more tok/s/chip.
At 226 tok/s/user interactivity on GLM 5.3 744B, GB300 NVL72 delivers 5472 tok/s/chip at $0.12 per million tokens; MI355X delivers 3913 tok/s/chip at $0.11. MI355X is 10% cheaper per token; GB300 NVL72 delivers 40% more tok/s/chip at this point. (Numbers reflect the default agentic-traces Β· fp4 selection for this URL β table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | GB300 NVL72:17084.2MI355X:10013.4 | GB300 NVL72:9392.1MI355X:5491.2 | GB300 NVL72:5472.2MI355X:3913.4 |
| Cost ($/M tok) | GB300 NVL72:$0.038MI355X:$0.042 | GB300 NVL72:$0.068MI355X:$0.076 | GB300 NVL72:$0.117MI355X:$0.106 |
| tok/s/MW | GB300 NVL72:8058599MI355X:4791079 | GB300 NVL72:4430250MI355X:2627356 | GB300 NVL72:2581216MI355X:1872463 |
| Concurrency | GB300 NVL72:~48MI355X:~6 | GB300 NVL72:~47MI355X:~3 | GB300 NVL72:~19MI355X:~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.
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GLM5.2/GLM5.3 744B 8K / 1K Total Tokens per $1 TCO vs. Interactivity