Head-to-head AI inference benchmark comparison of MI355X (AMD CDNA 4) and Vera Rubin NVL72 (NVIDIA Vera Rubin) on DeepSeekv4 Pro 0813 1.6T. 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 →
Setting 110 tok/s/user as the target on DeepSeekv4 Pro 0813 1.6T, MI355X produces 19815 tok/s/chip ($0.02 per million tokens) and Vera Rubin NVL72 produces 273427 ($0.00). Vera Rubin NVL72 is 473% cheaper per token; Vera Rubin NVL72 delivers 1280% more tok/s/chip.
At 147 tok/s/user interactivity on DeepSeekv4 Pro 0813 1.6T, MI355X delivers 9740 tok/s/chip at $0.04 per million tokens; Vera Rubin NVL72 delivers 231839 tok/s/chip at $0.00. Vera Rubin NVL72 is 889% cheaper per token; Vera Rubin NVL72 delivers 2280% more tok/s/chip at this point.
MI355X posts 3979 tok/s/chip for $0.10 per million tokens at 183 tok/s/user on DeepSeekv4 Pro 0813 1.6T; Vera Rubin NVL72 posts 147885 tok/s/chip for $0.01. Vera Rubin NVL72 is 1444% cheaper per token; Vera Rubin NVL72 delivers 3617% more tok/s/chip. (Numbers reflect the default agentic-traces · fp4 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | MI355X:19815.1Vera Rubin NVL72:273426.8 | MI355X:9739.6Vera Rubin NVL72:231838.9 | MI355X:3978.5Vera Rubin NVL72:147884.7 |
| Cost ($/M tok) | MI355X:$0.021Vera Rubin NVL72:$0.004 | MI355X:$0.043Vera Rubin NVL72:$0.004 | MI355X:$0.105Vera Rubin NVL72:$0.007 |
| tok/s/MW | MI355X:9480899Vera Rubin NVL72:82856601 | MI355X:4660118Vera Rubin NVL72:70254219 | MI355X:1903612Vera Rubin NVL72:44813555 |
| Concurrency | MI355X:~44Vera Rubin NVL72:~2904 | MI355X:~19Vera Rubin NVL72:~2480 | MI355X:~5Vera Rubin NVL72:~1370 |
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.
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1 The ATOM engine is promising, however it has yet to serve production tokens. It is still in its infant stage.
DeepSeek V4 Pro 0813 1.6T 8K / 1K Total Tokens per $1 TCO vs. Interactivity