Head-to-head AI inference benchmark comparison of MI325X (AMD CDNA 3) and MI355X (AMD CDNA 4) on DeepSeek R1. 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.
Setting 25 tok/s/user as the target on DeepSeek R1, MI325X produces 863 tok/s/chip ($0.35 per million tokens) and MI355X produces 3470 ($0.12). MI355X is 195% cheaper per token; MI355X delivers 302% more tok/s/chip.
At 34 tok/s/user interactivity on DeepSeek R1, MI325X delivers 706 tok/s/chip at $0.43 per million tokens; MI355X delivers 2498 tok/s/chip at $0.17. MI355X is 159% cheaper per token; MI355X delivers 254% more tok/s/chip at this point.
MI325X posts 474 tok/s/chip for $0.65 per million tokens at 44 tok/s/user on DeepSeek R1; MI355X posts 2206 tok/s/chip for $0.19. MI355X is 241% cheaper per token; MI355X delivers 366% more tok/s/chip. (Numbers reflect the default 8k/1k · fp8 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | MI325X:863.3MI355X:3470.1 | MI325X:706.1MI355X:2498.1 | MI325X:473.7MI355X:2205.6 |
| Cost ($/M tok) | MI325X:$0.354MI355X:$0.120 | MI325X:$0.433MI355X:$0.167 | MI325X:$0.645MI355X:$0.189 |
| tok/s/MW | MI325X:510837MI355X:1660324 | MI325X:417783MI355X:1195257 | MI325X:280302MI355X:1055316 |
| Concurrency | MI325X:~33MI355X:~2048 | MI325X:~20MI355X:~64 | MI325X:~11MI355X:~41 |
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 R1 0528 671B 8K / 1K Total Tokens per $1 TCO vs. Interactivity