Head-to-head AI inference benchmark comparison of MI325X (AMD CDNA 3) and MI355X (AMD CDNA 4) on gpt-oss 120B. 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.
At 55 tok/s/user interactivity on gpt-oss 120B, MI325X delivers 4487 tok/s/chip at $0.07 per million tokens; MI355X delivers 39326 tok/s/chip at $0.01. MI355X is 543% cheaper per token; MI355X delivers 776% more tok/s/chip at this point.
MI325X posts 3543 tok/s/chip for $0.09 per million tokens at 72 tok/s/user on gpt-oss 120B; MI355X posts 33155 tok/s/chip for $0.01. MI355X is 586% cheaper per token; MI355X delivers 836% more tok/s/chip.
Throughput at 90 tok/s/user on gpt-oss 120B: MI325X hits 1362 tok/s/chip, MI355X hits 27009. Per-million costs land at $0.22 and $0.02 respectively. MI355X is 1354% cheaper per token; MI355X delivers 1883% more tok/s/chip. (Numbers reflect the default 8k/1k Β· fp4 selection for this URL β table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | MI325X:4487.4MI355X:39325.7 | MI325X:3543.1MI355X:33154.6 | MI325X:1362.1MI355X:27008.6 |
| Cost ($/M tok) | MI325X:$0.068MI355X:$0.011 | MI325X:$0.086MI355X:$0.013 | MI325X:$0.224MI355X:$0.015 |
| tok/s/MW | MI325X:2655284MI355X:18816130 | MI325X:2096534MI355X:15863453 | MI325X:805963MI355X:12922781 |
| Concurrency | MI325X:~9MI355X:~85 | MI325X:~8MI355X:~52 | MI325X:~15MI355X:~35 |
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.
gpt-oss 120B 8K / 1K Total Tokens per $1 TCO vs. Interactivity