Head-to-head AI inference benchmark comparison of MI300X (AMD CDNA 3) and MI325X (AMD CDNA 3) on Qwen 3.5 397B-A17B. 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 →
At 5 tok/s/user interactivity on Qwen 3.5 397B-A17B, MI300X delivers 3331 tok/s/chip at $0.08 per million tokens; MI325X delivers 15612 tok/s/chip at $0.02. MI325X is 305% cheaper per token; MI325X delivers 369% more tok/s/chip at this point.
MI300X posts 3331 tok/s/chip for $0.08 per million tokens at 7 tok/s/user on Qwen 3.5 397B-A17B; MI325X posts 15612 tok/s/chip for $0.02. MI325X is 305% cheaper per token; MI325X delivers 369% more tok/s/chip.
Throughput at 9 tok/s/user on Qwen 3.5 397B-A17B: MI300X hits 3270 tok/s/chip, MI325X hits 15612. Per-million costs land at $0.08 and $0.02 respectively. MI325X is 312% cheaper per token; MI325X delivers 377% more tok/s/chip. (Numbers reflect the default agentic-traces · fp8 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | MI300X:3330.7MI325X:15612.1 | MI300X:3330.7MI325X:15612.1 | MI300X:3270.0MI325X:15612.1 |
| Cost ($/M tok) | MI300X:$0.079MI325X:$0.020 | MI300X:$0.079MI325X:$0.020 | MI300X:$0.081MI325X:$0.020 |
| tok/s/MW | MI300X:2396173MI325X:9237942 | MI300X:2396173MI325X:9237942 | MI300X:2352546MI325X:9237942 |
| Concurrency | MI300X:~24MI325X:~40 | MI300X:~24MI325X:~40 | MI300X:~23MI325X:~40 |
Agentic inference metrics from the AgentX scenario and fixed-sequence inference metrics across models, hardware configurations, and serving parameters.
TCO $/chip/hr: VR200: 3.61H100: 1.17H200: 1.22B200: 1.73B300: 2.26GB200: 1.86GB300: 2.31MI300X: 0.95MI325X: 1.1MI355X: 1.5RTX6000PRO: 0.68Jalapeño (Teacup): 1.47
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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Qwen3.5 397B 8K / 1K Total Tokens per $1 TCO vs. Interactivity