Head-to-head AI inference benchmark comparison of MI300X (AMD CDNA 3) and TPU7x (Google Ironwood) 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 →
Throughput at 142 tok/s/user on Qwen 3.5 397B-A17B: MI300X hits 713 tok/s/chip, TPU7x hits 37856. Per-million costs land at $0.37 and $0.01 respectively. TPU7x is 4800% cheaper per token; TPU7x delivers 5212% more tok/s/chip.
MI300X / TPU7x on Qwen 3.5 397B-A17B at 281 tok/s/user: 713 / 26017 tok/s/chip, $0.37 / $0.01 per million tokens. TPU7x is 3267% cheaper per token; TPU7x delivers 3551% more tok/s/chip.
Toward the upper edge of the 4–558 tok/s/user interactivity band, at 420 tok/s/user on Qwen 3.5 397B-A17B: MI300X runs 713 tok/s/chip at $0.37/M tokens, TPU7x runs 18064 at $0.02/M. TPU7x is 2238% cheaper per token; TPU7x delivers 2435% more tok/s/chip. (Numbers reflect the default agentic-traces · fp8 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | MI300X:712.6TPU7x:37855.9 | MI300X:712.6TPU7x:26016.9 | MI300X:712.6TPU7x:18063.5 |
| Cost ($/M tok) | MI300X:$0.370TPU7x:$0.008 | MI300X:$0.370TPU7x:$0.011 | MI300X:$0.370TPU7x:$0.016 |
| tok/s/MW | MI300X:512683TPU7x:31363598 | MI300X:512683TPU7x:21555033 | MI300X:512683TPU7x:14965630 |
| Concurrency | MI300X:~4TPU7x:~26 | MI300X:~4TPU7x:~16 | MI300X:~4TPU7x:~11 |
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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Qwen3.5 397B 8K / 1K Total Tokens per $1 TCO vs. Interactivity