Head-to-head AI inference benchmark comparison of MI300X (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 β same interactions as the main inference chart.
Near the low end of the 14β51 tok/s/user interactivity band, at 23 tok/s/user on DeepSeek R1: MI300X runs 725 tok/s/chip at $0.36/M tokens, MI355X runs 3501 at $0.12/M. MI355X is 206% cheaper per token; MI355X delivers 383% more tok/s/chip.
Setting 32 tok/s/user as the target on DeepSeek R1, MI300X produces 601 tok/s/chip ($0.44 per million tokens) and MI355X produces 2739 ($0.15). MI355X is 189% cheaper per token; MI355X delivers 356% more tok/s/chip.
At 42 tok/s/user interactivity on DeepSeek R1, MI300X delivers 406 tok/s/chip at $0.65 per million tokens; MI355X delivers 2244 tok/s/chip at $0.19. MI355X is 250% cheaper per token; MI355X delivers 452% more tok/s/chip at this point. (Numbers reflect the default 8k/1k Β· fp8 selection for this URL β table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | MI300X:725.2MI355X:3500.6 | MI300X:601.1MI355X:2739.3 | MI300X:406.3MI355X:2244.0 |
| Cost ($/M tok) | MI300X:$0.364MI355X:$0.119 | MI300X:$0.439MI355X:$0.152 | MI300X:$0.650MI355X:$0.186 |
| tok/s/MW | MI300X:521723MI355X:1674946 | MI300X:432443MI355X:1310666 | MI300X:292288MI355X:1073689 |
| Concurrency | MI300X:~30MI355X:~2048 | MI300X:~18MI355X:~515 | MI300X:~10MI355X:~45 |
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