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DeepSeekv4 Pro 0813 1.6T · Chip comparison

DeepSeekv4 Pro 0813 1.6T — MI300X vs MI325X

Head-to-head AI inference benchmark comparison of MI300X (AMD CDNA 3) and MI325X (AMD CDNA 3) on DeepSeekv4 Pro 0813 1.6T. 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.

At 12 tok/s/user interactivity on DeepSeekv4 Pro 0813 1.6T, MI300X delivers 292 tok/s/chip at $0.90 per million tokens; MI325X delivers 387 tok/s/chip at $0.79. MI325X is 14% cheaper per token; MI325X delivers 32% more tok/s/chip at this point.

MI300X posts 130 tok/s/chip for $2.03 per million tokens at 19 tok/s/user on DeepSeekv4 Pro 0813 1.6T; MI325X posts 207 tok/s/chip for $1.47. MI325X is 38% cheaper per token; MI325X delivers 59% more tok/s/chip.

Throughput at 25 tok/s/user on DeepSeekv4 Pro 0813 1.6T: MI300X hits 88 tok/s/chip, MI325X hits 111. Per-million costs land at $2.99 and $2.74 respectively. MI325X is 9% cheaper per token; MI325X delivers 26% more tok/s/chip. (Numbers reflect the default 1k/1k · fp8 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)

View performance-per-dollar view →

Interpolated from real benchmark data. Edit target interactivity values below to compare at different operating points.
Metric
Interactivity (tok/s/user)
Interactivity (tok/s/user)
Interactivity (tok/s/user)
Throughput (tok/s/chip)
MI300X:292.2MI325X:386.8
MI300X:130.0MI325X:207.3
MI300X:88.2MI325X:111.4
Cost ($/M tok)
MI300X:$0.903MI325X:$0.790
MI300X:$2.030MI325X:$1.474
MI300X:$2.992MI325X:$2.743
tok/s/MW
MI300X:210222MI325X:228896
MI300X:93512MI325X:122634
MI300X:63442MI325X:65918
Concurrency
MI300X:~101MI325X:~136
MI300X:~29MI325X:~46
MI300X:~15MI325X:~18

Inference Performance

Agentic inference metrics from the AgentX scenario and fixed-sequence inference metrics across models, hardware configurations, and serving parameters.

Total Tokens per $1 USD (Owning - Neocloud Giant) vs. Interactivity

DeepSeek V4 Pro 0813 1.6T • FP4 • 8K / 1K • Source: SemiAnalysis InferenceX™

TCO $/chip/hr: VR200: 3.61H100: 1.55H200: 1.59B200: 2.07B300: 2.52GB200: 2.26GB300: 2.79MI300X: 1.16MI325X: 1.32MI355X: 2.09RTX6000PRO: 0.75Jalapeño (Teacup): 1.56

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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