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GLM 5/5.1 · Chip comparison

GLM 5/5.1 — MI325X vs MI355X

Head-to-head AI inference benchmark comparison of MI325X (AMD CDNA 3) and MI355X (AMD CDNA 4) on GLM 5/5.1. 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 14 tok/s/user interactivity on GLM 5/5.1, MI325X delivers 491 tok/s/chip at $0.62 per million tokens; MI355X delivers 2040 tok/s/chip at $0.20. MI355X is 204% cheaper per token; MI355X delivers 315% more tok/s/chip at this point.

MI325X posts 309 tok/s/chip for $0.99 per million tokens at 19 tok/s/user on GLM 5/5.1; MI355X posts 1855 tok/s/chip for $0.22. MI355X is 340% cheaper per token; MI355X delivers 500% more tok/s/chip.

Throughput at 24 tok/s/user on GLM 5/5.1: MI325X hits 200 tok/s/chip, MI355X hits 1732. Per-million costs land at $1.53 and $0.24 respectively. MI355X is 537% cheaper per token; MI355X delivers 768% more tok/s/chip. (Numbers reflect the default 8k/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)
MI325X:491.3MI355X:2039.8
MI325X:309.0MI355X:1854.9
MI325X:199.6MI355X:1732.5
Cost ($/M tok)
MI325X:$0.622MI355X:$0.204
MI325X:$0.989MI355X:$0.225
MI325X:$1.531MI355X:$0.241
tok/s/MW
MI325X:290711MI355X:975990
MI325X:182855MI355X:887517
MI325X:118096MI355X:828946
Concurrency
MI325X:~34MI355X:~65
MI325X:~15MI355X:~57
MI325X:~8MI355X:~34

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

GLM5/5.1 744B • 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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1 The ATOM engine is promising, however it has yet to serve production tokens. It is still in its infant stage.