GLM 5/5.1 — B200 vs MI325X
Head-to-head AI inference benchmark comparison of B200 (NVIDIA Blackwell) and MI325X (AMD CDNA 3) 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.
Setting 16 tok/s/user as the target on GLM 5/5.1, B200 produces 3682 tok/s/chip ($0.13 per million tokens) and MI325X produces 413 ($0.74). B200 is 466% cheaper per token; B200 delivers 791% more tok/s/chip.
At 20 tok/s/user interactivity on GLM 5/5.1, B200 delivers 3682 tok/s/chip at $0.13 per million tokens; MI325X delivers 282 tok/s/chip at $1.08. B200 is 730% cheaper per token; B200 delivers 1205% more tok/s/chip at this point.
B200 posts 2822 tok/s/chip for $0.17 per million tokens at 25 tok/s/user on GLM 5/5.1; MI325X posts 180 tok/s/chip for $1.70. B200 is 899% cheaper per token; B200 delivers 1471% more tok/s/chip. (Numbers reflect the default 8k/1k · fp8 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | B200:3682.2MI325X:413.4 | B200:3682.2MI325X:282.1 | B200:2821.5MI325X:179.6 |
| Cost ($/M tok) | B200:$0.131MI325X:$0.739 | B200:$0.131MI325X:$1.083 | B200:$0.170MI325X:$1.701 |
| tok/s/MW | B200:2153308MI325X:244630 | B200:2153308MI325X:166947 | B200:1650028MI325X:106283 |
| Concurrency | B200:~560MI325X:~24 | B200:~560MI325X:~13 | B200:~250MI325X:~7 |
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 - Hyperscaler) vs. Interactivity
GLM5/5.1 744B • FP4 • 8K / 1K • Source: SemiAnalysis InferenceX™
TCO $/chip/hr: H100: 1.17H200: 1.22B200: 1.73B300: 2.26GB200: 1.86GB300: 2.31MI300X: 0.95MI325X: 1.1MI355X: 1.5RTX6000PRO: 0.68
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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