Head-to-head AI inference benchmark comparison of B300 (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.
Near the low end of the 10–28 tok/s/user interactivity band, at 14 tok/s/user on GLM 5/5.1: B300 runs 1658 tok/s/chip at $0.38/M tokens, MI325X runs 491 at $0.62/M. B300 is 64% cheaper per token; B300 delivers 238% more tok/s/chip.
Setting 19 tok/s/user as the target on GLM 5/5.1, B300 produces 1551 tok/s/chip ($0.40 per million tokens) and MI325X produces 309 ($0.99). B300 is 144% cheaper per token; B300 delivers 402% more tok/s/chip.
At 24 tok/s/user interactivity on GLM 5/5.1, B300 delivers 1458 tok/s/chip at $0.43 per million tokens; MI325X delivers 200 tok/s/chip at $1.53. B300 is 256% cheaper per token; B300 delivers 630% more tok/s/chip at this point. (Numbers reflect the default 8k/1k · fp8 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | B300:1658.3MI325X:491.3 | B300:1551.4MI325X:309.0 | B300:1457.8MI325X:199.6 |
| Cost ($/M tok) | B300:$0.379MI325X:$0.622 | B300:$0.405MI325X:$0.989 | B300:$0.431MI325X:$1.531 |
| tok/s/MW | B300:872815MI325X:290711 | B300:816521MI325X:182855 | B300:767274MI325X:118096 |
| Concurrency | B300:~107MI325X:~34 | B300:~75MI325X:~15 | B300:~56MI325X:~8 |
Agentic inference metrics from the AgentX scenario and fixed-sequence inference metrics across models, hardware configurations, and serving parameters.
TCO $/chip/hr: VR200: 3.61H100: 1.17H200: 1.22B200: 1.73B300: 2.26GB200: 1.86GB300: 2.31MI300X: 0.95MI325X: 1.1MI355X: 1.5RTX6000PRO: 0.68Jalapeño (Teacup): 1.47
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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GLM5/5.1 744B 8K / 1K Total Tokens per $1 TCO vs. Interactivity