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GLM 5/5.1 · Chip comparison

GLM 5/5.1 — B300 vs GB200 NVL72

Head-to-head AI inference benchmark comparison of B300 (NVIDIA Blackwell) and GB200 NVL72 (NVIDIA Blackwell) 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 30–147 tok/s/user interactivity band, at 59 tok/s/user on GLM 5/5.1: B300 runs 2598 tok/s/chip at $0.24/M tokens, GB200 NVL72 runs 9553 at $0.05/M. GB200 NVL72 is 347% cheaper per token; GB200 NVL72 delivers 268% more tok/s/chip.

Setting 88 tok/s/user as the target on GLM 5/5.1, B300 produces 1755 tok/s/chip ($0.36 per million tokens) and GB200 NVL72 produces 6203 ($0.08). GB200 NVL72 is 330% cheaper per token; GB200 NVL72 delivers 253% more tok/s/chip.

At 118 tok/s/user interactivity on GLM 5/5.1, B300 delivers 1278 tok/s/chip at $0.49 per million tokens; GB200 NVL72 delivers 1574 tok/s/chip at $0.33. GB200 NVL72 is 50% cheaper per token; GB200 NVL72 delivers 23% more tok/s/chip at this point. (Numbers reflect the default 8k/1k · fp4 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)
B300:2597.9GB200 NVL72:9552.7
B300:1754.8GB200 NVL72:6202.9
B300:1277.6GB200 NVL72:1574.4
Cost ($/M tok)
B300:$0.242GB200 NVL72:$0.054
B300:$0.358GB200 NVL72:$0.083
B300:$0.491GB200 NVL72:$0.328
tok/s/MW
B300:1367336GB200 NVL72:5108387
B300:923579GB200 NVL72:3317076
B300:672435GB200 NVL72:841925
Concurrency
B300:~20GB200 NVL72:~916
B300:~9GB200 NVL72:~661
B300:~5GB200 NVL72:~75

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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