GLM 5/5.1 — GB300 NVL72 vs MI355X
Head-to-head AI inference benchmark comparison of GB300 NVL72 (NVIDIA Blackwell) 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.
Setting 36 tok/s/user as the target on GLM 5/5.1, GB300 NVL72 produces 12331 tok/s/chip ($0.05 per million tokens) and MI355X produces 1218 ($0.34). GB300 NVL72 is 557% cheaper per token; GB300 NVL72 delivers 912% more tok/s/chip.
At 43 tok/s/user interactivity on GLM 5/5.1, GB300 NVL72 delivers 12059 tok/s/chip at $0.05 per million tokens; MI355X delivers 949 tok/s/chip at $0.44. GB300 NVL72 is 725% cheaper per token; GB300 NVL72 delivers 1171% more tok/s/chip at this point.
GB300 NVL72 posts 11271 tok/s/chip for $0.06 per million tokens at 50 tok/s/user on GLM 5/5.1; MI355X posts 670 tok/s/chip for $0.62. GB300 NVL72 is 992% cheaper per token; GB300 NVL72 delivers 1582% more tok/s/chip. (Numbers reflect the default 8k/1k · fp4 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | GB300 NVL72:12331.4MI355X:1218.0 | GB300 NVL72:12059.4MI355X:948.6 | GB300 NVL72:11270.6MI355X:670.1 |
| Cost ($/M tok) | GB300 NVL72:$0.052MI355X:$0.342 | GB300 NVL72:$0.053MI355X:$0.439 | GB300 NVL72:$0.057MI355X:$0.622 |
| tok/s/MW | GB300 NVL72:5816715MI355X:582775 | GB300 NVL72:5688417MI355X:453886 | GB300 NVL72:5316301MI355X:320643 |
| Concurrency | GB300 NVL72:~2253MI355X:~8 | GB300 NVL72:~2052MI355X:~8 | GB300 NVL72:~1492MI355X:~7 |
Total Tokens per $1 USD (Owning - Neocloud Giant) vs. Interactivity
GLM5/5.1 744B • FP4 • 8K / 1K • Source: SemiAnalysis InferenceX™
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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Interactivity is the rate at which a single user receives generated tokens while the model streams its answer — how quickly new words appear on screen. Higher values feel snappier; operators trade it against batch throughput.
How many total tokens (input + output) one US dollar of infrastructure spend buys, priced with the all-in hourly ownership cost of a Neocloud Giant operator. It is the reciprocal of cost per token, so higher means cheaper.
Formula: tok/$ = (total tok/s/chip × 3,600) ÷ all-in cost per chip-hour ($)
1 The ATOM engine is promising, however it has yet to serve production tokens. It is still in its infant stage.