Head-to-head AI inference benchmark comparison of H200 (NVIDIA Hopper) 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.
Near the low end of the 18–84 tok/s/user interactivity band, at 34 tok/s/user on GLM 5/5.1: H200 runs 740 tok/s/chip at $0.46/M tokens, MI355X runs 1276 at $0.33/M. MI355X is 40% cheaper per token; MI355X delivers 72% more tok/s/chip.
Setting 51 tok/s/user as the target on GLM 5/5.1, H200 produces 563 tok/s/chip ($0.60 per million tokens) and MI355X produces 957 ($0.44). MI355X is 38% cheaper per token; MI355X delivers 70% more tok/s/chip.
At 68 tok/s/user interactivity on GLM 5/5.1, H200 delivers 434 tok/s/chip at $0.78 per million tokens; MI355X delivers 747 tok/s/chip at $0.56. MI355X is 40% cheaper per token; MI355X delivers 72% more tok/s/chip at this point. (Numbers reflect the default 8k/1k · fp8 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Throughput (tok/s/chip) | H200:740.0MI355X:1276.0 | H200:563.3MI355X:956.5 | H200:433.9MI355X:746.7 |
| Cost ($/M tok) | H200:$0.458MI355X:$0.327 | H200:$0.602MI355X:$0.436 | H200:$0.781MI355X:$0.558 |
| tok/s/MW | H200:540170MI355X:610522 | H200:411175MI355X:457666 | H200:316738MI355X:357251 |
| Concurrency | H200:~21MI355X:~18 | H200:~10MI355X:~9 | H200:~6MI355X:~5 |
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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1 The ATOM engine is promising, however it has yet to serve production tokens. It is still in its infant stage.
GLM5/5.1 744B 8K / 1K Total Tokens per $1 TCO vs. Interactivity