GLM 5.3 744B — MI355X vs Vera Rubin NVL72 Performance per Dollar
Cost per million tokens of MI355X (AMD CDNA 4) versus Vera Rubin NVL72 (NVIDIA Vera Rubin) on GLM 5.3 744B. Large-hyperscaler-volume ownership TCO normalized by total tokens — performance per dollar across LLM workloads. Pick the more cost-efficient SKU at every target interactivity level. Use the chart controls below to switch sequences, precisions, and metrics — same interactions as the main inference chart.
AgentX replays real coding-agent sessions rather than fixed-length prompts, so context grows turn over turn and most of each request is served from cache instead of being recomputed. That turns the comparison into a systems question: KV transfer between nodes, prefix-aware routing, and cache capacity all move the curve alongside raw chip throughput. Fixed-sequence workloads stay the clean baseline for kernel and silicon performance, so the two scenarios answer different questions about the same hardware. Learn more about AgentX →
MI355X: $0.05 per million tokens. Vera Rubin NVL72: $0.01. Both at 151 tok/s/user on GLM 5.3 744B, with Vera Rubin NVL72 379% cheaper.
Around the middle of the 112–271 tok/s/user interactivity band — at 191 tok/s/user — MI355X runs $0.09 per million tokens on GLM 5.3 744B while Vera Rubin NVL72 runs $0.02. Vera Rubin NVL72 is the cheaper choice by 480%.
On GLM 5.3 744B at 231 tok/s/user, the per-million math comes out to $0.10 for MI355X and $0.02 for Vera Rubin NVL72; Vera Rubin NVL72 delivers 328% more total tokens per dollar. (Numbers reflect the default agentic-traces · fp4 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
Chip pricing (owning hyperscaler): MI355X $1.50/chip/hr · Vera Rubin NVL72 $3.61/chip/hr. Source: SemiAnalysis Market July 2026 Pricing Surveys & AI Cloud TCO Model.
| Metric | Interactivity (tok/s/user) | Interactivity (tok/s/user) | Interactivity (tok/s/user) |
|---|---|---|---|
| Dollar per Million Tokens | MI355X:$0.054Vera Rubin NVL72:$0.011 | MI355X:$0.093Vera Rubin NVL72:$0.016 | MI355X:$0.103Vera Rubin NVL72:$0.024 |
| Concurrency | MI355X:~4Vera Rubin NVL72:~318 | MI355X:~2Vera Rubin NVL72:~199 | MI355X:~4Vera Rubin NVL72:~108 |
Inference Performance
Agentic inference metrics from the AgentX scenario and fixed-sequence inference metrics across models, hardware configurations, and serving parameters.
GLM5.2/GLM5.3 744B 8K / 1K Cost per Million Total Tokens vs. Median Interactivity
GLM5.2/GLM5.3 744B 8K / 1K Cost per Million Total Tokens vs. Median Interactivity- Cost Tier:
- Source:
- SemiAnalysis InferenceX™
TCO $/chip/hr:
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.
No data available
No measurements to plot for this selection. Review the benchmark controls above or adjust quick filters.
Shift+Scroll to zoom • Drag to pan • Double-click to reset • Click a point to pin tooltip