GLM 5.3 744B — H200 vs MI325X Performance per Dollar
Cost per million tokens of H200 (NVIDIA Hopper) versus MI325X (AMD CDNA 3) on GLM 5.3 744B. Owning-hyperscaler TCO normalized by output 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 →
On GLM 5.3 744B at 22 tok/s/user, the per-million math comes out to $0.41 for H200 and $0.40 for MI325X; MI325X delivers 3% more output per dollar.
At 43 tok/s/user on GLM 5.3 744B, H200 costs $0.41 per million tokens; MI325X costs $0.44. H200 is 7% more cost-efficient at this operating point.
H200 edges MI325X at 63 tok/s/user on GLM 5.3 744B — $0.41 per million tokens versus $0.44, a 7% cost-per-token gap. (Numbers reflect the default agentic-traces · fp8 selection for this URL — table and chart below update if you change sequence, precision, or model in the controls.)
Chip pricing (owning hyperscaler): H200 $1.22/chip/hr · MI325X $1.10/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 | H200:$0.414MI325X:$0.400 | H200:$0.414MI325X:$0.444 | H200:$0.414MI325X:$0.444 |
| Concurrency | H200:~12MI325X:~2 | H200:~12MI325X:~1 | H200:~12MI325X:~1 |
Inference Performance
Agentic inference metrics from the AgentX scenario and fixed-sequence inference metrics across models, hardware configurations, and serving parameters.