Muse Spark Open 816B — H200 vs MI355X Performance per Dollar
Cost per million tokens of H200 (NVIDIA Hopper) versus MI355X (AMD CDNA 4) on Muse Spark Open 816B. 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 edges H200 at 68 tok/s/user on Muse Spark Open 816B — $0.05 per million tokens versus $0.06, a 29% cost-per-token gap.
Push Muse Spark Open 816B to 107 tok/s/user and H200 lands at $0.08 per million tokens against MI355X's $0.08 — MI355X pulls ahead by 4%.
H200: $0.12 per million tokens. MI355X: $0.15. Both at 145 tok/s/user on Muse Spark Open 816B, with H200 21% cheaper. (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): H200 $1.22/chip/hr · MI355X $1.50/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:$0.060MI355X:$0.046 | H200:$0.080MI355X:$0.077 | H200:$0.124MI355X:$0.150 |
| Concurrency | H200:~12MI355X:~15 | H200:~7MI355X:~10 | H200:~5MI355X:~1 |
Inference Performance
Agentic inference metrics from the AgentX scenario and fixed-sequence inference metrics across models, hardware configurations, and serving parameters.
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