AMD Instinct MI355X GPU platform vs NVIDIA HGX B200 GPU platform: A Three-Level Inference Infrastructure Evaluation
Authors:
Russ Fellows
Cameron Moccari
July 21, 2026
When enterprises evaluate AI infrastructure, traditional approaches often focus primarily on raw performance metrics. While important, assessing hardware solely through maximum token throughput delivers an incomplete view of operational reality. Similarly, cost examined in isolation tells only part of the story. Neither alone provides the data organizations need to make informed infrastructure decisions, leaving a gap in understanding how infrastructure performs for real business objectives.
To bridge this gap, Signal65 conducted a three level evaluation of AMD Instinct MI355X GPU platform compared to NVIDIA HGX B200 GPU platform, evaluating three aspects:
- Raw Hardware Performance: Measured total token throughput (tokens per second) across each vendor’s optimized software inference stack.
- Operational Economics: The cost-efficiency of that performance calculated using current market GPU-hour hosting rates.
- Business Workload Impact: The translation of hardware capacity and economics into tangible enterprise outcomes, evaluating both batch processes and latency sensitive interactive workloads.
Key Takeaways:
The testing encompassed three production-class large language models: GPT-OSS-120B, Qwen3-Next-80B, and Kimi-K2.6. These models were evaluated across various input/output context sizes and request concurrency levels on standard 8-GPU nodes:
Key Findings:
- AMD MI355X delivered superior business outcomes compared to NVIDIA B200 across three distinct workload configurations — batch document processing, long-form generation, and latency-sensitive interactive serving — each mapping to a real application scenario.
- Financial Analysis: 1.3x more documents processed per hour, with 53% lower cost per document
- Long Report Generation: Trades ~5% throughput for 37% lower cost per page vs NVIDIA B200
- Interactive RAG Chat Application: serves the same number of users at 39.6% lower cost per hour and 42% lower p99 latency
- AMD MI355X achieved higher performance than NVIDIA B200 for 2 out of the 3 models tested:
- 1.3x greater throughput when running GPT-OSS-120B.
- 1.27x greater throughput than B200 when running Qwen3-Next-80B.
- NVIDIA B200 delivered 17% higher throughput than MI355X when running Kimi-K2.6, while MI355X leads on price-performance
- MI355X achieves more value per dollar:
- GPT-OSS-120B – 2.15x more tokens per dollar
- Kimi-K2.6 – 1.42x more tokens per dollar
- Qwen3-Next-80B: 2.11x more tokens per dollar
Ultimately, MI355X delivers consistent economic and workload advantages across all three models tested, including instances where NVIDIA outperforms on raw throughput. This demonstrates that infrastructure cost and workload efficiency are just as consequential as raw performance in determining real-world business value.
Research commissioned by:
