Benchmarking Confidential GPU Inference on NVIDIA H100 under Intel TDX
However, the performance cost of enabling confidential execution for GPU-accelerated large language model serving remains workload dependent and operationally important. This paper presents a benchmark study comparing standard non-confidential execution with confidential computing mode on a single NVIDIA H100 80GB GPU hosted in an Intel TDX confidential instance. The evaluation uses two representative language models, Mistral-7B v0.1 and Qwen3-30B-A3B, and measures time to first token, end-to-end request latency, per-request token generation throughput, global token throughput, and closed-loop request throughput under increasing concurrency.
- ▪However, the performance cost of enabling confidential execution for GPU-accelerated large language model serving remains workload dependent and operationally important.
- ▪This paper presents a benchmark study comparing standard non-confidential execution with confidential computing mode on a single NVIDIA H100 80GB GPU hosted in an Intel TDX confidential instance.
- ▪The evaluation uses two representative language models, Mistral-7B v0.1 and Qwen3-30B-A3B, and measures time to first token, end-to-end request latency, per-request token generation throughput, global token throughput, and closed-loop reque
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2607.19353 |
| Publication time | Thu, 23 Jul 2026 00:00:00 -0400 |
| Retrieval time | 2026-07-23T04:57:25.932Z |
| Last seen | 2026-07-23T08:18:15.996Z |
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Computer Science > Artificial Intelligence arXiv:2607.19353 (cs) [Submitted on 20 May 2026] Title:Benchmarking Confidential GPU Inference on NVIDIA H100 under Intel TDX Authors:Wei Wang, Abdul Hyee Waqas, Burns Smith View a PDF of the paper titled Benchmarking Confidential GPU Inference on NVIDIA H100 under Intel TDX, by Wei Wang and 2 other authors View PDF HTML (experimental) Abstract:Confidential computing is becoming a practical deployment requirement for AI inference workloads that process sensitive inputs or protect proprietary model assets. However, the performance cost of enabling confidential execution for GPU-accelerated large language model serving remains workload dependent and operationally important.
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