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Confidential GPU Inference Benchmarked on NVIDIA H100

Wei Wang, Abdul Hyee Waqas, Burns Smith· July 23, 2026 View original

Summary

This study benchmarks the performance cost of confidential computing for GPU-accelerated LLM inference on an NVIDIA H100 under Intel TDX. It reveals that confidential mode increases latency and reduces throughput for models like Mistral-7B and Qwen3-30B-A3B, with larger models saturating earlier.

Confidential computing is becoming a crucial requirement for AI inference workloads that handle sensitive data or proprietary models. This paper presents a benchmark study to quantify the performance overhead associated with enabling confidential execution for GPU-accelerated large language model (LLM) serving. The evaluation was conducted on a single NVIDIA H100 80GB GPU, hosted within an Intel TDX confidential instance. Researchers compared standard non-confidential execution with confidential computing mode using two representative LLMs: Mistral-7B v0.1 and Qwen3-30B-A3B. Various performance metrics were measured, including time to first token, end-to-end latency, token generation throughput, and closed-loop request throughput under increasing concurrency. The findings indicate that confidential mode introduces a measurable performance penalty. For instance, average time to first token increased by 21.8% for Mistral-7B and 27.8% for Qwen3-30B-A3B, while global token throughput dropped by 17.7% and 21.1% respectively in fixed request-rate experiments. Although confidential GPU inference retains usable throughput, capacity planning must account for both the throughput penalty and the earlier saturation observed for larger models.

Why it matters

Professionals deploying LLMs with sensitive data need to understand the performance implications of confidential computing to accurately plan infrastructure capacity, manage costs, and meet service level agreements while ensuring data privacy.

How to implement this in your domain

  1. 1Assess the privacy requirements for your LLM inference workloads and determine if confidential computing is necessary.
  2. 2Benchmark your specific LLM models and hardware configurations under confidential computing environments to quantify performance overheads.
  3. 3Adjust capacity planning and resource allocation strategies to account for the reduced throughput and earlier saturation observed in confidential modes.
  4. 4Evaluate the trade-off between enhanced security and performance impact when designing confidential AI inference solutions.

Who benefits

Cloud ComputingHealthcareBFSIDefenseAI Infrastructure

Key takeaways

  • Confidential computing for LLM inference introduces performance overheads.
  • Latency increases and throughput decreases in confidential mode on NVIDIA H100/Intel TDX.
  • Larger LLMs may saturate earlier under confidential execution.
  • Capacity planning must account for these performance penalties.

Original post by Wei Wang, Abdul Hyee Waqas, Burns Smith

"arXiv:2607.19353v1 Announce Type: new 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…"

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Originally posted by Wei Wang, Abdul Hyee Waqas, Burns Smith on X · view source

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