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NVIDIA's integration of memory encryption and encrypted NVLink into its Blackwell architecture demonstrates that high-performance AI inference can coexist with hardware-enforced data privacy. By optimizing frameworks like TensorRT LLM to minimize latency overhead, the company addresses the critical need for secure processing of sensitive information in regulated sectors without sacrificing throughput. This development marks a significant step toward deploying large-scale AI models in environments where confidentiality is paramount.

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NVIDIA chips now encrypt AI work to keep data private during inference

Quantum ZeitgeistSeptember 23, 20265 min read

NVIDIA Confidential Computing encrypts data processed on NVIDIA GPUs, securing AI inference with memory encryption and NVLink.

NVIDIA is evaluating adaptations to its Blackwell GPUs to maintain AI inference performance while adding a new layer of data privacy. As large language models process increasingly sensitive information, the company is building a trusted environment using memory-encrypted confidential virtual machines, confidential GPUs, and encrypted NVLink interconnects. Maintaining high performance within this system requires co-optimization of AI frameworks and the confidential computing environment; workload characteristics, such as request volume, directly impact how noticeable any performance overhead becomes. NVIDIA Blackwell GPUs are demonstrating the ability to retain between 96.1% and 98.2% of baseline output-token throughput when running AI inference…

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