NVIDIA has introduced Confidential Computing for its Blackwell GPU architecture, delivering hardware-rooted security designed to protect AI models and sensitive data during active inference without compromising performance. As enterprise adoption of generative AI accelerates, concerns over data privacy, model theft, and regulatory compliance have become primary adoption barriers. The new Confidential Computing framework addresses these challenges by embedding cryptographic protections directly into the silicon of Blackwell processors, including the RTX PRO 6000 and HGX B200 and B300 platforms. The security architecture relies on a factory-fused private key that remains inaccessible to host software or firmware, establishing an immutable hardware root of trust. Before a workload executes, NVIDIA Remote Attestation Service verifies the system configuration against known-good integrity manifests. Once a trusted execution environment validated by Intel TDX or AMD SEV-SNP is confirmed, decryption keys are deployed securely. After this initial handshake, security operations run transparently, preventing additional latency during individual inference requests. NVIDIA engineers identified secure work submission overhead and encrypted host-to-device bandwidth as the primary factors influencing performance. To mitigate these bottlenecks, the company optimized kernel launch sequences and collaborated with upstream inference frameworks to streamline encrypted data transfers. Independent benchmarks conducted on the Qwen 3.5-397B-A17B-FP8 model using the SGLang runtime demonstrated that enabling Confidential Computing introduces minimal throughput degradation. Across concurrency levels ranging from four to two hundred fifty-six concurrent requests, steady-state inference speed and time-to-first-token metrics experienced only a one to eight percent reduction compared to unencrypted baselines. Larger batch configurations and optimized sequence lengths further minimized the performance impact. By integrating zero-trust architecture principles directly into GPU compute pipelines, NVIDIA provides a compliant pathway for organizations to deploy proprietary large language models and sensitive enterprise data in untrusted cloud environments. The architecture supports end-to-end encryption across NVLink interconnects and maintains compatibility with standard containerized orchestration tools. This development signals a shift toward production-ready AI security, allowing enterprises to meet stringent data governance requirements while preserving the inference velocity necessary for real-time applications. Organizations can now leverage hardware-backed confidentiality to safeguard intellectual property and user privacy without sacrificing computational efficiency or operational throughput.