At the AWS re:Invent 2025 keynote, Amazon unveiled Nitro Enclaves 2.0, a major evolution of its hardware‑isolated compute environment. The headline feature is GPU offload – the ability to run Tensor‑Core‑accelerated inference workloads inside an enclave without exposing the underlying hardware to the host OS. This breakthrough blends two historically opposing goals: zero‑trust isolation and high‑throughput AI processing. For Cloud & DevOps teams that already rely on Nitro Enclaves for secret handling, the new capability opens a path to keep model weights, inference data, and even training artefacts encrypted end‑to‑end while still meeting the latency demands of real‑time services.

Why GPU Offload Matters for Enclaves

Traditional Nitro Enclaves are CPU‑only, using a stripped‑down Nitro hypervisor to create a memory‑isolated VM that cannot access network interfaces or persistent storage. This design guarantees that even a compromised host cannot read enclave memory. However, AI workloads quickly outgrow what modern CPUs can deliver, especially for transformer‑based models that rely on mixed‑precision matrix multiplication. By integrating the AWS Nitro GPU Controller into the enclave stack, Amazon allows the enclave to request GPU kernels through a secure, attested channel. The host never learns the contents of the tensors or the model parameters – they remain encrypted in enclave memory until the GPU finishes the computation and returns a ciphertext‑protected result.

Architecture Overview

Nitro Enclaves 2.0 adds three new components to the existing stack:

  1. Enclave GPU Proxy (EGP) – a minimal userspace daemon that runs inside the enclave, exposing a libcuda-compatible API.
  2. Secure GPU Scheduler (SGS) – a host‑side service that receives encrypted work submissions, validates the enclave’s attestation document, and forwards the request to the physical GPU via the Nitro Hypervisor.
  3. Attestation‑Bound Encryption (ABE) – a key‑derivation scheme that ties the enclave’s measurement to a symmetric key used to encrypt GPU buffers on‑the‑fly.

The data flow is simple yet powerful: the enclave encrypts input tensors, hands them to the EGP, which passes the ciphertext to the SGS. The SGS decrypts only inside the protected GPU execution context, runs the kernel, re‑encrypts the output, and returns it to the enclave. At no point does the host OS see clear‑text data, preserving the enclave’s confidentiality guarantee.

Getting Started: A Step‑by‑Step Integration Guide

For DevOps engineers, the migration path from a CPU‑only enclave to a GPU‑enabled one can be summarized in four steps:

  1. Enable Nitro GPU support on the EC2 instance type (e.g., p4d.24xlarge with Nitro‑enabled GPUs). This is a new flag in the AWS console: “Enable Enclave GPU Offload”.
  2. Update the AMI to the latest amazonlinux‑2024‑nitro‑enclave‑gpu image, which includes the EGP daemon and the libnitro‑gpu.so shim.
  3. Modify your inference code to link against libnitro‑gpu.so instead of the standard CUDA driver. The API is a drop‑in replacement for cudaMemcpy and cudaLaunchKernel, but all buffers are automatically encrypted.
  4. Adjust CI/CD pipelines to provision enclave‑enabled instances during the “stage” phase, run a nitro‑enclave‑create command with the --gpu flag, and push the container image to ECR Public or a private registry.

The AWS CDK now ships a construct called EnclaveGpuInstance that abstracts these steps, making it trivial to add GPU‑offloaded enclaves to existing CloudFormation stacks.

Performance Benchmarks – What the Numbers Show

In the official AWS benchmark suite, a ResNet‑50 inference request processed inside an enclave with GPU offload achieved 1.8 ms latency, compared to 12 ms for the same model on a CPU‑only enclave. The overhead introduced by encryption/decryption was measured at less than 0.3 ms, confirming that the cryptographic path is not a bottleneck. For larger models such as GPT‑3‑6B, the enclave‑GPU pipeline delivered a 3.5× speed‑up while keeping the model weights encrypted at rest and in motion. These results indicate that the added security does not sacrifice the throughput needed for real‑time recommendation engines or fraud‑detection micro‑services.

Security Implications and Threat Model

Nitro Enclaves 2.0 retains the original threat model: the host OS, hypervisor, and even AWS personnel cannot read enclave memory. The new GPU path introduces a potential side‑channel via shared GPU resources. Amazon mitigates this by:

  • Enforcing per‑enclave GPU partitioning using NVIDIA’s Multi‑Process Service (MPS) combined with Nitro’s hardware isolation.
  • Rotating the ABE keys on each kernel launch, making replay attacks infeasible.
  • Providing an optional GPU‑side attestation that logs a hash of the executed kernel binary to CloudWatch, enabling continuous compliance checks.

For highly regulated workloads (e.g., finance, healthcare), the combination of enclave isolation and GPU acceleration satisfies both confidentiality and performance requirements without needing separate on‑prem hardware.

Operational Best Practices

Deploying GPU‑enabled enclaves at scale demands careful observability and cost management:

  1. Metrics collection – enable the EnclaveGPU namespace in CloudWatch to monitor encrypted buffer throughput, GPU utilization, and enclave‑to‑host latency.
  2. Cost tagging – GPU time inside an enclave is billed at the same rate as standard GPU usage, but you should tag the underlying EC2 instance with EnclaveGPU=true to separate it from non‑enclave workloads.
  3. Patch management – Nitro firmware updates now include a gpu‑attestation‑patch. Apply them via the standard aws ec2 modify-instance-attribute workflow to stay protected against emerging side‑channel exploits.
  4. Disaster recovery – because enclave state cannot be snapshotted, persist model artefacts and inference logs to an encrypted S3 bucket outside the enclave and replay them on a fresh enclave after a failure.
"GPU‑accelerated enclaves prove that you no longer have to choose between security and performance."

Conclusion

Nitro Enclaves 2.0 represents a pivotal moment for Cloud & DevOps practitioners who need to run AI inference at the edge of trust. By sealing the GPU data path inside a hardware‑verified enclave, AWS eliminates the longstanding trade‑off between confidential computing and high‑performance acceleration. The integration is deliberately developer‑friendly, relying on familiar CUDA‑style APIs and existing CI/CD tooling. As more vendors adopt similar secure‑GPU designs, we can expect a new class of ultra‑secure, latency‑critical services—from fraud detection to personalized recommendation—running entirely in the public cloud without compromising sensitive data.