Preface: When comparing VMware TKGI, Docker, and Kubernetes (K8s) for CUDA (NVIDIA’s parallel computing platform) workflows, the “best” choice depends on your scale and infrastructure.
Choose Docker – if you are a data scientist doing local model development.
Choose Native Kubernetes – if you are building a large-scale AI platform on physical hardware (Bare-metal) for maximum performance.
Choose VMware TKGI – if you need high availability, vGPU flexibility, and are already heavily invested in the VMware ecosystem.
Background: CVE‑2025‑33220 lives in the hypervisor’s vGPU Manager, not in:
- Docker
- Containerd
- Kubernetes
- NVIDIA Container Runtime
- NVIDIA Docker runtime
- PyTorch/TensorFlow workloads
- CUDA libraries inside containers
CVE‑2025‑33220 requires:
- Freeing an object inside the hypervisor
- A later operation accessing that SAME freed internal heap structure
- The hypervisor NOT realizing the handle is stale
- A malformed RM object relationship or command sequence
- Conditions normal CUDA applications never generate
If there is no hypervisor-based vGPU, there is no attack surface, because:
- The ioctl path stops at the bare‑metal NVIDIA GPU driver
- There is no vGPU Manager backend
- No vGPU protocol messages are generated
- No hypervisor memory structures exist to exploit
The CVE is triggered only under very specific hypervisor‑internal states that normal or even “weird order” RMAPI usage will never produce.
Vulnerability details: CVE-2025-33220 – NVIDIA vGPU software contains a vulnerability in the Virtual GPU Manager, where a malicious guest could cause heap memory access after the memory is freed. A successful exploit of this vulnerability might lead to code execution, escalation of privileges, data tampering, denial of service, or information disclosure.
Official announcement: Please refer to the link for details.- https://nvidia.custhelp.com/app/answers/detail/a_id/5747