ONNX Runtime vs Gemini

ONNX Runtime is mid-platform-modernization. v1.25.0 raised the build floor to C++20 and CUDA 12.0, removed the ArmNN execution provider, and bumped ONNX to 1.21. v1.24.1 made the parallel move on the Python side — dropped 3.10, added 3.14 and free-threaded (PEP 703) variants, and introduced the EP Plugin API for dynamically loaded execution providers. Between those structural releases, the 1.24.x patch line has been heavily security-focused: multiple heap out-of-bounds fixes (GatherCopyData, RoiAlign, Lora Adapters, ArrayFeatureExtractor). New model and operator support continues — Qwen3.5 across LinearAttention/CausalConvState/RMSNorm/RotEMB, including WebGPU.

The runtime is repositioning for the next wave: free-threaded Python lets ML workloads finally escape the GIL on CPU paths, the EP Plugin API decouples hardware-vendor execution providers from the runtime release cycle, and the WebGPU EP keeps adding frontier-model coverage. The cost is sharp deprecation — C++20, CUDA 12, no more Python 3.10, no more x86_64 macOS — but this is the pattern of a project clearing technical debt to support the next two years of GPU-vendor diversity and edge inference.

Expect more vendor execution providers (Qualcomm QNN, Apple Neural Engine, Intel) to migrate onto the new Plugin EP API in the next two releases, and continued security-patch cadence on 1.24.x for users who can't move to 1.25 yet. WebGPU EP coverage will keep tracking new model architectures — Qwen 3.5 today, the next frontier MoE class tomorrow.