Silicon-Optimized Inference Snaps (canonical.com)

Canonical announced “optimized inference snaps” for Ubuntu: snap packages that detect a device’s silicon and automatically install the best combination of model build, runtime engine, and quantization for popular models like Qwen 2.5 VL and DeepSeek R1 with a single command (e.g., sudo snap install qwen-vl --beta; sudo snap install deepseek-r1 --beta). The public beta already includes Intel- and Ampere-optimized builds and the packaging framework is open-sourced, letting snaps dynamically fetch and load the recommended build for the host to simplify dependencies and reduce latency. This is significant because it collapses the complex matrix of model sizes, runtimes, and hardware-specific optimizations into a frictionless developer experience, enabling efficient local inference across desktops, servers and edge devices. Canonical’s approach integrates vendor optimizations (Intel’s OpenVINO support and Ampere-tuned AIO builds are cited) so models run with vendor-recommended parameters and quantizations out of the box. For the AI/ML community this means faster time-to-deploy, better utilization of heterogeneous silicon, and an easier path to ship performant, maintainable on-device inference that can improve as more silicon partners contribute optimizations.