Additionally, QLoader supports . If the target hardware lacks a specific kernel for a mixed-precision operation (e.g., an INT2 matrix multiplication), the loader transparently de-quantizes the weights to a supported precision (e.g., INT8) in the cache, ensuring the model runs correctly without crashing, trading off minimal latency for compatibility.
This greedy approach is computationally cheaper than RL-based search and yields near-optimal solutions for standard DNN architectures.
If an attacker has physical access to your device, they can open it, short two test points, and—using QLoader—dump everything: your photos, messages, keys, and credentials. No password required. This is why modern iPhones (which don’t use Qualcomm for the main AP) are often considered more physically secure, and why Google introduced to isolate this kind of low-level access.
A potential concern is the overhead introduced by the QLoader unpacking kernels. Our profiling shows that for batch sizes greater than 1, the unpacking overhead is less than 1% of the total inference time. For batch size 1 (common in edge scenarios), the reduced memory fetch time compensates for the unpacking instructions, resulting in a net speedup.
To address these challenges, we propose . QLoader is not merely a quantization algorithm but a comprehensive framework consisting of a sensitivity analyzer, a mixed-precision scheduler, and a runtime inference loader. The core philosophy of QLoader is "load what is necessary," dynamically adjusting the precision of weights and activations based on the specific hardware capabilities of the target device.