Zipslicer, a library for loading LLM checkpoints on consumer hardware
This is a low-level opensource library I developed for my own use and decided to share, as it makes it possible to process large checkpoints of neural networks without renting high-RAM instances, on a regular PC. It replaces torch.load() with a custom function that produces a dictionary that materializes tensors on the fly. Compared to other solutions it doesn't require sharding or re-encoding checkpoints and uses them completely as-is. It is a foundation to make it possible to run inference and compress language models and other large models one layer at a time - in principle, even one…
In plain words
Zipslicer is an open-source library that enables users to load large neural network checkpoints on consumer computers without renting high-RAM cloud instances. It replaces PyTorch's standard load function with a custom one that materializes tensors on demand from checkpoint files. Unlike alternative solutions, Zipslicer works with unmodified checkpoints and requires no sharding or re-encoding. It allows processing large language models layer-by-layer or even tensor-by-tensor, supporting inference and compression tasks on standard PCs.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
This is a low-level opensource library I developed for my own use and decided to share, as it makes it possible to process large checkpoints of neural networks without renting high-RAM instances, on a regular PC. It replaces torch.load() with a custom function that produces a dictionary that materializes tensors on the fly. Compared to other solutions it doesn't require sharding or re-encoding checkpoints and uses them completely as-is. It is a foundation to make it possible to run inference and compress language models and other large models one layer at a time - in principle, even one tensor at a time. I describe the rationale and technical details of the library's design in the blogpost: https://kir-gadjello.github.io/posts/zipslicer/
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