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Bump bitsandbytes from 0.44.1 to 0.48.1#217

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Bumps bitsandbytes from 0.44.1 to 0.48.1.

Release notes

Sourced from bitsandbytes's releases.

0.48.1

This release fixes a regression introduced in 0.48.0 related to LLM.int8(). This issue caused poor inference results with pre-quantized checkpoints in HF transformers.

What's Changed

Full Changelog: bitsandbytes-foundation/bitsandbytes@0.48.0...0.48.1

0.48.0: Intel GPU & Gaudi support, CUDA 13, performance improvements, and more!

Highlights

🎉 Intel GPU Support

We now officially support Intel GPUs on Linux and Windows! Support is included for all major features (LLM.int8(), QLoRA, 8bit optimizers) with the exception of the paged optimizer feature.

This support includes the following hardware:

  • Intel® Arc™ B-Series Graphics
  • Intel® Arc™ A-Series Graphics
  • Intel® Data Center GPU Max Series

A compatible PyTorch version with Intel XPU support is required. The current minimum is PyTorch 2.6.0. It is recommended to use the latest stable release. See Getting Started on Intel GPU for guidance.

🎉 Intel Gaudi Support

We now officially support Intel Gaudi2 and Gaudi3 accelerators. This support includes LLM.int8() and QLoRA with the NF4 data type. At this time optimizers are not implemented.

A compatible PyTorch version with Intel Gaudi support is required. The current minimum is Gaudi v1.21 with PyTorch 2.6.0. It is recommended to use the latest stable release. See the Gaudi software installation guide for guidance.

NVIDIA CUDA

  • The 4bit dequantization kernel was improved by @​Mhmd-Hisham in #1746. This change brings noticeable speed improvements for prefill, batch token generation, and training. The improvement is particularly prominent on A100, H100, and B200.
  • We've added CUDA 13.0 compatibility across Linux x86-64, Linux aarch64, and Windows x86-64 platforms.
    • Hardware support for CUDA 13.0 is limited to Turing generation and newer.
    • Support for Thor (SM110) is available in the Linux aarch64 build.

🚨 Breaking Changes

  • Dropped support for PyTorch 2.2. The new minimum requirement is 2.3.0.
  • Removed Maxwell GPU support for all CUDA builds.

What's Changed

... (truncated)

Changelog

Sourced from bitsandbytes's changelog.

v0.45.1

Improvements:

  • Compatibility for triton>=3.2.0
  • Moved package configuration to pyproject.toml
  • Build system: initial support for NVIDIA Blackwell B100 GPUs, RTX 50 Blackwell series GPUs and Jetson Thor Blackwell.
    • Note: Binaries built for these platforms are not included in this release. They will be included in future releases upon the availability of the upcoming CUDA Toolkit 12.7 and 12.8.

Bug Fixes:

  • Packaging: wheels will no longer include unit tests. (#1478)

Dependencies:

  • Sets the minimum PyTorch version to 2.0.0.

0.45.0

This is a significant release, bringing support for LLM.int8() to NVIDIA Hopper GPUs such as the H100.

As part of the compatibility enhancements, we've rebuilt much of the LLM.int8() code in order to simplify for future compatibility and maintenance. We no longer use the col32 or architecture-specific tensor layout formats while maintaining backwards compatibility. We additionally bring performance improvements targeted for inference scenarios.

Performance Improvements

This release includes broad performance improvements for a wide variety of inference scenarios. See this X thread for a detailed explanation.

Breaking Changes

🤗PEFT users wishing to merge adapters with 8-bit weights will need to upgrade to peft>=0.14.0.

Packaging Improvements

  • The size of our wheel has been reduced by ~43.5% from 122.4 MB to 69.1 MB! This results in an on-disk size decrease from ~396MB to ~224MB.
  • Binaries built with CUDA Toolkit 12.6.2 are now included in the PyPI distribution.
  • The CUDA 12.5.0 build has been updated to CUDA Toolkit 12.5.1.

Deprecations

  • A number of public API functions have been marked for deprecation and will emit FutureWarning when used. These functions will become unavailable in future releases. This should have minimal impact on most end-users.
  • The k-bit quantization features are deprecated in favor of blockwise quantization. For all optimizers, using block_wise=False is not recommended and support will be removed in a future release.
  • As part of the refactoring process, we've implemented many new 8bit operations. These operations no longer use specialized data layouts.

Full Changelog

Commits

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@dependabot dependabot bot added dependencies Pull requests that update a dependency file python Pull requests that update python code labels Oct 3, 2025
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@phenobarbital phenobarbital force-pushed the dependabot/pip/bitsandbytes-0.48.1 branch from ef951d6 to b0a20d1 Compare October 9, 2025 10:16
@dependabot
Bump bitsandbytes from 0.44.1 to 0.48.1
6ed1919 
Bumps [bitsandbytes](https://github.com/bitsandbytes-foundation/bitsandbytes) from 0.44.1 to 0.48.1.
- [Release notes](https://github.com/bitsandbytes-foundation/bitsandbytes/releases)
- [Changelog](https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/CHANGELOG.md)
- [Commits](bitsandbytes-foundation/bitsandbytes@0.44.1...0.48.1)

---
updated-dependencies:
- dependency-name: bitsandbytes
  dependency-version: 0.48.1
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
@dependabot dependabot bot force-pushed the dependabot/pip/bitsandbytes-0.48.1 branch from b0a20d1 to 6ed1919 Compare October 23, 2025 20:27
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dependabot bot commented on behalf of github Oct 30, 2025

Superseded by #322.

@dependabot dependabot bot closed this Oct 30, 2025
@dependabot dependabot bot deleted the dependabot/pip/bitsandbytes-0.48.1 branch October 30, 2025 05:31
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