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Update dependency bitsandbytes to v0.50.0 - #17

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Update dependency bitsandbytes to v0.50.0#17
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renovate/bitsandbytes-0.x

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@renovate renovate Bot commented Jul 26, 2026

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This PR contains the following updates:

Package Change Age Confidence
bitsandbytes (changelog) ==0.49.2 → ==0.50.0 age confidence

Release Notes

bitsandbytes-foundation/bitsandbytes (bitsandbytes)

v0.50.0: : Faster 4-bit inference, CPU/ROCm/Apple Silicon improvements, and more!

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This release brings a new fused 4-bit GEMM for inference on CUDA and ROCm, faster CPU ops on x86-64 and ARM64, reduced host-side overhead, and a much improved Apple Silicon backend. We've also added Windows on ARM CPU support, ROCm builds for Windows, additional ROCm and CUDA build variants, and new optimizer support on CPU and Intel XPU.

Highlights

🚀 New 4-bit GEMM kernels for inference (CUDA)

New fused 4-bit dequantize + GEMM kernels replace the old GEMV and dequantize + F.linear paths for small-to-medium batch sizes. 4-bit inference is up to 4x faster at batch sizes of 2 through 64 across Turing through Blackwell, with wins at batch size 1 in many cases too. Nested (double) quantization and bias are fused in as well, so nested quant sees an additional benefit. Kernel selection happens automatically at runtime based on shape, GPU architecture, and SM count. See #​1949 for benchmarks and details.

🚀 Fused 4-bit SIMT GEMM on ROCm

The SIMT version of the new 4-bit GEMM has been ported to ROCm and wired into the same dispatch for small inference batches, validated on gfx1100, gfx1201, and gfx1151 (#​1979).

AMD ROCm improvements

Stability and performance improvements bring AMD ROCm support out of preview; it is now considered stable.

  • ROCm wheels for Windows are now published for ROCm 7.2 and 7.14 (#​1915, #​2007).
  • Added ROCm 7.14 and dropped the ROCm 6.2 and 6.3 builds (#​2007).
  • Expanded GPU target coverage to add RDNA2 consumer GPUs, additional RDNA3 and RDNA3.5 GPUs, and CDNA1 (gfx908) data center GPUs (#​1886, #​2007).
Apple Silicon (MPS)

Apple Silicon support is improved. The MPS backend added optimized Metal kernels from the Hub (#​1875) and was substantially improved (#​1960, #​1983, #​1994) so that all 4-bit and LLM.int8() configurations now work on MPS. On macOS 26+, install the kernels package for the best performance, which enables the optimized Metal kernels; otherwise a naive fallback is used. The MPS path requires torch >= 2.9.

The last remaining feature for support parity is the 8bit optimizers, which will land in a future release.

CPU performance on x86-64 and ARM64

Blockwise quantization and dequantization on CPU are considerably faster, mostly from better SIMD usage plus some compile flag tuning. Improvements range from 1.1x to over 20x depending on op, dtype, and hardware, with the largest gains on fp16 and on x86-64 CPUs without AVX-512. The LLM.int8() matmul on CPU was improved as well. See #​1968 for benchmarks.

Reduced host-side CPU overhead

Reduced Python dispatch overhead, especially on the CUDA/ROCm backend (#​1953).

Windows on ARM CPU support

Windows ARM64 CPU wheels are now built with NEON-optimized kernels (#​1959), with nightly test coverage added (#​1962).

New optimizer support
NVIDIA CUDA
  • Added CUDA 13.2 build wheels and nightly test coverage (#​1925).
  • Smaller wheels: reduced the CUDA build matrix and added a closest-version fallback for library loading (#​1980).
Intel XPU
  • Added a oneAPI 2026 build; wheels now package both the 2025 and 2026 variants and pick the right one at runtime based on the PyTorch XPU version (#​2002).
  • Fixed gemv_4bit bf16 correctness on Intel Arc A-series (Alchemist) GPUs (#​1942).

🚨 Breaking Changes

  • Minimum PyTorch version is now 2.4 (#​1926).
  • Removed deprecated APIs: the research module, non-blockwise (block_wise=False) optimizers, and legacy dynamic quantization functions, along with their CUDA/HIP kernels (#​1871, #​1880).
  • Removed legacy sparse functionality (spmm_coo, spmm_coo_very_sparse) and dropped the cusparse/hipsparse dependencies (#​1881).

Deprecations

  • igemm, batched_igemm, and check_matmul are deprecated and now emit warnings (#​2003).
  • Passing 4-bit weights quantized in transposed [in_features, out_features] orientation to matmul_4bit now emits a DeprecationWarning. Support is likely to be removed in the future. This is not a typical use case (#​1949).

Other improvements and bug fixes

This release also includes a number of other improvements, bug fixes, and documentation updates. See the full changelog below.

What's Changed

New Contributors

Full Changelog: bitsandbytes-foundation/bitsandbytes@0.49.2...0.50.0

Configuration

📅 Schedule: (UTC)

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    • At any time (no schedule defined)
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🚦 Automerge: Disabled by config. Please merge this manually once you are satisfied.

♻ Rebasing: Whenever PR becomes conflicted, or you tick the rebase/retry checkbox.

🔕 Ignore: Close this PR and you won't be reminded about this update again.

  • If you want to rebase/retry this PR, check this box

This PR was generated by Mend Renovate. View the repository job log.

@renovate

renovate Bot commented Jul 26, 2026 •
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⚠️ Artifact update problem

Renovate failed to update an artifact related to this branch. You probably do not want to merge this PR as-is.

♻ Renovate will retry this branch, including artifacts, only when one of the following happens:

  • any of the package files in this branch needs updating, or
  • the branch becomes conflicted, or
  • you click the rebase/retry checkbox if found above, or
  • you rename this PR's title to start with "rebase!" to trigger it manually

The artifact failure details are included below:

File name: uv.lock
Command failed: uv lock --upgrade-package bitsandbytes
Using CPython 3.14.7 interpreter at: /opt/containerbase/tools/python/3.14.7/bin/python3.14
  × No solution found when resolving dependencies for split (markers:
  │ python_full_version >= '3.14' and sys_platform == 'win32'):
  ╰─▶ Because torchvision==0.27.1 depends on torch==2.12.1 and gemma-4 depends
      on torch==2.13.0, we can conclude that gemma-4 and torchvision==0.27.1
      are incompatible.
      And because gemma-4 depends on torchvision==0.27.1 and your workspace
      requires gemma-4, we can conclude that your workspace's requirements
      are unsatisfiable.

hint: The resolution failed for an environment that is not the current one, consider limiting the environments with `tool.uv.environments`.

@renovate
renovate Bot force-pushed the renovate/bitsandbytes-0.x branch from 9dce7cf to 617fd97 Compare July 30, 2026 18:58
@renovate
renovate Bot force-pushed the renovate/bitsandbytes-0.x branch from 617fd97 to e896887 Compare August 12, 2026 01:11
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