sglang-kernel
Kernel Library for SGLang
Decision gist · record as of 2026-08-14
Yes, if you are building or deploying an LLM inference engine that benefits from custom CUDA kernels and you have torch == 2.13.0 and Python ≥3.10 available. The active maintenance, permissive license, and high repository engagement suggest solid backing. Install friction is moderate due to compiled wheels, but no runtime dependencies simplify integration. Not suitable as a standalone package; it is a dependency for inference frameworks.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires torch == 2.13.0 and Python ≥3.10.
- NVIDIA CUDA GPU required.
- Known segmentation fault with CUDA 12.6; update ptxas to 12.8.
License · maintenance · safety
permissive license (permissive) — Apache License 2.0 (permissive) allows commercial use, modification, and distribution with minimal restrictions. You must include license and copyright notices and document changes.
last release 2026-08-06 (8 days) · last repo commit 2026-08-14 · 31,807 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 643,262 downloads/mo, #5,608 on PyPI
Alternatives
Verify before relying
pip install sglang-kernel
# Import and use within inference framework that depends on sglang-kernel- Whether pre-built wheels cover all target architectures and CUDA versions needed for your deployment
- Performance gains relative to standard PyTorch operations for your specific model and batch sizes
- Exact kernel operations exposed and their integration patterns within inference frameworks
What it is and what it does
sglang-kernel is a library of hand-optimized CUDA kernels designed to accelerate inference for large language models and vision-language models. It provides custom compute primitives that integrate with PyTorch, allowing LLM inference engines to execute critical operations more efficiently on NVIDIA GPUs.
The package is distributed as pre-compiled wheels for x86_64 and aarch64 architectures, with no runtime Python dependencies. It requires torch == 2.13.0 and Python ≥3.10, and is intended to be used as a low-level acceleration layer within larger inference frameworks rather than as a standalone application. The source code lives in the sglang project repository.
Use it for
- Accelerate batch matrix multiplication and attention operations in LLM inference engines
- Optimize vision-language model inference by providing specialized CUDA kernels for image and token processing
- Reduce latency and increase throughput in production LLM serving systems that depend on custom kernel performance
- Integrate with PyTorch-based inference frameworks that need fine-grained control over GPU compute primitives
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building or deploying an LLM inference engine that benefits from custom CUDA kernels and you have torch == 2.13.0 and Python ≥3.10 available.
The active maintenance, permissive license, and high repository engagement suggest solid backing. Install friction is moderate due to compiled wheels, but no runtime dependencies simplify integration. Not suitable as a standalone package; it is a dependency for inference frameworks.
Install
sglang-kernel on PyPI
Before you install
Medium install friction due to compiled wheel distribution (aarch64 and x86_64 variants). Active maintenance with recent release 8 days ago and 31807 repository stars. Requires torch == 2.13.0 and Python ≥3.10.
Requires torch == 2.13.0 and Python ≥3.10. NVIDIA CUDA GPU required. Known segmentation fault with CUDA 12.6; update ptxas to 12.8.
License in practice
Apache License 2.0 (permissive) allows commercial use, modification, and distribution with minimal restrictions. You must include license and copyright notices and document changes.
Quickstart
pip install sglang-kernel
# Import and use within inference framework that depends on sglang-kernelVerify before relying
- Whether pre-built wheels cover all target architectures and CUDA versions needed for your deployment
- Performance gains relative to standard PyTorch operations for your specific model and batch sizes
- Exact kernel operations exposed and their integration patterns within inference frameworks
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 8 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 643,262 / month, #5,608 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Environment :: GPU :: NVIDIA CUDALicense :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3 |
Evidence: sglang_kernel-0.4.6.post1-cp310-abi3-manylinux2014_aarch64.whl; sglang_kernel-0.4.6.post1-cp310-abi3-manylinux2014_x86_64.whl
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