vLLM
/
vllm-project.github.io
mirror of https://github.com/vllm-project/vllm-project.github.io.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Fix dead links to installation docs 

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Harry Mellor 2025-01-27 13:08:50 +00:00
parent b84215b2e4
commit 59933df72b
2 changed files with 4 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
_posts/2023-06-20-vllm.md
View File

@ -108,7 +108,7 @@ This utilization of vLLM has also significantly reduced operational costs. With
### Get started with vLLM ### Get started with vLLM
Install vLLM with the following command (check out our [installation guide](https://vllm.readthedocs.io/en/latest/getting_started/installation.html) for more): Install vLLM with the following command (check out our [installation guide](https://docs.vllm.ai/en/latest/getting_started/installation/index.html) for more):
```bash ```bash
$ pip install vllm $ pip install vllm

6
_posts/2025-01-10-dev-experience.md
View File

@ -29,7 +29,7 @@ For those who prefer a faster package manager, [**uv**](https://github.com/astra
uv pip install vllm uv pip install vllm
``` ```
Refer to the [documentation](https://docs.vllm.ai/en/latest/getting_started/installation/gpu-cuda.html#install-released-versions) for more details on setting up [**uv**](https://github.com/astral-sh/uv). Using a simple server-grade setup (Intel 8th Gen CPU), we observe that [**uv**](https://github.com/astral-sh/uv) is 200x faster than pip: Refer to the [documentation](https://docs.vllm.ai/en/latest/getting_started/installation/gpu/index.html?device=cuda#create-a-new-python-environment) for more details on setting up [**uv**](https://github.com/astral-sh/uv). Using a simple server-grade setup (Intel 8th Gen CPU), we observe that [**uv**](https://github.com/astral-sh/uv) is 200x faster than pip:
```sh ```sh
# with cached packages, clean virtual environment # with cached packages, clean virtual environment
@ -77,11 +77,11 @@ VLLM_USE_PRECOMPILED=1 pip install -e .
The `VLLM_USE_PRECOMPILED=1` flag instructs the installer to use pre-compiled CUDA kernels instead of building them from source, significantly reducing installation time. This is perfect for developers focusing on Python-level features like API improvements, model support, or integration work. The `VLLM_USE_PRECOMPILED=1` flag instructs the installer to use pre-compiled CUDA kernels instead of building them from source, significantly reducing installation time. This is perfect for developers focusing on Python-level features like API improvements, model support, or integration work.
This lightweight process runs efficiently, even on a laptop. Refer to our [documentation](https://docs.vllm.ai/en/latest/getting_started/installation/gpu-cuda.html#python-only-build-without-compilation) for more advanced usage. This lightweight process runs efficiently, even on a laptop. Refer to our [documentation](https://docs.vllm.ai/en/latest/getting_started/installation/gpu/index.html?device=cuda#build-wheel-from-source) for more advanced usage.
### C++/Kernel Developers ### C++/Kernel Developers
For advanced contributors working with C++ code or CUDA kernels, we incorporate a compilation cache to minimize build time and streamline kernel development. Please check our [documentation](https://docs.vllm.ai/en/latest/getting_started/installation/gpu-cuda.html#full-build-with-compilation) for more details. For advanced contributors working with C++ code or CUDA kernels, we incorporate a compilation cache to minimize build time and streamline kernel development. Please check our [documentation](https://docs.vllm.ai/en/latest/getting_started/installation/gpu/index.html?device=cuda#build-wheel-from-source) for more details.
## Track Changes with Ease ## Track Changes with Ease