60808bd4c7
- Make device tab names more explicit - Add comprehensive list of devices to https://docs.vllm.ai/en/latest/getting_started/installation/index.html - Add `attention` blocks to the intro of all devices that don't have pre-built wheels/images --------- Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
1.2 KiB
1.2 KiB
Installation
vLLM initially supports basic model inferencing and serving on x86 CPU platform, with data types FP32, FP16 and BF16.
:::{attention} There are no pre-built wheels or images for this device, so you must build vLLM from source. :::
Requirements
- OS: Linux
- Compiler:
gcc/g++ >= 12.3.0(optional, recommended) - Instruction Set Architecture (ISA): AVX512 (optional, recommended)
:::{tip} Intel Extension for PyTorch (IPEX) extends PyTorch with up-to-date features optimizations for an extra performance boost on Intel hardware. :::
Set up using Python
Pre-built wheels
Build wheel from source
:::{include} build.inc.md :::
:::{note}
- AVX512_BF16 is an extension ISA provides native BF16 data type conversion and vector product instructions, which brings some performance improvement compared with pure AVX512. The CPU backend build script will check the host CPU flags to determine whether to enable AVX512_BF16.
- If you want to force enable AVX512_BF16 for the cross-compilation, please set environment variable
VLLM_CPU_AVX512BF16=1before the building. :::