A high-throughput and memory-efficient inference and serving engine for LLMs
Mirrored from https://github.com/tduncan-lum/Nvidia-Optimization-Stack---VLLM.git
Synced Nov 15, 04:45 UTC
[doc] Fix broken link in the installation for CPU (#19980)
Signed-off-by: Kay Yan <kay.yan@daocloud.io>
Kay Yan committed
Jun 24, 2025 at 12:04 UTC
0eed516951e0c5b834d0e59b86fd6b555fa90f65
2 files changed
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docs/getting_started/installation/cpu/arm.inc.md
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@@ -23,7 +23,7 @@ ARM CPU backend currently supports Float32, FP16 and BFloat16 datatypes.
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# --8<-- [end:pre-built-wheels]
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# --8<-- [start:build-wheel-from-source]
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---8<-- "docs/getting_started/installation/cpu/cpu/build.inc.md"
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+--8<-- "docs/getting_started/installation/cpu/build.inc.md"
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Testing has been conducted on AWS Graviton3 instances for compatibility.
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docs/getting_started/installation/cpu/x86.inc.md
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@@ -24,7 +24,7 @@ vLLM initially supports basic model inferencing and serving on x86 CPU platform,
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# --8<-- [end:pre-built-wheels]
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# --8<-- [start:build-wheel-from-source]
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---8<-- "docs/getting_started/installation/cpu/cpu/build.inc.md"
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+--8<-- "docs/getting_started/installation/cpu/build.inc.md"
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!!! note
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- 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.