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, 03:30 UTC
[doc] fix link (#20417)
Signed-off-by: reidliu41 <reid201711@gmail.com>
Reid committed
Jul 3, 2025 at 15:21 UTC
359200f6ac9919140b995d5aa906854ba16e4870
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examples/offline_inference/profiling_tpu/README.md
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@@ -4,7 +4,7 @@ This script is used to profile the TPU performance of vLLM for specific prefill
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Note: an actual running server is a mix of both prefill of many shapes and decode of many shapes.
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-We assume you are on a TPU already (this was tested on TPU v6e) and have installed vLLM according to the [installation guide](https://docs.vllm.ai/en/latest/getting_started/installation/ai_accelerator/index.html).
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+We assume you are on a TPU already (this was tested on TPU v6e) and have installed vLLM according to the [Google TPU installation guide](https://docs.vllm.ai/en/latest/getting_started/installation/google_tpu.html).
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> In all examples below, we run several warmups before (so `--enforce-eager` is okay)
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