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robertgshaw2-neuralmagic avatar robertgshaw2-neuralmagic commented on June 29, 2024

This is not a bug. We pre-allocate memory for the KV cache based on the following formula:

  • (total memory * gpu_util) - weights - maximum_activation_size`

The maximum_activation_size is measured from peak memory from running a profile run with your model_max_length. So you are seeing memory go up during the profiling run and then drop back down

from vllm.

majestichou avatar majestichou commented on June 29, 2024

This is not a bug. We pre-allocate memory for the KV cache based on the following formula:

  • (total memory * gpu_util) - weights - maximum_activation_size`

The maximum_activation_size is measured from peak memory from running a profile run with your model_max_length. So you are seeing memory go up during the profiling run and then drop back down

Small context lengths may consume more memory than large context lengths. What is the reason?

from vllm.

robertgshaw2-neuralmagic avatar robertgshaw2-neuralmagic commented on June 29, 2024

This is not a bug. We pre-allocate memory for the KV cache based on the following formula:

  • (total memory * gpu_util) - weights - maximum_activation_size`

The maximum_activation_size is measured from peak memory from running a profile run with your model_max_length. So you are seeing memory go up during the profiling run and then drop back down

Small context lengths may consume more memory than large context lengths. What is the reason?

We allocate memory for the KV cache size and weights based on the maximum potential activation size.

  • longer_content ==> large maximum activation size ==> less space for kv cache ==> less memory allocated

from vllm.

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