Comments (4)
Yes, you can serve multiple different models, multiple instances of the same model, or multiple instances of multiple models, on one or more CPUs and GPUs, simultaneously.
The docs discuss it here: https://docs.nvidia.com/deeplearning/sdk/tensorrt-inference-server-master-branch-guide/docs/model_configuration.html?highlight=batching#instance-groups
As does this blog post (in the Performance section): https://devblogs.nvidia.com/nvidia-serves-deep-learning-inference/
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@deadeyegoodwin, thanks for your prompt reply
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@deadeyegoodwin for a large model that doesn't fit on a single GPU, how does triton split the model onto mutlple GPUs then?
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@deadeyegoodwin for a large model that doesn't fit on a single GPU, how does triton split the model onto mutlple GPUs then?
Triton may not support this case based on my experience.
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Related Issues (20)
- Significant latency between COMPUTE_END and REQUEST_END HOT 1
- Issue on page /user_guide/response_cache.html HOT 4
- triton can provide request transmission in the form of a file stream? HOT 1
- In Triton, multiple instances of the same model load multiple copies of the model file into memory, leading to CUDA out of memory. Why can't multiple instances share the same model file? HOT 2
- Will tensorRT backend be compatible with tensorRT 9.1+ ? HOT 8
- Sidecar Container CPU Throttling when Deploying using Triton with ONNX Backend on Kubernetes HOT 2
- Missing :te header when using envoy proxy with grpc-web filter HOT 5
- Dynamic batching does not work properly with python backend HOT 1
- [400] 'MODEL' version 1 is not at ready state even if /v2/health/ready has succeeded HOT 6
- Conda Package for Inference Server HOT 4
- Incomplete LLM response HOT 3
- After load a model, Triton server suddenly not work that it shows CUDA failed to initialize. Unknown error (error 999). HOT 2
- After load a model, Triton server suddenly not work that it shows CUDA failed to initialize. Unknown error (error 999). HOT 7
- [CMake error] Building Triton on arm64 machine using build.py HOT 4
- Errors from tutorial : Deploying a vLLM model in Triton HOT 3
- c++ developer tools API - segmentation fault with multithreaded calls of AsyncInfer HOT 10
- only cpu have a error
- How to generate rawInputContents with multiple dimensions and multiple input parameters in GRPC? HOT 8
- Set cuda_memory_pool_byte_size to solve CNMEM_STATUS_OUT_OF_MEMORY HOT 3
- Exception serializing request - dealing with large input HOT 4
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