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This vid helps get started w/ Nvidia Triton fast.

i. ## Choosing Between NVIDIA Triton and TensorRT: A Head-to-Head Comparison

Both NVIDIA Triton and TensorRT are powerful tools from NVIDIA for optimizing and deploying deep learning models. While they share some functionalities, they cater to different purposes and offer distinct advantages:

**NVIDIA Triton Inference Server:**

* **Focus:** **Deploying pre-trained models for inference** across various platforms, including CPUs, GPUs, and specialized hardware accelerators.
* **Strengths:**
* **Flexibility:** Supports various deep learning frameworks (TensorFlow, PyTorch, ONNX, etc.) and deployment options (cloud, on-premise).
* **Scalability:** Handles multiple concurrent requests efficiently, making it suitable for high-volume inference workloads.
* **Model management:** Provides features for model versioning, loading, and unloading models on the fly.
* **Security:** Offers security features like authentication and authorization for secure model access.
* **Weaknesses:**
* **Not ideal for model optimization:** While it can run optimized models, it doesn't have built-in optimization features like TensorRT.
* **Higher complexity:** The setup and configuration might require more effort compared to TensorRT.

**NVIDIA TensorRT:**

* **Focus:** **Optimizing and deploying deep learning models for high performance inference on NVIDIA GPUs.**
* **Strengths:**
* **Performance:** Achieves significant performance improvements through optimizations like quantization and tensor fusion.
* **Ease of use:** Offers a user-friendly API and tools for model conversion and deployment.
* **Integration:** Integrates well with other NVIDIA technologies like CUDA and cuDNN for further performance enhancements.
* **Weaknesses:**
* **Limited deployment flexibility:** Primarily designed for NVIDIA GPUs, limiting deployment options on other platforms.
* **Less framework support:** Supports a smaller range of deep learning frameworks compared to Triton.
* **Limited model management:** Lacks advanced model management features like Triton.

**Choosing Between Triton and TensorRT:**

The best choice depends on your specific needs and priorities:

* **Use Triton if:**
* You need to deploy pre-trained models across various platforms, including non-NVIDIA hardware.
* You require advanced model management features like versioning and on-the-fly model loading.
* You need to handle high-volume inference workloads with scalability.
* **Use TensorRT if:**
* Your primary goal is maximizing inference performance on NVIDIA GPUs.
* You want a user-friendly and efficient way to optimize and deploy pre-trained models.
* You primarily use deep learning frameworks supported by TensorRT.

In some cases, you might even consider using both tools together. For instance, you could leverage Triton for deploying models across various platforms while using TensorRT for optimizing models specifically for NVIDIA GPUs within that deployment.