Introduction
As deep learning models become larger and more complex, optimizing inference speed while maintaining accuracy is crucial. NVIDIA offers multiple solutions, including TensorRT, Triton Inference Server, and Triton with TensorRT, each catering to different deployment needs. This article explores these options, compares their performance, and recommends the best approach for various use cases.
Understanding the Options
1. TensorRT (Standalone)
TensorRT is NVIDIA’s high-performance deep learning inference SDK designed for GPUs. It optimizes deep learning models through:
– Layer fusion (merging operations to reduce memory and computation)
– Precision calibration (using FP16, INT8, or mixed precision for faster execution)
– Kernel auto-tuning (optimizing for specific hardware configurations)
When to Use TensorRT Standalone:
✔️ Best for single-model, high-throughput, low-latency applications
✔️ Ideal for edge devices or on-premise GPUs
❌ Not scalable for multi-model inference across different devices.
2. Triton Inference Server (ONNX)
Triton Inference Server is NVIDIA’s scalable and multi-framework serving solution, designed to support TensorFlow, PyTorch, ONNX, and TensorRT models.
It provides:
– Automatic batching to optimize inference performance
– Multi-GPU and multi-node inference for distributed deployments
– Model ensemble support to chain multiple models efficiently
When to Use Triton with ONNX Models:
✔️ Best for cloud-based inference across multiple GPUs/CPUs
✔️ Great when model conversion to TensorRT is not feasible
❌ May not fully utilize GPU optimizations like TensorRT
3. Triton Inference Server with TensorRT
This approach combines the scalability of Triton with the performance optimizations of TensorRT. Key benefits include:
– FP16/INT8 acceleration with TensorRT for speed improvements
– Automatic request batching within Triton for improved throughput
– Easier scaling across multiple GPUs or cloud instances
When to Use Triton with TensorRT:
✔️ Best for large-scale, high-performance inference workloads
✔️ Perfect for applications requiring model orchestration and dynamic batching
❌ Requires additional TensorRT model conversion upfront
Benchmarking Inference Speed Across NVIDIA AI Solutions
Approach | FP32 Inference Time | FP16 Inference Time |
TensorRT Standalone | 18 min | 12 min |
Triton Server (ONNX) | 4.5 min | 3 min |
Triton + TensorRT | 4.5 min | 3 min |
Challenges and Considerations
Challenge | TensorRT | Triton (ONNX) | Triton + TensorRT |
Model conversion effort | ✅ High | ✅ Low | ✅ Medium |
Multi-GPU scalability | ❌ No | ✅ Yes | ✅ Yes |
Dynamic batching | ❌ No | ✅ Yes | ✅ Yes |
Mixed precision (FP16/INT8) | ✅ Yes | ❌ No | ✅ Yes |
Deployment flexibility | ❌ Low | ✅ High | ✅ High |
Choosing the Right Approach
Scenario | Recommended Approach |
Real-time inference on a single GPU | ✅ TensorRT Standalone |
Serving multiple models across GPUs | ✅ Triton Inference Server (ONNX) |
Maximizing throughput for a large model | ✅ Triton with TensorRT |
Deploying an AI-powered SaaS solution | ✅ Triton (ONNX) |
Optimizing inference for edge devices | ✅ TensorRT Standalone |
Performance Analysis
After inference, we performed multiple analyses to validate embeddings.
- Distribution of Embedding Values
The histogram below shows the distribution of values across embeddings. Most values are centered around 0, indicating proper normalization.
- PCA Visualization of Embeddings
A PCA transformation was applied to reduce embeddings to 2D, making it easier to visualize clustering.
- Cosine Similarity Analysis
We computed cosine similarity across embeddings to identify image pairs with high similarity. A heatmap was plotted for the first 20 images.
Final Recommendations
- For fastest inference: Triton with TensorRT is the best choice for enterprise-grade deployments.
- For compatibility with multiple models and frameworks: Use Triton Server with ONNX.
- For simple, low-latency, single-GPU applications: Standalone TensorRT is a good fit.
Conclusion
Triton with TensorRT is the best combination for organizations running AI inference at scale. While standalone TensorRT is good for edge cases, and Triton with ONNX helps support multiple frameworks, the FP16 acceleration in TensorRT with Triton provides the best balance of speed and scalability.
