PyTorch is a popular framework for building and running deep learning models.
Researchers and engineers use PyTorch because it is flexible and has a strong ecosystem.
PyTorch is commonly used for:
- Model development
- Training
- Fine-tuning
- Experimentation
- Exporting models
- Simple inference
Many model repositories provide PyTorch weights or code.
PyTorch uses tensors for numerical data. A tensor can live on CPU or GPU.
A common beginner mistake is mixing tensors on different devices. If the model is on GPU but the input is on CPU, the program may fail or move data slowly.
PyTorch is easy to debug because operations run in a direct, Python-friendly way. This helps development, but raw PyTorch may not be the fastest serving option for high-volume inference.
PyTorch can serve models directly, especially for smaller workloads. For large LLM serving, teams often use specialized engines that optimize batching, KV cache, and scheduling.
- PyTorch is a core deep learning framework.
- It is excellent for development and experimentation.
- Production serving may need additional optimization.
- Device placement matters.
- Know whether tensors are on CPU or GPU.
- Use
torch.no_grad()or inference mode for inference code. - Benchmark raw PyTorch against serving engines for LLM workloads.
- Keep model loading and request handling separate.