Table of Contents
What's the Difference?
The simplest analogy: Training is like studying for an exam, while inference is taking the exam. During training, the model learns patterns from vast amounts of data. During inference, it applies that knowledge to make predictions on new data.
Training = Learning
- • Process millions of examples
- • Adjust billions of parameters
- • Takes hours to weeks
- • Requires massive GPU power
- • Done once or periodically
Inference = Using
- • Process one input at a time
- • Parameters are frozen
- • Takes milliseconds to seconds
- • Lower GPU requirements
- • Runs 24/7 in production
Common Misconception
Many assume inference is "free" after training. In reality, inference often accounts for 90% of total AI compute costs because it runs continuously at scale, while training is a one-time expense.
What is AI Training?
Training is the process of teaching an AI model to recognize patterns in data. The model starts with random weights and gradually adjusts them based on feedback from millions of examples.
The Training Process
Training Characteristics
What is AI Inference?
Inference is using a trained model to make predictions on new data. When you ask ChatGPT a question, generate an image with Stable Diffusion, or get product recommendations — that's inference.
The Inference Process
Inference Characteristics
Side-by-Side Comparison
| Aspect | Training | Inference |
|---|---|---|
| Purpose | Create/improve the model | Use the model to make predictions |
| Compute Intensity | Very High | Low to Medium |
| Memory Usage | High (gradients, optimizer states) | Lower (model weights only) |
| Latency Priority | Throughput matters more | Latency is critical |
| Batch Size | Large batches (32-4096) | Small batches (1-32) |
| Frequency | Periodic (days/weeks) | Continuous (24/7) |
| Data Flow | Forward + Backward pass | Forward pass only |
| Precision | FP32/FP16/BF16 | INT8/INT4/FP16 |
Hardware Requirements
Different GPUs excel at different tasks. Training typically needs more VRAM and compute power, while inference prioritizes latency and cost-efficiency.
| GPU | Training | Inference | Notes |
|---|---|---|---|
| NVIDIA H100 | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | Training: Best for large-scale training Inference: Overkill for most inference |
| NVIDIA A100 80GB | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | Training: Excellent for LLM training Inference: Great for batch inference |
| NVIDIA A10G | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | Training: Good for fine-tuning Inference: Optimized for inference |
| NVIDIA T4 | ⭐⭐ | ⭐⭐⭐⭐ | Training: Limited for training Inference: Cost-effective inference |
| RTX 4090 | ⭐⭐⭐⭐ | ⭐⭐⭐⭐ | Training: Great for personal training Inference: Good local inference |
Pro Tip: Right-Size Your Hardware
Don't use H100s for inference if T4s will do. The cost difference is 10x+. Similarly, don't try to train large models on consumer GPUs — you'll spend more time waiting than working.
Cost Comparison
| Provider | Training GPU | Inference GPU |
|---|---|---|
| AWS (p4d.24xlarge) | $32.77/hr | $3.06/hr (g5.xlarge) |
| Google Cloud (a2-highgpu) | $26.45/hr | $2.48/hr (g2-standard) |
| Azure (NC24ads A100) | $27.20/hr | $2.52/hr (NC4as T4) |
| Griddly Cloud | $0.80/hr (A100) | $0.25/hr (A10G) |
Optimization Tips
Training Optimization
- Use mixed precision (FP16/BF16) for 2x speedup
- Enable gradient checkpointing to reduce memory
- Use DeepSpeed or FSDP for multi-GPU training
- Consider LoRA/QLoRA for efficient fine-tuning
Inference Optimization
- Quantize models to INT8/INT4 for 2-4x speedup
- Use vLLM or TensorRT-LLM for LLM serving
- Implement batching for throughput optimization
- Use KV-cache for autoregressive models