What is Inference?
Inference (in machine learning) is the process of using a trained model to make predictions or generate outputs on new, unseen data, representing the deployment phase where learned patterns are applied to real-world inputs without updating model parameters.
Quick Facts
| Full Name | Model Inference |
|---|---|
| Created | Concept fundamental to machine learning since 1950s |
| Specification | Official Specification |
How It Works
Model inference is the operational phase that follows training, where a machine learning model applies its learned weights and biases to process new inputs and produce predictions. Unlike training, which involves computationally expensive backpropagation and gradient updates, inference performs only forward passes through the network. This distinction makes inference significantly faster and less resource-intensive. Modern inference optimization techniques include quantization (reducing numerical precision), pruning (removing unnecessary connections), knowledge distillation (transferring knowledge to smaller models), and batching (processing multiple inputs simultaneously). Inference can be deployed on various platforms including cloud servers, edge devices, mobile phones, and specialized hardware accelerators like GPUs, TPUs, and NPUs. Modern LLM inference optimization techniques include vLLM (PagedAttention for efficient memory management), TensorRT-LLM (NVIDIA's optimized inference engine), Speculative Decoding (using smaller models to accelerate generation), and Continuous Batching for maximizing throughput. These optimizations can reduce inference costs by 2-10x while maintaining output quality.
Key Characteristics
- Forward propagation only without backpropagation or weight updates
- Latency optimization as a primary concern for real-time applications
- Batch processing to maximize throughput and hardware utilization
- Model quantization reducing precision from FP32 to INT8 or lower
- Memory efficiency through optimized model loading and caching
- Deterministic outputs when temperature and sampling are controlled
Common Use Cases
- Real-time prediction services for recommendation systems and fraud detection
- Edge deployment on IoT devices, smartphones, and embedded systems
- API services providing model predictions as cloud-hosted endpoints
- Autonomous systems including self-driving vehicles and robotics
- Interactive AI applications like chatbots and virtual assistants
Example
Loading code...Frequently Asked Questions
What is the difference between training and inference in machine learning?
Training is the process of teaching a model by adjusting its parameters using labeled data and backpropagation. Inference is using the trained model to make predictions on new data without updating parameters. Training is computationally expensive and done once, while inference is faster and performed repeatedly in production.
Why is inference optimization important for LLMs?
LLM inference is expensive due to large model sizes and autoregressive generation. Each token requires a full forward pass, and memory bandwidth often becomes the bottleneck. Optimization techniques like quantization, KV-cache, batching, and speculative decoding can reduce costs by 2-10x while maintaining output quality.
What is quantization and how does it speed up inference?
Quantization reduces the numerical precision of model weights from 32-bit floating point (FP32) to lower precisions like INT8 or INT4. This reduces memory usage and increases throughput since smaller data types require less memory bandwidth and can leverage faster integer arithmetic, often with minimal quality loss.
What is the KV-cache in LLM inference?
The KV-cache (Key-Value cache) stores the key and value tensors from previous tokens during autoregressive generation. Without caching, each new token would require recomputing attention for all previous tokens. The KV-cache trades memory for computation, significantly speeding up generation but requiring careful memory management.
How does batch size affect inference performance?
Larger batch sizes improve hardware utilization and throughput by processing multiple requests simultaneously. However, they increase latency for individual requests and require more memory. The optimal batch size balances throughput requirements, latency constraints, and available GPU memory. Continuous batching allows dynamic batch adjustment.