Model compression helps deploy powerful neural networks on devices with limited memory and compute while keeping accuracy high. In this guide, we walk through the Top 10 Model Compression Techniques that practitioners rely on to reduce size, cut latency, and lower energy use. You will learn when each method shines, what trade offs to watch, and how techniques can be combined to get better results. From structured pruning to low rank factorization and distillation, the focus is on practical choices that scale. Each section explains core ideas, tooling tips, and evaluation advice so you can compress confidently.
#1 Pruning and Sparsification
Pruning removes insignificant weights to create sparse networks that run faster and use less memory. Start with magnitude pruning that zeros weights below a threshold, then move to structured pruning that removes entire channels or heads for hardware friendly speedups. Iterative prune and fine tune cycles usually outperform one shot removal. Use global thresholds across layers to balance sensitivity and avoid bottlenecks. Modern runtimes exploit block sparsity for predictable gains. Track sparsity versus accuracy curves, and restore a copy before each trial. Combine pruning with quantization to lock in speed and reduce memory pressure further.
#2 Post Training Quantization
Quantization maps full precision parameters and activations to lower bit widths such as int8 or int4. Post training quantization needs only a small calibration set to estimate activation ranges, which makes it fast and simple. Per channel scales generally preserve accuracy better than per tensor scales, especially for convolutional layers. Use symmetric quantization for weights and asymmetric ranges for activations when hardware supports both. Evaluate accuracy with representative inputs and monitor layer wise errors to locate fragile blocks. Pair with lightweight bias correction or rounding aware techniques to recover points. Dequantize sensitive layers selectively when accuracy is critical.
#3 Quantization Aware Training
Quantization aware training simulates low precision arithmetic inside the forward pass so the model learns to be robust to rounding noise. Use fake quantization nodes for weights and activations, and keep batch normalization fixed late in training to stabilize scales. Start from a well tuned full precision checkpoint, then train for a short schedule with a lower learning rate. Mix per channel weight quantization with per tensor activations to match hardware. Gradually tighten activation clipping during training to avoid saturation. Most tasks recover near full precision accuracy at int8, and vision transformers and LLMs often benefit from tailored layer rules.
#4 Knowledge Distillation
Distillation trains a compact student model to match a larger teacher through soft targets and intermediate hints. Use temperature scaled logits to provide richer gradients than hard labels, and blend teacher loss with ground truth to keep alignment. Feature distillation transfers attention maps or layer activations to help the student learn structure. Self distillation, where the model teaches itself over time, can remove the need for a separate teacher. Curriculum schedules that move from easy to hard examples stabilize training. Evaluate with both task metrics and calibration error, and consider ensembling multiple teachers to diversify useful signals.
#5 Low Rank Factorization
Many weight matrices have low intrinsic rank. Approximating them with products of thin matrices reduces parameters and compute while preserving expressiveness. Popular choices include SVD based factorization for convolutions and attention projections, and Tucker or CP decompositions for higher order tensors. Start by estimating rank using energy retention targets such as 90 to 95 percent, then fine tune to recover accuracy. Apply factorization to the largest layers first to maximize savings. For LLMs, low rank adapters inject small trainable updates on top of frozen backbones to adapt tasks with tiny footprints.
#6 Weight Sharing and Parameter Tying
Weight sharing replaces unique parameters with a small codebook of shared values or tied tensors. Techniques like hashing trick and k means quantization cluster similar weights so multiple connections reference the same entry. This reduces storage and can regularize training by enforcing consistency. Recurrent and transformer models also benefit from tying input and output embeddings. When applying sharing after training, add a short fine tuning phase to adapt to the discrete codebook. Measure perplexity or loss drift to ensure that rare features are not degraded. Combine with pruning for additional compression without large accuracy drops.
#7 Architecture Design for Efficiency
Designing models with compression in mind yields greater gains than retrofitting later. Mobile friendly blocks such as depthwise separable convolutions, group convolutions, and squeeze and excitation improve parameter efficiency. Transformer variants use sparse attention, linear attention, or low rank projections to cut quadratic costs. Neural architecture search can target latency on specific devices rather than only FLOPs. Use operator aware constraints so the search favors fused kernels available on your target runtime. Train with regularization that encourages sparsity and low rank structure so later compression steps are smoother. Always validate improvements with real end to end latency.
#8 Tensor Decomposition and Structured Matrices
Beyond simple low rank splits, impose mathematical structure that shrinks parameters and accelerates math. Circulant, Toeplitz, and butterfly matrices enable fast transforms with logarithmic factors while keeping expressive power. Tensor train and tensor ring decompositions break high dimensional tensors into sequences of small cores. These approaches suit very large embeddings and attention projections. Choose the structure that maps to efficient kernels in your framework, since theoretical speedups require matching implementations. Initialize from trained weights using factorization, then fine tune to adapt. Monitor numerical stability and add small weight decay to avoid drift during training.
#9 Dynamic Inference and Early Exiting
Dynamic networks adapt compute to input difficulty to save time and energy. Early exit classifiers attach heads to intermediate layers and stop when confidence crosses a threshold. Token pruning or routing in transformers drops uninformative tokens so later layers operate on smaller sequences. Conditional computation activates only a subset of experts or channels for each example. These ideas keep model capacity for hard cases while accelerating easy ones. Tune thresholds on a validation set to balance accuracy and latency targets. Log per sample compute and ensure service level objectives remain satisfied under real traffic distribution.
#10 Mixed Precision and Operator Fusion
Mixed precision executes operations in float16 or bfloat16 while keeping critical accumulations in higher precision. This reduces memory bandwidth and allows larger batch sizes without large accuracy loss. Calibrate loss scaling to prevent underflow and check that normalization layers remain stable. Combine with kernel fusion to collapse sequences of small operations into single launches that reduce overhead. Graph compilers can fold constants, reorder ops, and eliminate dead branches to streamline execution. Export models to runtimes that support quantized and mixed precision paths together so you can select best kernels per layer.