What is Distilled and Quantized Models?

Researchers make advancements in machine learning every day, and I recently explored two of them: distillation and quantization. While I was familiar with quantization, distillation was entirely new to me. It’s exciting to see how we can create more efficient AI systems that maintain strong performance, and I’ve captured a brief overview of both concepts here.

Bigger isn’t always better. With the rise of massive neural networks—often containing billions of parameters—deployment can be a huge challenge. Memory constraints, inference latency, and energy consumption all become critical bottlenecks, especially on resource-limited devices.

That’s where two popular optimization strategies—knowledge distillation and model quantization—come into play. Both aim to make models more efficient and easier to deploy, but they go about it in distinct ways.

What is Knowledge Distillation?

Knowledge distillation is a method that transfers the “knowledge” from a large, complex “teacher” model to a smaller, more efficient “student” model. Traditional training uses only the final labels (e.g., the class “cat” or “dog” in an image classification problem). In contrast, distillation also leverages the teacher model’s “soft” predictions (or logits). These predictions aren’t limited to a single class but reveal how the teacher distributes its confidence across multiple classes.

Why does that matter? It’s akin to learning with more context. Instead of saying, “That’s a dog,” the teacher might indicate something like, “I’m 80% sure it’s a dog, 15% sure it’s a wolf, and 5% sure it’s a fox.” This additional information helps the student model understand why a particular label is correct, rather than just memorizing the outcome.

This approach often allows the smaller student network to closely match the performance of the larger teacher, while enjoying big gains in speed and memory efficiency.

Benefits of Distilled Models

1. Speed: By design, the student is smaller and can run faster during inference.
2. Size: Fewer parameters lead to reduced storage requirements, making it easier to deploy on devices with limited memory.
3. Performance: A well-distilled student can retain a surprising amount of the teacher’s performance.

Typical Use Cases

• Edge and Mobile AI: Where computational resources and memory are limited.
• Large-Scale Deployments: Even with powerful servers, scaling out billions of queries can be expensive. Smaller models save on both cost and energy.

What is Model Quantization?

Model quantization takes a different approach. Instead of transferring knowledge from one model to another, quantization focuses on reducing the numerical precision of a single model’s weights and activations. Deep learning frameworks often use 32-bit floating-point (FP32) numbers by default, but many real-world applications don’t need that level of precision to make accurate predictions.

By lowering the precision—commonly to 8-bit integers (INT8) or 16-bit floating-point (FP16)—we can significantly shrink the model’s size and reduce computation time, especially on hardware designed for low-precision arithmetic.

Quantization essentially involves two key steps:

1. Calibration: Determine the range of values (weights/activations) that need to be mapped to a lower-precision format.
2. Scaling & Rounding: Apply a scaling factor so that these values fit neatly into the new numeric range (e.g., from FP32 down to INT8). Then round or clip values as needed.

Benefits of Quantized Models

• Smaller Footprint: Storing weights in 8 bits vs. 32 bits cuts memory usage by 75%.
• Faster Inference: Low-precision integer operations are often faster on CPUs and GPUs, and some specialized accelerators (like tensor cores) provide speedups specifically for INT8 or FP16.
• Compatibility: Most deep learning frameworks support quantization workflows, making it relatively straightforward to implement.

Trade-Offs

• Accuracy Drop: In some cases—especially if you quantize down to very low bits—performance can suffer. Proper calibration and fine-tuning typically mitigate this.
• Hardware Support: Your hardware must support low-precision arithmetic to see big speedups.

Can They Be Combined?

Absolutely. You can start by training a distilled student model and then quantize that student for an additional boost in efficiency. This two-step process yields a model that’s both structurally smaller (due to distillation) and numerically smaller (due to quantization), making it ideal for highly constrained environments.

Conclusion

Distilled and quantized models each offer a path to efficient inference but tackle the problem from different angles. Distillation compresses and transfers knowledge from a big teacher model into a leaner student, helping retain accuracy in a smaller network. Quantization optimizes the numeric representation of an existing model’s parameters and activations, reducing memory and speeding up operations.

Whether you choose one method or combine both, the goal remains the same: deliver near state-of-the-art performance with reduced computational overhead. As machine learning applications continue to expand to resource-limited devices and scale to billions of requests, these techniques are becoming increasingly vital for practical, sustainable AI deployment.