How do you determine the appropriate quantization precision levels for your Large language models?

Quantization is a crucial technique in deep learning and signal processing that helps reduce model size and computation cost by representing numbers with fewer bits. Choosing the right quantization level is a balance between accuracy and efficiency. This article explores key considerations for deciding your quantization levels.

Understanding Quantization Levels

Quantization levels refer to the number of discrete values that can represent continuous numerical data. The most common quantization schemes include:

• W8A8: A symmetric quantization scheme where both weights and activations use 8-bit precision. Example: SmoothQuant.
• W4A16: A mixed-precision approach where weights are quantized to 4 bits while activations remain at 16 bits, balancing efficiency and accuracy. Example: AWQ (Activation-aware Weight Quantization).
• W4A4: A fully 4-bit quantization for both weights and activations, significantly reducing memory and compute costs but often requiring careful calibration to maintain accuracy. Example: Bitsandbytes 4-bit quantization.

Analyze Which One Fits Your Case: Memory-Bound or Compute-Bound Scenario

We all wish quantization techniques could accelerate AI models without any performance drop. However, in reality, the lower the quantization levels you choose, the more likely it is that model performance will degrade significantly. Therefore, understanding the bottleneck of your application is essential when deciding which part is sacrificable.

• Memory-bound scenario: If your application is bounded by the bandwidth, it means the GPU has to push around the weights in memory, and this is essentially what limits how many tokens per second you can generate.
• Compute-bound scenario: If your application is constrained by computational power (e.g., real-time inference with strict latency requirements), choosing a quantization method that accelerates computation without excessive accuracy loss is crucial.

Crucial: A Memory-Bound Method Isn’t Always Beneficial for a Compute-Bound Application

A memory-bound method typically quantizes model weights to very low precision levels, such as INT4, to reduce memory footprint and bandwidth requirements. However, if your system is compute-bound, this approach might not provide the expected performance gains. Here’s why:

• Increased Computational Complexity: While INT4 reduces memory usage, certain hardware accelerators (e.g. Nvidia Ampere series) lack optimized support for extremely low-bit computations. As a result, model weights stored in INT4 may need to be converted to higher precision like FP16 before operations, negating the intended efficiency gains.
• Limited Parallelism: Compute-bound applications often rely on high-throughput operations that are optimized for 8-bit or 16-bit integer arithmetic. Using INT4 may disrupt these optimizations, leading to inefficient hardware utilization.
• Quantization Overhead: Lower-bit quantization methods sometimes introduce additional overhead, such as scaling operations or dequantization steps, which can increase latency in compute-bound scenarios.

Thus, selecting a quantization method should align with the specific hardware constraints and application needs rather than solely focusing on reducing memory usage.