How Quantization Reduces Memory in Edge LLMs

Quantization is a technique that simplifies the precision of 

 (LLM) parameters, enabling them to run on devices with limited memory and processing power. By converting 32-bit floating-point numbers (FP32) into smaller formats like 16-bit, 8-bit, or 4-bit integers, quantization significantly reduces the memory footprint of models. For example, a 7-billion-parameter model can shrink from 28 GB in FP32 to just 7 GB with INT8 or 3.5 GB with INT4 - cutting memory use by up to 87.5%.

This approach is critical for deploying LLMs on edge devices, such as smartphones or IoT hardware, which typically have 1–8 GB of memory. While quantization can slightly impact model accuracy, methods like Quantization-Aware Training (QAT) and Post-Training Quantization (PTQ) help balance memory efficiency with acceptable performance. Dynamic quantization and low-bit formats like INT4 further optimize memory usage for constrained environments.

Quantization transforms LLM deployment by making advanced AI accessible on resource-limited devices while maintaining practical accuracy levels.

Main Quantization Methods for Memory Reduction

After discussing the balance between memory usage and accuracy, let's dive into the specific quantization techniques that make these reductions possible. Edge LLMs rely on three primary quantization methods, each offering unique benefits and trade-offs. Here's a closer look at these methods to help you optimize your model for edge deployment.

Post-Training Quantization (PTQ) simplifies the process by converting pre-trained models into lower-precision formats after training is finished 

. Essentially, you take your trained model, calibrate it to determine scaling factors for lower-precision operations, and then export the quantized version to create an optimized deployment engine 

. While PTQ is straightforward, it can lead to accuracy drops, especially with more aggressive quantization levels 

. However, it’s particularly effective for large, robust models where minor performance dips are acceptable.

Quantization-Aware Training (QAT) tackles the accuracy issue by simulating quantization effects during training. This approach helps the model adapt to lower precision, often resulting in better accuracy compared to PTQ 

. The process involves three key steps: modifying the model to simulate quantization numerics, fine-tuning it under these conditions, and converting the layers to the desired lower bit-widths 

. For example, in a case study with Llama3-8B fine-tuned on the C4 dataset, QAT reduced perplexity by 16.8% (19.403 vs. 23.316) compared to PTQ when deployed to XNNPACK, while maintaining the same model size (3.881 GB) and similar inference speeds 

. QAT can recover up to 96% of the accuracy loss and 68% of the perplexity degradation seen with PTQ, but it does demand more computational power and training data 

Dynamic quantization takes a more adaptive approach by adjusting precision levels during inference, calculating optimal clipping ranges for activations as needed 

. While this method boosts accuracy, it tends to be computationally heavier. On the other hand, low-bit quantization formats like INT8 and INT4 provide aggressive memory savings - up to a 79% reduction in energy consumption compared to FP16 - and can cut inference latency by up to 69% under ideal conditions 

. However, extreme quantization may introduce dequantization overhead, and its benefits can taper off at very low precision levels 

When deciding between these methods, think about your hardware limitations, performance goals, and available resources. PTQ is a great choice for fast deployment, while QAT is better suited for scenarios where accuracy is critical. Dynamic and low-bit quantization are ideal for maximizing memory efficiency, though they come with added complexity. Use these insights to guide your optimization strategy.

Now that you're familiar with the different quantization methods, let's dive into the practical steps for implementing quantization in your edge LLM deployment. This process helps you balance memory efficiency and accuracy, guiding you from selecting a method to validating the final model on your target hardware.

Step 1: Choose the Right Quantization Method

Start by assessing your application's needs and the constraints of your deployment environment. The goal here is to strike the best balance between accuracy, model size, computational efficiency, and hardware compatibility.

Once you've selected the method that aligns with your goals, the next step is to pick the right tool for the job.

Step 2: Select the Best Quantization Tools

The tools you choose should match your quantization method, model architecture, and hardware requirements. Here’s a quick overview of some popular options:

After selecting the tools that best fit your needs, you’re ready to move on to converting and testing your model.

This is where you apply the quantization principles to your model and ensure it performs well on your target device. The key is to balance memory savings with acceptable accuracy.

When evaluating the model, go beyond basic accuracy metrics. Look at task-specific measures that align with your application's goals. Document improvements in memory usage and speed, as well as any accuracy trade-offs, to guide future optimization efforts.

Quantization methods are all about finding the sweet spot between cutting down memory usage and maintaining performance. When deploying quantized large language models on edge devices, the challenge lies in balancing memory reduction with accuracy.

Different quantization levels offer varying degrees of memory savings and performance benefits. Here's a breakdown of how they compare:

 hits the right balance between memory savings and performance. It's particularly suitable for memory-limited environments like IoT devices.

 pushes the boundaries of compression, offering substantial memory savings without a major dip in performance. Techniques like Sparse-Quantized Representation (SpQR) have shown that models can be compressed to around 4 bits per parameter with less than a 1% difference in perplexity 

. This makes it a solid choice for edge devices where every megabyte matters.

However, when quantization goes below 4 bits - such as 3-bit or lower - the trade-offs become more pronounced. Accuracy can drop significantly, making these formats more suited for experimental or proof-of-concept use rather than production.

The theoretical gains from quantization translate into tangible improvements on edge hardware. For instance, in the case of the ResNet CNN model, 

INT8 post-training quantization doubles inference speed while halving model size

. Similarly, in large language model (LLM) deployments, 8-bit quantization maintains performance levels comparable to their full-precision counterparts, ensuring reliability in production.

 steps in as a game-changer. It delivers critical memory savings while keeping performance losses minimal. Interestingly, in scenarios where memory usage is similar, models with lower bit precision but larger parameter scales often outperform smaller, unquantized models.

Advanced methods like SpQR take this a step further. By using small group sizes with bilevel quantization and isolating outlier weights at 16-bit precision, SpQR retains essential model behavior while achieving aggressive compression. For example, storing one 32-bit scaling factor per 64 parameters adds 0.5 bits per parameter, whereas double quantization reduces this to just 0.127 bits per parameter 

The impact of quantization is particularly striking on GPUs and CPUs designed for low-precision computations. Edge devices with hardware acceleration optimized for these formats see the largest performance boosts. On the other hand, older or less specialized hardware may not fully leverage these benefits, highlighting the importance of matching the quantization approach to the hardware capabilities.

When it comes to deploying quantized large language models (LLMs) on edge devices, success hinges on meticulous planning and smart decisions. From understanding hardware limitations to selecting the right models and establishing robust monitoring systems, these practices can make all the difference in ensuring consistent performance in production environments.

Finding the Right Size vs. Quality Balance

Balancing model compression with acceptable performance is a critical aspect of edge deployment. Quantization, for instance, can cut energy consumption by as much as 79% compared to FP16, making it a game-changer for devices running on batteries. However, over-quantizing can introduce dequantization overhead and negatively impact precision.

To strike this balance, assess the accuracy needed for your specific task and confirm that your edge device supports the required precision (e.g., mixed-precision matrix multiplication

). Models with uniformly distributed weights tend to handle quantization better, whereas smaller models are more prone to precision loss.

For even better results, consider combining multiple compression techniques. A great example is Microsoft’s Phi-3 mini, which packs just 3.8 billion parameters but achieves an impressive 69% on the MMLU knowledge benchmark by employing various optimization strategies

Pre-quantized models can save you significant time and computational resources. Look for models compatible with mobile runtimes like 

 Mobile, ensuring that these runtimes support low-precision operations. Tools such as 

, and llama.cpp are designed to leverage CPU instruction sets like AVX2 and AVX-512 (for x86 processors) or NEON (for ARM processors) to speed up integer arithmetic

Before deployment, double-check that your drivers and libraries support the low-precision operations required by your model. If not, the system might default to slower CPU computations. For devices with dedicated AI hardware, tailor your quantization strategy to align with the hardware's unique capabilities

. These steps complement earlier-discussed quantization methods and help ensure smooth deployment.

If you’re looking to deepen your understanding of quantization and edge deployment, Newline’s AI bootcamp offers a hands-on approach. This program covers the essentials of transformer-based LLMs while equipping you with practical strategies to optimize and deploy models on resource-constrained devices. Through project-based tutorials, you’ll learn to implement quantization techniques, evaluate performance trade-offs, and use industry-standard tools and frameworks.

Additionally, Newline hosts weekly live workshops on AI automation, taught by experts from companies like Google, 

. These sessions focus on advanced coding techniques and agent-based automation. The curriculum includes complete source code, 

, and downloadable resources to ensure you’re prepared for real-world applications. By enrolling in the Pro Plan, you gain access to an extensive library of video tutorials, guides, books, and masterclasses. Plus, the platform’s Discord community offers a collaborative space to troubleshoot challenges, exchange optimization tips, and connect with fellow developers.

Quantization has become a game-changer for making large language models (LLMs) practical on edge devices. By reducing the precision of model parameters, developers can significantly lower memory usage while still achieving acceptable performance. For instance, GPT-3, with its massive 175 billion parameters and a memory requirement of 350 GB in FP32, can be compressed to just 3 or 4 bits per parameter - all without sacrificing accuracy 

When choosing a quantization method, consider your specific goals. 

 helps retain model performance within 1% of the original network 

. These options give developers the flexibility to optimize deployments effectively.

The steps we've outlined provide a clear path to implementing quantization. Keep in mind that the decision between per-layer and per-channel granularity can have a significant impact on your outcomes 

The key to successful edge deployments lies in carefully balancing memory efficiency with performance. Modern quantization techniques allow for impressive compression ratios while maintaining the intelligence and functionality of the models. Whether you're using pre-quantized models or designing custom solutions, today's tools make it possible to run LLMs on devices that once seemed too limited in resources.

As shown in our examples, quantization transforms deployment challenges into opportunities. It enables powerful LLMs to function on smartphones, IoT devices, and other memory-constrained platforms, where every byte counts. By applying these strategies, you can harness the full potential of edge AI and bring advanced capabilities to devices that were previously out of reach.

How does quantization affect the performance of large language models on edge devices?

Quantization can have a small effect on the accuracy of large language models when they're used on edge devices, but modern techniques work to keep this impact minimal. By lowering the precision of model parameters - such as switching to 8-bit or even smaller representations - both memory usage and computational demands are greatly reduced.

 are designed to retain most of the model's performance, ensuring only a slight dip in accuracy. This trade-off is generally worth it for edge deployments, as it allows the model to run efficiently while still delivering high-quality outputs.

What are the differences between Post-Training Quantization (PTQ) and Quantization-Aware Training (QAT) for deploying LLMs on edge devices?

Post-Training Quantization (PTQ) and Quantization-Aware Training (QAT) serve the same goal - making models more efficient - but they differ in implementation, accuracy, and complexity.

 is applied after a model has been fully trained. It’s a quicker and simpler approach, making it a good fit for scenarios where fast deployment on edge devices is a priority. However, this method can lead to some accuracy loss, especially with larger models.

, by contrast, integrates quantization into the training process itself. While it demands more resources and time, it typically preserves accuracy much better. This makes it a better choice for deploying large language models on edge devices where performance is a key concern.

Ultimately, the decision between PTQ and QAT depends on the specific requirements of your project - whether you prioritize ease and speed or accuracy and performance.

What is dynamic quantization, and when is it the best choice for edge device deployment?

Dynamic quantization is a method that uses lower-precision formats, like 8-bit integers, for model weights and activations during inference. Unlike static quantization, it skips the need for pre-quantizing data during training or calibration, making it easier to apply.

This approach is particularly handy when deploying large language models (LLMs) on edge devices. It cuts down memory usage and boosts inference speed, all while preserving model accuracy. If you're looking for a way to optimize efficiency and performance without the hassle of retraining or calibration, dynamic quantization is a solid choice.

AUTHOR

zaoyang

Explore how quantization optimizes large language models for edge devices by reducing memory usage while maintaining performance and accuracy.

Learn

The newline Guide to Building Your First GraphQL Server with Node and TypeScript

Teach

Amelia Wattenberger

Author of Fullstack D3

Community

How Quantization Reduces Memory in Edge LLMs

Tags

Responses (0)

Masterclasses

Tutorials

Fullstack React with TypeScript