Does quantization make the AI slower?

Quantization often makes inference faster because less data needs to be moved between RAM and the CPU or GPU. However, if the model becomes too large for your RAM and starts using disk swap, performance will degrade significantly.

How do I know which quantization to choose for Stratat?

Check your system's total RAM first. If you have 32GB, start with Q4 or Q5. If you have 64GB or more, Q8 might be viable. Always test the lowest quantization that still provides coherent answers for your needs.

Is Q4 better than FP16?

Q4 is much smaller and uses less memory than FP16 (16-bit floating point). While FP16 offers higher theoretical accuracy, Q4 is often the only way to run large models on consumer hardware. The quality difference is often minimal for everyday tasks.

What Is Quantization in Local LLM Deployment for Stratat

Learn how quantization reduces model size and RAM usage for local AI on Stratat. Understand trade-offs in accuracy and speed with our guide.

Quantization is a technique that reduces the precision of a neural network's parameters to make large language models fit into consumer hardware. By converting high-precision data to lower precision, you can run models locally on the Stratat engine with significantly less memory and often faster inference times.

Why You Need Quantization for Local AI

The primary obstacle to running large language models (LLMs) locally is memory bandwidth and capacity. A standard 70-billion parameter model in its original 16-bit format requires roughly 140 gigabytes of VRAM or RAM. Most consumer workstations cannot support this. Quantization compresses this model by rounding numbers to fewer bits, allowing the model to fit into available system resources.

For users of the Stratat desktop app, this means you can deploy powerful models without needing enterprise-grade hardware. The trade-off is a slight reduction in the model's reasoning fidelity, but for many tasks, the difference is negligible compared to the gain in accessibility.

How Stratat Handles Quantization

When you load a model into the Stratat client, the engine applies the quantization scheme defined in the model file. Common schemes include Q4 (4-bit), Q5 (5-bit), and Q8 (8-bit). Lower numbers mean smaller file sizes and lower RAM usage, but potentially lower accuracy. Higher numbers preserve more of the original model's intelligence but demand more resources.

You can verify your system's capacity by checking your available RAM before selecting a model. If you have 32GB of RAM, a Q4 quantized 70B model might run, but a Q8 version would likely fail to load or swap heavily to disk, causing severe slowdowns. Always choose the lowest quantization level that still provides acceptable output quality for your specific use case.

Quantization Levels and Resource Usage

The table below illustrates how different quantization levels impact model size and required memory for a hypothetical 70B parameter model, helping you decide which Stratat model to download.

Quantization Level	Approximate Model Size	Minimum RAM Required	Quality Impact
Q4 (4-bit)	38-40 GB	40 GB	Minor loss
Q5 (5-bit)	45-47 GB	48 GB	Negligible
Q8 (8-bit)	70-72 GB	72 GB	Near-original

Quantization is the process of reducing the numerical precision of model weights to optimize memory usage and inference speed without significantly compromising performance.