Quantization Techniques for LLMs: FP16 to INT4 and GPTQ

An 8-billion parameter model stored in FP16 precision requires roughly 16 GB of VRAM just to load. To run these models on consumer GPUs or edge devices, we must reduce their memory footprint. Quantization—representing model weights with fewer bits (like 8-bit or 4-bit integers instead of 16-bit floats)—is the key to democratizing LLM deployments.

Post-Training Quantization (PTQ) vs. Quantization-Aware Training (QAT)

QAT incorporates quantization constraints during training, yielding high accuracy but at high compute costs. PTQ applies quantization to an already-trained model. Modern PTQ algorithms like GPTQ and AWQ achieve near-lossless compression down to 4 bits using a small calibration dataset.

How GPTQ Compresses Weights

GPTQ uses second-order Taylor expansions to optimize the quantized weights row-by-row. By adjusting remaining weights to compensate for the quantization error of processed weights, GPTQ maintains high perplexity scores even at extreme compression rates.

GGUF and CPU Inference

GGUF is a binary file format designed for fast CPU-based inference using llama.cpp. By quantizing models to mixed-precision formats (e.g., 4-bit weights with 8-bit key-value caches), developers can run local LLMs efficiently on everyday hardware.

Production PyTorch LoRA Adaptor Loop

Below is a production-grade PyTorch implementation showing how Low-Rank Adaptation (LoRA) projection layers are declared and computed mathematically during model forward passes:

import torch
import torch.nn as nn
import math

class LoRALinear(nn.Module):
    def __init__(self, in_features: int, out_features: int, r: int = 8, lora_alpha: float = 16.0):
        super().__init__()
        self.base_layer = nn.Linear(in_features, out_features)
        self.r = r
        self.alpha = lora_alpha
        self.scaling = lora_alpha / r

        # LoRA projection matrices
        self.lora_A = nn.Parameter(torch.zeros(r, in_features))
        self.lora_B = nn.Parameter(torch.zeros(out_features, r))
        
        # Initialize parameters
        nn.init.kaiming_uniform_(self.lora_A, a=math.sqrt(5))
        nn.init.zeros_(self.lora_B)
        self.base_layer.weight.requires_grad = False # Freeze base

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        base_out = self.base_layer(x)
        lora_out = (x @ self.lora_A.t() @ self.lora_B.t()) * self.scaling
        return base_out + lora_out

Model Performance & Retrieval Profiles

Below is the performance comparison profile for our processing pipeline tested in staging against sanitized validation datasets:

US & UK Regulatory Standards for Artificial Intelligence

Deploying machine learning models in the US and UK markets requires strict alignment with local regulatory frameworks. In the United States, applications must respect the guidelines set by the FTC regarding algorithmic transparency, alongside the Executive Order on Safe, Secure, and Trustworthy AI. In the United Kingdom, AI systems must comply with the UK General Data Protection Regulation (UK GDPR), which enforces strict rules on automated profiling (under Article 22). Conducting bias auditing and maintaining explainable decision paths is critical to avoiding compliance sanctions in both jurisdictions.