Python Model Quantization in 2026: INT4 Inference Without Accuracy Loss

Running LLMs Locally Without the Hardware Tax

Running a 70B parameter model locally used to require multiple A100s or a Mac Studio with 192GB of RAM. In 2026, you can run comparable models on a single RTX 4090 with 24GB of VRAM — if you quantize aggressively without destroying accuracy.

Post-training quantization has evolved dramatically. The techniques that barely worked in 2023 — naive INT8 rounding — have been replaced by calibration-aware methods that preserve perplexity within 1 to 2 percent of the full-precision model, even at 4 bits per weight.

The Quantization Spectrum in 2026

FP16 baseline gives you full accuracy but needs the most memory. INT8 with smoothquant techniques loses less than 0.3 percent accuracy while giving 1.8x speedup. INT4 with GPTQ or AWQ loses 0.5 to 1 percent accuracy but delivers 3.5x speedup. The real breakthrough is HQQ — Half-Quadratic Quantization — which achieves INT4 with only 0.2 to 0.5 percent accuracy loss and runs in minutes instead of hours.

HQQ reformulates quantization as a half-quadratic minimization problem. Instead of finding scaling factors that minimize L2 distance between original and quantized weights, HQQ introduces auxiliary variables that decouple the optimization. Each weight’s quantization error gets individually minimized, resulting in near-lossless compression.

Real-World Accuracy Measurements

On MMLU, a 70B Llama-3-based model quantized with HQQ INT4 scored 82.1, compared to 82.8 for the FP16 version. That’s a 0.7 point drop — within the variance of different evaluation runs. On HumanEval for coding tasks, the gap was even smaller: 78.2 versus 78.5.

The key insight: not all layers need the same bit width. Attention layers are more sensitive to quantization than feed-forward layers. Mixed-precision quantization, where attention stays at INT8 and MLP layers go to INT4, achieves most of the memory savings with even smaller accuracy loss.

Where Quantization Still Fails

Small models — under 3 billion parameters — degrade noticeably at INT4. The capacity isn’t there to absorb the quantization noise. Math and reasoning tasks suffer more than language generation. GSM8K accuracy drops 3 to 5 points at INT4 for 7B models, while conversation quality barely changes.

If you’re deploying a model for customer-facing chat, INT4 quantization is a safe bet in 2026. If you’re deploying for code generation or mathematical reasoning, test thoroughly on your specific use case.

The GGUF Ecosystem

For local inference, GGUF remains the dominant format. llama.cpp now supports HQQ-quantized models through the GGUF format, with K-quant variants that outperform naive 4-bit quantization by 2 to 3 perplexity points. The tooling has matured so you can convert any HuggingFace model to GGUF with a single command.

Quantization is no longer a compromise you make reluctantly. It’s the default for local deployment, and in 2026, the quality is good enough that most users won’t notice the difference.