Does Quantization Make Models "Stupid"? A Home Lab Experiment
Everyone loves quantization. We all want to run massive 70B parameter models on consumer hardware, and squeezing a model down from 16-bit to 8-bit (or 4-bit) is usually the way we get there. The common wisdom is that the quality loss is "negligible."
But as an engineer, I don't like taking "common wisdom" for granted. I wanted to know specifically: Does quantization hurt a model's ability to reason through code?
I recently built a benchmarking suite to pit Qwen 2.5 7B (BF16) against its 8-bit quantized twin. The results were not what I expected.
The Motivation: The 16GB Ceiling
If you are running local LLMs on a modern GPU, you likely have limited VRAM to play with. My ceiling at home is 16GB. This is the awkward "middle child" of AI hardware. It's plenty for 7B models, but once you step up to 14B or larger, you have to start making compromises. You have to quantize.
I wrote a Python framework to find out exactly what those compromises cost us.
The Benchmark: Coding is Binary
Most LLM benchmarks rely on multiple-choice questions or "vibes." I prefer unit tests. Code either runs, or it doesn't.
I created full_precision_versus_quantized.py, a harness that runs the models through a gauntlet of 11 distinct coding challenges. These aren't "Hello World" scripts; they require multi-step logic:
- Dependency Resolution: Sorting build orders without circular logic.
- Water Tank Sim: Calculating volume changes with conditional logic (pumps cycling on/off).
- Hex Color Averaging: Bitwise manipulation of strings.
To ensure fairness, I forced deterministic behavior. I seeded PyTorch and CUDA (seed 42 + i) to ensure that every time the model ran, it faced the exact same initial conditions.
The Results: A Bloodbath ( mostly )
I ran 20 iterations per test for each model. Here is the breakdown:
| Test Name | 7B-BF16 (Full) | 7B-8bit (Quant) | Delta |
|---|---|---|---|
| Dependency Resolution | 100% | 0% | 🔻 100% |
| Hex Color Averager | 20% | 0% | 🔻 20% |
| Date Difference | 100% | 35% | 🔻 65% |
| Inventory Ledger | 100% | 60% | 🔻 40% |
| JSON Path Finder | 100% | 30% | 🔻 70% |
| Semantic Version Sort | 100% | 35% | 🔻 65% |
| Time String Parser | 100% | 55% | 🔻 45% |
| Water Tank Sim | 85% | 35% | 🔻 50% |
| URL Query Parser | 100% | 90% | 🔻 10% |
| Priority Processor | 80% | 100% | 🔼 20% |
| Manual Subnet Calc | 65% | 85% | 🔼 20% |
The "Stupid" Factor
Look at Dependency Resolution. The Full Precision model aced it every single time (100%). The 8-bit model failed every single time (0%).
This suggests that for tasks requiring strict, step-by-step logic chains, 8-bit quantization introduces enough noise to break the reasoning capability entirely. It’s not just "slightly worse"; it’s functionally broken for that specific task.
The Anomalies
Strangely, the 8-bit model actually outperformed the full precision model in Priority Processor and Manual Subnet Calc.
Why? My theory is that the "noise" introduced by quantization might act as a regularizer in very specific instances, preventing the model from "overthinking" simple arithmetic or sorting tasks. However, relying on this is risky.
Conclusion
If you are using local LLMs for creative writing or roleplay, 8-bit (and even 4-bit) quantization is likely fine. The "vibes" survive the compression.
However, if you are using Local LLMs for coding agent workflows or complex logic: Stick to Full Precision (BF16).
The drop from 100% to 0% on dependency resolution proves that quantization isn't free. It cuts corners on the neural pathways required for deep logic. If your GPU has the VRAM, don't compress your model just because you can.
Check out the full benchmark code and run it on your own hardware here: GitHub: full_precision_versus_quantized