When an AI invents a name, a number, or a birthplace, it usually isn't a flaw in the model, it's the provider running it at reduced precision to save money on hardware. Here's the plain-English difference between running a model at 16-bit, 8-bit and 4-bit, and why it decides whether you get the truth or a confident guess.
Every number inside a large language model is stored as a floating-point value, a decimal held to a certain precision. Those millions of tiny numbers (the "weights") encode the relationships between every concept the model knows. How precisely you store them decides how faithfully the model can reason. Think of it like measuring a room: the finer your instrument, the truer your answer.
The industry's rush to 4-bit isn't innovation, it's corner-cutting. Done to the extreme, it stops being measurement and becomes guessing. You're not being sold AI; you're being sold a hallucination engine with a confident voice.
Every weight keeps near-perfect fidelity. The model's understanding of how concepts relate stays intact. This is what SARAH runs, on DGX hardware built for exactly this.
You lose some detail: numbers get rounded, and some relationships between concepts begin to blur. Cheaper to run; usable for many tasks, but errors creep in over long or precise work.
Weights are compressed so aggressively that the mathematical relationships between concepts fracture. You're no longer measuring, you're approximating. This is where "the AI made it up" comes from.
An LLM's knowledge lives in its weights. Quantize them down to 4-bit and you damage that knowledge in three compounding ways:
It's the predictable result of running a model below the precision it was built for, to fit it onto cheaper, older hardware. The model didn't fail you. The provider chose the shortcut.
| 16-bit (FP16/BF16) | 8-bit (INT8) | 4-bit (NF4/FP4) | |
|---|---|---|---|
| In one line | Full fidelity: the real model | Compressed: usable, lossy | Approximated: guesses creep in |
| Fidelity | Near-perfect; relationships intact | Minor loss, often recoverable | Significant; concept links fracture |
| Accuracy impact | No measurable loss vs full FP32 | Small (~1-3%), tunable via calibration | Large (~5-10%+); hallucination-prone |
| Memory (≈70B model) | ~140 GB: needs serious GPUs | ~half of FP16 | ~quarter of FP16: fits on cheap kit |
| Why providers use it | Because the truth matters | Latency / cost balance | To run on 5-year-old hardware cheaply |
| Right use | Training & high-accuracy answers | Some production inference | Edge/offline where compute is scarce |
| What you're served | The genuine article | A reasonable copy | A toy sold as enterprise software |
Figures are illustrative orders of magnitude; exact numbers vary by model and method. The point is the direction: precision down, error up.
Illustrative figures. "Perplexity" is a standard accuracy measure where lower is better. The pattern is consistent across models: as bit-width falls, memory and latency drop sharply, and so does accuracy.
| Model | Precision | VRAM | Latency | Accuracy (perplexity, lower = better) |
|---|---|---|---|---|
| Llama-2-13B | 16-bit | 26 GB | 250 ms | 4.2: best |
| Llama-2-13B | 8-bit | 11 GB | 85 ms | 4.3 |
| Llama-2-13B | 4-bit | 6.5 GB | 40 ms | 5.2: worst |
| Mistral-7B | 16-bit | 14 GB | 120 ms | 3.8: best |
| Mistral-7B | 8-bit | 6 GB | 45 ms | 3.9 |
The 4-bit speed-and-memory saving is real, but look at the accuracy column. That degradation is exactly where invented facts, names and numbers come from. Providers keep the saving; you inherit the errors.
The hard truth of the subscription-AI model isn't the technology, it's the dilution.
The model you reach through a cheap public subscription is rarely the full model. To serve millions of users profitably, providers pull the very levers described on this page, smaller models, quantized weights, throttled speed, rate limits, shrunken context windows. You're shown what the technology can do, then sold a cut-down tier of it. So when that tier invents a number or a name, people conclude "AI can't be trusted", when the reality is they were never handed the real thing.
When you own a SARAH box, you aren't renting a throttled shadow. You get the full-precision weights, the full context window, no rate limits and no backdoor telemetry, the real model, on hardware you own, in your own building. AI isn't dangerous because it's accessible; the damage is done when it's gate-kept and quietly watered down. The venom was never the AI. It's the dilution of the truth.
Every weight preserved. When you ask SARAH for a fact, you get the fact, not an approximation dressed up as an answer.
Full precision is locked in on hardware you own. No cloud provider's cost-cutting can quietly downgrade what you're running.
When your team uses SARAH to draft, decide or quote, they aren't rolling dice on whether the numbers are real.
Imagine paying for a Michelin-starred meal and being served instant ramen, "just as good," they say, because "it fills your stomach." When providers serve you a 4-bit model, that's the swap they're making. SARAH serves you the real thing. AI isn't about cheap answers. It's about uncompromising clarity.
SARAH runs at full 16-bit precision on sovereign hardware you own, the truth, every time, in 17 languages. Let's show you the difference on a live call.