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A quantized local LLM can give a different answer because quantization approximates the model’s weights, which can shift the scores it assigns to the next token. That small shift may change the next word—and then the rest of the response. A different answer does not, by itself, prove a meaningful quality loss. To judge one, compare the quantized model with its source checkpoint under controlled conditions and test the tasks you actually care about.
Why does my quantized local LLM give different answers?
Quantization represents model weights with fewer bits than a higher-precision version. Depending on the method, it may use scales, groups, mixed precision, or calibration to preserve important values. During inference, the model uses these approximated values, shifting its internal calculations and the scores—or logits—it assigns to possible next tokens. The Qwen quantization guide describes these approaches, including methods that use an importance matrix to protect sensitive weights.
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If two candidate tokens have similar scores, even a small shift can change which one is selected. The model then conditions its next prediction on a different token, so the text can diverge well beyond the point where the initial change occurred.
Quantization is not the only possible cause. Sampling settings such as temperature can introduce variation even when the weights are identical. For a useful comparison, use greedy decoding if your runtime supports it; otherwise, fix and report the seed and repeat runs. Keep the prompt, system message, chat template, tokenizer, context, stop rules, and runtime the same. Do not assume a runtime guarantees bit-for-bit deterministic output unless that has been established for your specific runtime and hardware.
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Is a 4-bit model worse than the original?
Not as a universal rule. Lower precision can reduce memory use, but its effect on quality depends on the model, quantization method, bit width, calibration, task, and inference implementation. Studies report results that vary across methods, model sizes, bit widths, and benchmarks; there is no single quality-loss percentage or best bit width that applies to every model and use.
For example, Meta’s Llama 3.2 model card reports MMLU (5-shot) scores of 49.3 for BF16 and 43.3 for Vanilla PTQ, and IFEval (0-shot) scores of 59.5 and 51.5, respectively. Those are results for the card’s particular evaluation and model; Meta notes that the Vanilla PTQ comparison model is not released. They should not be treated as an estimate for another quantization or task.
Likewise, a model’s file size is not a complete estimate of the memory it will need while running. A smaller quantized file may help it fit on a device, but it does not guarantee faster inference. Check speed on your own hardware and runtime rather than inferring it from bit width or file size.
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How to check whether a GGUF quant is any good
1. Confirm you are comparing the same model
Where possible, compare the GGUF file with the higher-precision checkpoint from which it was made. Verify the model family and revision, base versus instruction-tuned variant, tokenizer, and chat template. If any of those differ, the comparison cannot isolate the effect of quantization. Also confirm that the quantized file is compatible with your runtime.
Record the exact quantization name, not just the bit count. Schemes can use mixed quantization types or other choices that a label such as “4-bit” does not capture. Meta’s Llama 3.2 card, for example, identifies a quantization scheme designed for a particular inference framework and Arm CPU backend.
2. Build a prompt set for your intended use
Use a fixed set of representative prompts, rather than judging from one showcase example. Include the kinds of work you expect the model to do, such as factual questions, domain-specific tasks, instruction following, structured output, code, or long-context retrieval. Add expected answers or a scoring rubric where practical. If you use a public benchmark, record its dataset split, prompt and shot configuration, and scoring method.
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3. Compare generations under matched settings
Run every prompt through both models with the same generation settings. Record the runtime and version, hardware, context limit, decoding parameters, seed or repeat policy, and system prompt. Score correctness and task success, not just surface-level similarity: a valid paraphrase can differ in wording without being worse. For high-stakes use, add human review and a domain-appropriate evaluation rather than relying on an automatic score alone.
4. Use perplexity or KL divergence as diagnostics
Perplexity estimates how well a model predicts the next token in a corpus. Lower perplexity is better only when comparing the same model and tokenizer under comparable evaluation conditions; it is not a general measure of chat-answer quality across unrelated models or tokenizers.
The llama.cpp perplexity documentation describes using perplexity primarily to assess loss from quantization against FP16. It cautions that values are not directly comparable across different tokenizers and that exact numbers depend on implementation details. Use the same corpus and preprocessing for both models. WikiText-2 is a common base-model comparison set in the documentation, but the Qwen guide cautions that it is not a good evaluation set for instruction models; for an instruct or chat model, choose data related to its intended use.
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For a closer look at prediction changes, llama.cpp can record reference logits and calculate KL divergence for the quantized model. A value of zero means the compared distributions are identical. The documentation also describes examining changes in probability assigned to the correct token and percentiles of those changes. These measures help diagnose distribution shifts, but do not establish whether the model succeeds at your tasks.
5. Measure task quality and efficiency separately
Evaluate the abilities that matter for your use: correctness, instruction following, formatting, reasoning, or tool behavior. Research evaluates quantization across areas such as knowledge and capacity, alignment, and efficiency; one broad study of instruction-tuned models also reports that results vary by method, model size, bit width, and benchmark. A benchmark score is evidence about that evaluation, not a substitute for your own task checks.
Measure memory or disk use and throughput separately from quality. A pinned llama.cpp quantization README lists these file-size examples:
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| Model | Original file size | Q4_K_M file size |
|---|---|---|
| Llama 3.1 8B | 32.1 GB | 4.9 GB |
| Llama 3.1 70B | 280.9 GB | 43.1 GB |
These are documented file sizes for the named models and format, not guaranteed system RAM requirements or a general sizing formula. Runtime memory also depends on factors outside these file-size figures.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to record so the comparison is interpretable
- Model provenance: model family and revision, source checkpoint, base or instruction-tuned variant, and quantization scheme.
- Evaluation setup: prompts or dataset and split, preprocessing, scoring rubric, and any expected answers.
- Generation setup: runtime and version, chat template, system prompt, context limit, decoding parameters, and seed or repeat policy.
- Compute and results: hardware, task-level scores, perplexity or distribution metrics if used, memory or file size, and throughput.
Keeping these details together makes it possible to distinguish a quantization effect from a change in model, prompt format, sampling, or runtime. It also gives others enough context to interpret or reproduce the result.
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