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The fairest way to compare quantized versions of one large language model is to treat each file as a treatment in a controlled experiment. Keep the base-model revision, tokenizer, chat template, runtime, kernels, prompts, context length, decoding settings, batch size, and hardware identical; change only the quantization recipe. Start with a BF16 or FP16 reference, then measure task quality, held-out perplexity, model size, peak memory, prompt-processing speed, generation throughput, latency, and quantization time. Choose the variant that satisfies your actual quality, memory, or latency constraint—not the one with the smallest file or the most impressive single benchmark.
What does a fair comparison of quantized LLMs look like?
A fair comparison isolates quantization as the independent variable. If one variant uses a different tokenizer, prompt format, context length, GPU, runtime build, or sampling setting, a score or speed difference cannot reliably be attributed to quantization.
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Quantization changes numerical precision so weights, and sometimes activations, use fewer bits. The result can reduce memory substantially, but it can also alter the model’s probability distribution and task behavior. GPTQ and AWQ use calibration data to decide how to preserve important behavior; on-the-fly approaches can be simpler to reproduce but may use different kernels and have different performance.
Define the intended deployment before testing. A coding assistant, a retrieval-augmented chatbot, a batch summarizer, and a benchmark harness need different quality tests and may select different Pareto-optimal variants.
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Which conditions must remain identical?
Write a test manifest and pin every variable that can affect an outcome. The quantized file should be the only deliberate change between runs.
Model and software identity
- Pin the exact base-model revision and tokenizer revision, not just a model family name.
- Record the chat template and any system, user, or assistant formatting. A template mismatch can look like a quality regression.
- Record the runtime, loader, kernel, compiler, and driver versions. A GPTQ or AWQ file may use a specialized kernel that another loader does not support.
- Record the quantization algorithm, bit width, group size, weight-only or weight-and-activation scheme, calibration dataset, calibration sample count, and whether an importance matrix or another calibration artifact was used.
Prompt and decoding identity
- Use the same prompt set, ordering, context length, truncation policy, and stop-token handling.
- For deterministic task scoring, use greedy decoding. If sampling is required, publish temperature, top-p or top-k, repetition settings, maximum new tokens, and the random seed.
- Keep batch size and sequence lengths fixed for timing and memory tests. Changing either can move the bottleneck from compute to memory bandwidth.
Hardware identity
Run every variant on the same GPU or CPU, with the same number of devices, offload policy, power mode, and background load. State whether weights, key-value cache, and temporary buffers reside in VRAM, system RAM, or both.
How do I establish a reference model?
Use an unquantized BF16 or FP16 build as the reference. Save enough information to reproduce both its quality and its resource measurements.
- Load the pinned model and tokenizer with the intended chat template.
- Run the complete prompt set with the fixed decoding configuration and save outputs, pass/fail labels, and logits where the runtime makes them available.
- Run the held-out language-model corpus with the same sliding-window perplexity implementation planned for quantized files.
- Warm up the runtime, then collect repeated timing and memory measurements using the same sequence lengths and batch size planned for every variant.
- Store the model-file size and the exact command, configuration, and environment information.
The reference is a baseline, not an assumption of perfect quality. A quantized model can occasionally score better on a finite benchmark because of measurement noise; that does not prove quantization improved the underlying model.
Which metrics should be in the comparison?
Report quality and systems cost together. A table that contains only perplexity or only tokens per second cannot answer whether a file is suitable for deployment.
| Axis | What to measure | Why it matters |
|---|---|---|
| Task quality | Exact match, accuracy, instruction following, coding tests, retrieval checks, or domain-specific success rate | Shows behavior on the workload you actually care about |
| Distribution drift | Held-out perplexity and delta from the BF16/FP16 reference | Detects next-token prediction changes under controlled tokenization and corpus conditions |
| Model footprint | File size plus peak VRAM and peak RAM | Determines whether the model and runtime buffers fit |
| Throughput | Prompt-processing tokens per second and generation tokens per second | Separates input ingestion from autoregressive decoding |
| Latency | Time to first token and end-to-end completion time | Captures interactive responsiveness |
| Build cost | Quantization time and peak memory during quantization | Matters when producing many variants or updating frequently |
| Operational fit | Backend support, kernel availability, failures, and reproducibility of the recipe | A theoretically good file is useless if it cannot run reliably in your stack |
Hugging Face’s guidance organizes quantization comparisons around accuracy, throughput, peak VRAM, and quantization time. It describes roughly twofold memory savings for 8-bit methods and roughly fourfold savings for 4-bit methods, while noting that speed and accuracy depend on the method, implementation, task, and hardware.
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How should I measure perplexity?
Run the same held-out corpus through every model with a sliding-window implementation, identical context handling, and identical tokenizer. Lower perplexity means the model is better at predicting the next token under that test; it is not a universal measure of chat quality.
Control the comparison
- Use one corpus, such as the Wikitext-2 convention documented by llama.cpp, for all variants.
- Use the same tokenization, context window, stride, beginning-of-sequence handling, and masking rules.
- Report the implementation and corpus revision. A change in preprocessing can be larger than the quantization effect.
Interpret the number cautiously
Perplexity is not directly comparable between models that use different tokenizers. Even with one tokenizer, a small change may have little practical effect on instruction following, coding, or factual retrieval. Treat perplexity as a distribution-drift diagnostic and pair it with task tests.
For a useful report, include the reference perplexity, each variant’s value, the absolute delta, corpus and tokenizer identity, and whether the run completed without skipped or overflowing tokens.
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How do I test real task quality?
Build a fixed, representative prompt set and score the behavior your deployment needs. Include both ordinary cases and failure-sensitive cases rather than relying on a general leaderboard.
Construct the prompt set
- Sample prompts from production logs after removing personal or confidential data, or write a coverage matrix for your domain.
- Include short and long contexts, structured-output requests, multilingual inputs if relevant, tool-call or JSON constraints, code with tests, and adversarial or ambiguous instructions.
- Freeze reference answers or grading rules before running the quantized files. Do not change labels after seeing results.
Choose scoring methods
- Use exact match for deterministic labels, extraction, or schema-constrained outputs.
- Use unit tests or execution-based checks for code generation.
- Use a documented rubric or a blinded judge for open-ended answers, and report inter-rater agreement or judge variance when available.
- Separate refusal, formatting, and factuality failures; a fluent answer in the wrong format may still be unusable.
Qwen’s published Qwen2-7B-Instruct table uses greedy decoding and reports MMLU, C-Eval, and IFEval. Those metrics are useful anchors, but they do not replace a workload-specific test.
What do published results show about quality loss?
Published examples demonstrate why no single bit width or algorithm wins on every model and metric. The Qwen2-7B-Instruct results below report average scores under the documented greedy-decoding setup.
| Variant | Average score | MMLU | Notes |
|---|---|---|---|
| BF16 | 66.9 | 70.5 | Unquantized reference in the table |
| GPTQ-Int8 | 66.2 | 69.1 | Calibration-based 8-bit variant |
| GPTQ-Int4 | 64.1 | 67.8 | Calibration-based 4-bit variant |
| AWQ | 64.1 | 67.4 | Calibration-based variant; average ties GPTQ-Int4 while MMLU differs |
The table also reports C-Eval and IFEval, so its average is not a universal “quality percentage.” The different MMLU results for GPTQ-Int4 and AWQ show why method, metric, and model must be evaluated together.
How should I compare model size, memory, and speed?
Measure the complete serving configuration, not just the weight file. The key-value cache, activations, allocator behavior, and framework overhead can determine whether a model fits and how fast it responds.
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Prompt processing is usually parallel across input tokens; generation produces one token at a time and is often more sensitive to memory bandwidth and kernel efficiency. Report both rates in tokens per second, along with sequence length and batch size. For interactive systems, add time to first token and end-to-end latency for a stated output length.
Use repeated, warmed measurements
- Start from a clean process and record the initial memory state.
- Run one or more warm-up requests so compilation, graph capture, and allocator setup are not counted as steady-state speed.
- Run multiple timed iterations, discard or explain outliers, and report the number of runs plus mean and spread.
- Measure peak VRAM and RAM during load, prompt processing, and generation separately when possible.
- Record out-of-memory errors, unsupported-kernel fallbacks, and partial CPU offload instead of silently accepting them.
A smaller file is not automatically faster. A backend may have highly optimized kernels for one format and a fallback implementation for another; hardware, batch size, and context length can reverse the ranking.
Example: Llama 3 8B perplexity and file size
The llama.cpp documentation reports the following on its documented CUDA/RTX 4090 setup:
| Format | Perplexity | Model size |
|---|---|---|
| FP16 | 6.233160 | 14.97 GiB |
| q8_0 | 6.234284 | 7.96 GiB |
| q6_K | 6.253382 | 6.14 GiB |
| q5_K_M | 6.288607 | 5.33 GiB |
These values illustrate a small perplexity change alongside a large footprint reduction on one documented setup. They do not predict chat quality, latency, or results on a different backend, tokenizer, corpus, or GPU.
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Is GPTQ or AWQ better?
Neither is universally better. GPTQ and AWQ are calibration-based methods, and the recipe, calibration data, runtime, kernels, and target workload determine the outcome.
GPTQ
GPTQ requires an upfront calibration step and produces pre-quantized weights that some runtimes serve with optimized kernels. Record the calibration dataset, number of samples, sequence handling, group size, and any importance information; two files both labeled “GPTQ 4-bit” can therefore behave differently.
AWQ
AWQ also uses calibration to preserve salient weight behavior. Hugging Face’s guidance notes that AWQ calibration can be shorter in its documented workflow, but calibration duration is not a quality guarantee. Verify backend support and benchmark the exact AWQ file you intend to deploy.
On-the-fly approaches
Bitsandbytes and similar approaches can simplify operational reproducibility because conversion is performed as part of loading or deployment. They may not use the same optimized kernels as a pre-quantized GPTQ or AWQ file, so compare measured throughput and memory rather than assuming an algorithm-level speed advantage.
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Use nominal weight savings as a first filter, then confirm with a real load at the target context and batch size. Hugging Face’s approximate guidance is about twofold memory reduction for 8-bit methods and about fourfold for 4-bit methods relative to the corresponding higher-precision weights, but runtime overhead and cache memory remain.
- Check the quantized file size against available VRAM or RAM, leaving headroom for the runtime, operating system, and key-value cache.
- Estimate cache growth from context length and batch size; a model that fits at a short prompt can fail at a long one.
- Check whether the loader silently offloads layers to CPU. Offload may avoid an out-of-memory error while increasing latency substantially.
- Test the maximum expected concurrent requests, not just one interactive prompt.
If a file fails to load, capture the exact error and determine whether the cause is insufficient memory, unsupported tensor type, missing kernel, incompatible driver, or a malformed quantization artifact. A smaller bit width is not a fix for an unsupported format.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What are the most common comparison mistakes?
Changing several variables at once
Comparing a 4-bit file on one GPU with an 8-bit file on another, or changing the chat template between runs, makes the result uninterpretable. Re-run both variants under one manifest.
Using perplexity as the only quality metric
A lower perplexity can coexist with worse instruction following or structured-output reliability. Add task tests that represent the deployment.
Comparing perplexity across tokenizers
Tokenization changes the prediction units and denominator, so the raw values are not directly comparable. Use a shared tokenizer or report the limitation prominently.
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Ignoring calibration details
Calibration data is part of the model variant. A domain-matched calibration set may preserve a workload differently from a generic set, and undocumented samples prevent reproduction.
Timing cold starts or hidden fallbacks
Compilation and loading can dominate a one-shot timing. Conversely, a runtime may fall back to a slower kernel without an obvious warning. Warm up, inspect logs, and report both steady-state and startup behavior when it matters.
Hiding failures
An out-of-memory run, unsupported kernel, invalid JSON output, or truncated completion is a result. Count it in reliability reporting instead of removing the variant from the table.
How should I choose the final variant?
Convert deployment needs into hard constraints, eliminate variants that fail them, and then select the best quality among the survivors.
- Set a minimum task-quality threshold and define which failures are unacceptable.
- Set memory limits for peak VRAM and RAM at the maximum context and concurrency you support.
- Set latency or throughput targets for both prompt processing and generation.
- Discard variants that fail to load, require unsupported kernels, or violate the quality threshold.
- Among the remaining files, select the highest-quality option that meets the operational targets. If several are close, prefer the recipe with documented calibration and stable backend support.
- Keep the full manifest and outputs so a runtime, driver, model revision, or prompt change can trigger a controlled re-test.
A quality-sensitive workload may favor 8-bit or a higher-quality 4-bit recipe. A memory-bound workload may accept 4-bit or lower, while a latency-bound workload must be tested on its actual backend because the smallest file is not always the fastest.
What should a reproducible comparison report contain?
Publish one row per variant and enough context for another engineer to repeat the run.
- Base-model and tokenizer revisions, quantization format, bit width, group size, and weight/activation scheme.
- Calibration dataset, sample count, preprocessing, importance matrix or other calibration artifact, and quantization time.
- Runtime, loader, kernel, driver, compiler, hardware, offload policy, batch size, context length, and decoding settings.
- Reference and quantized outputs, task scores, perplexity and delta, file size, peak VRAM/RAM, prompt throughput, generation throughput, time to first token, and end-to-end latency.
- Run count, variance or confidence interval where available, out-of-memory events, unsupported kernels, formatting failures, and any excluded cases with a reason.
This record turns a one-time benchmark into an auditable deployment decision. Re-run it whenever a model revision, tokenizer, runtime, kernel, hardware configuration, or prompt contract changes.
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Frequently Asked Questions
Can two files with the same 4-bit label behave differently?
Yes. Group size, calibration samples, weight or activation handling, clipping choices, and conversion tools can differ even when both filenames say “4-bit.” Compare the complete recipe and tensor metadata, not only the bit-width suffix.
Why did my quantized model fit in VRAM but fail on a long prompt?
The weight file is only part of the memory budget. Key-value cache and temporary buffers grow with context length, batch size, and concurrency, so a short smoke test can pass while the production context causes an out-of-memory error.
Should I include startup time in a tokens-per-second result?
Report startup separately. Loading, kernel compilation, and graph setup are one-time costs, whereas tokens per second normally describes warmed steady-state generation; combining them hides the behavior users experience during repeated requests.
How often should a quantization comparison be repeated?
Repeat it after changing the base-model revision, tokenizer, chat template, runtime, kernel or driver, hardware, or production prompt contract. Those changes can alter quality and speed independently of the quantization format.
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