GGUF Quantization: Q4 vs Q5 for Local LLMs

GGUF packages tensors with metadata such as architecture and tokenizer information for efficient inference in llama.cpp-compatible runtimes.

The format does not validate the publisher, training data, license, or safety of the model.

For verification, save the model and runtime versions, source input, relevant settings, and observed output together. Repeat the step while changing only one factor, and record unexpected results and untested limits as carefully as successes before applying the guidance to private or production data.

Quantization represents weights with fewer bits, reducing file and memory size. The trade-off is possible information loss, which can affect tasks differently.

Published GGUF tables describe Q4_K at about 4.5 bits per weight, Q5_K at 5.5, and Q6_K at 6.5625.

Attach the measurement date, versions, and input conditions to the table. Similar headline numbers can hide different failure modes and operating costs, so interpret each column against the real task before collapsing the comparison into one score.

The weight file is only part of total usage. KV cache, runtime buffers, the operating system, and other applications also consume memory.

A model that barely fits may spill to slower memory or fail with longer context.

Define completion with an observable result instead of a general impression. Repeat the same input, and if the output changes, isolate whether the model, runtime settings, or source data changed before moving to the next stage.

Keep the base model, runtime, prompt, temperature, and context fixed. Change only the quantization and repeat each task several times.

Include your real language and workload: factual extraction, summarization, JSON, long documents, and answer-not-found cases.

For verification, save the model and runtime versions, source input, relevant settings, and observed output together. Repeat the step while changing only one factor, and record unexpected results and untested limits as carefully as successes before applying the guidance to private or production data.

Use trusted publishers and verify hashes when available. Update the runtime because model parsers can have security vulnerabilities.

Quantization does not erase the original model license. Review commercial-use and redistribution terms before deployment.

Preserve the before-and-after state and the time of the check so that another run can reproduce the result. Include at least one failure condition—such as empty input, constrained resources, or a restart—to reveal the boundary of the step rather than documenting only the happy path.