LLM Quantization Services
Packages & Pricing.

LLM quantization services for smaller, faster local models

LLM quantization services convert a full-precision FP16 model into a compact format such as GGUF or AWQ, so it runs on smaller GPUs, laptops or edge devices. Omeiro picks bit levels, runs the conversion and tests that quality holds up on your tasks.

You see benchmarks for both accuracy and speed, so the trade-off is measured rather than assumed.

LLM quantization: matching bit level to hardware and task

Bit level is a trade-off you can measure. Lower precision shrinks memory use and speeds inference, but quality can slip, particularly on reasoning, code and non-English text. LLM quantization therefore starts from your hardware limit, then tests candidate levels on your actual prompts instead of generic scores. Before ordering, state your memory budget, target speed and the model you want to compress, along with a set of typical questions for testing.

Format follows runtime. GGUF is designed for llama.cpp-style runtimes that run on CPUs and mixed hardware, while AWQ suits GPU serving stacks, so the choice depends on where the model will live. LLM quantization also touches licences, since some models restrict redistribution of converted files. Keep the original weights, version every converted file and record the benchmark results next to it, so you can reverse the decision if quality turns out lower than expected.