Process converting a large language model from FP16 precision to 4-bit Q4_K_M quantization using llama.cpp on Windows with AM

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Convert FP16 LLM to 4‑bit Q4_K_M on Windows AMD Radeon...

Convert FP16 LLM to 4‑bit Q4_K_M on Windows AMD Radeon GPUs via llama.cpp

By AI Daily Post Edited by Brian Petersen, Editor-in-Chief

June 20, 2026 • 2 min read

Running large language models on AMD Radeon™ GPUs is now a realistic option for anyone with a PC. While the tech is impressive, it’s the recent surge in open‑source tooling that makes it feel within reach. Integrated graphics (iGPU) and discrete cards (dGPU) can both serve as cost‑effective platforms for local AI, thanks to a growing suite of accelerators.

Here’s the thing: you can choose a polished desktop app, a lean command‑line workflow, or a fully custom runtime—each backed by the same underlying hardware. Lemonade offers a user‑friendly launcher that handles GGUF and ONNX formats across CPU, GPU, and NPU execution. LM Studio provides a desktop environment for downloading, serving and chatting with models.

Ollama adds a simple CLI and GUI with a broad model library. At the core, llama.cpp delivers a low‑level, highly optimized engine that powers many of these higher‑level solutions. This guide walks through step‑by‑step setup for each approach, showing how to tune performance on Radeon‑powered systems and get state‑of‑the‑art language models running locally.

Windows The following example demonstrates how to convert an FP16 model to a 4‑bit Q4_K_M format using llama.cpp: cd llama.cpp cmake -B build cmake --build build --config Release build\bin\Release\llama-quantize.exe phi-3.5-mini-instruct-fp16.gguf phi-3.5-mini-instruct-Q4_K_M.gguf Q4_K_M Quantization Options: Q4_K_M: 4-bit quantization, medium quality (recommended balance of size and quality) Q4_K_S: 4-bit quantization, small size (more compression, slight quality loss) Q5_K_M: 5-bit quantization, medium quality (better quality, larger file) Q8_0: 8-bit quantization (highest quality, larger file) After quantization, the new .gguf file will be much smaller and can be used directly at runtime with llama.cpp or any inference runtime that supports GGUF formats.

A Practical Guide to Running LLMs on AMD Radeon™ GPUs - AMD ROCm AI Blog

Why this matters We can now convert a FP16 model to a 4‑bit Q4_K_M format with a handful of commands in llama.cpp, and run it on Windows AMD Radeon GPUs. The guide shows a simple build‑and‑quantize flow: cmake, build, then llama‑quantize.exe. This lowers memory demand and opens the door for local inference on integrated and discrete Radeon chips that were previously sidelined.

For developers, the steps are concrete; for founders, the cost‑effective hardware may look appealing. Yet performance numbers are absent, so it’s unclear whether the 4‑bit variant delivers usable speed on typical workloads. Researchers will note that the quantization method is specific to Q4_K_M, and broader compatibility remains untested.

We appreciate the open‑source momentum, but we remain cautious about stability across diverse Windows setups. We can integrate the quantized model into existing pipelines, yet the article does not detail deployment nuances. Can this approach scale to larger models?

Future work may explore other quantization schemes, but the current guide focuses solely on Q4_K_M. In short, the tutorial proves feasibility, while leaving open questions about real‑world efficiency and robustness.