Q4_K_M vs Q8_0 for your local LLM: what I actually notice (2026)

Description and use

Choosing between Q4_K_M and Q8_0 for local LLMs: Q4_K_M offers fast token processing with minimal quality loss, suitable for most tasks, while Q8_0 is reserved for precision‑critical use cases.

Problem

Choosing the right quantization level for local LLMs to balance VRAM usage and output quality.

Outcome

Using Q4_K_M on 8 GB GPUs yields ~3 % quality loss but faster tokens; Q8_0 is justified only for precision‑critical tasks. The bundle stack runs entirely on local hardware, saving cloud costs.

Workflow

• 1. Measure VRAM capacity of GPU.
• 2. Download Q4_K_M and Q8_0 quantized models from GGUF repo.
• 3. Test both on local setup, noting token speed and task pass rates.
• 4. Choose the quantization that fits in VRAM and meets task precision needs.
• 5. Deploy with LM Studio for inference, Open WebUI for chat, Docker for stack isolation.
• 6. Monitor performance; switch to higher precision only when tasks fail.
• 7. Use pre‑tested quantized models to avoid mid‑night repo issues.

Evidence

Going from Q8_0 down to Q4_K_M cost me maybe three percent on the generic chatbots, and I could not feel it in normal conversation.

Source

My rule now is boring but practical: whatever still fits in VRAM first, pick the biggest quant that does.

Source