Q8 quantization offers near-lossless quality with 25% memory savings vs 16-bit, preferred for production tasks?

Q8 quantization offers near-lossless quality with 25% memory savings vs 16-bit, preferred for production tasks.

Q4 provides 50% memory reduction and is widely accepted for most chat and creative use cases, with less than 0.5 perplexity loss on average?

Q4 provides 50% memory reduction and is widely accepted for most chat and creative use cases, with less than 0.5 perplexity loss on average.

Q3 and Q2 are risky for reasoning-heavy or coding tasks but can work for lightweight generation on consumer GPUs (e.g., 8GB VRAM).

Open Source

r/LocalLLaMA May 20, 2026

⚡Users reveal real-world experiences with 16-bit, Q8, Q4, and Q3 quantizations on Gemma and Qwen.

Deep Dive

A Reddit post sparks debate over quantization levels, with some users saying they’d never go under Q8 and others finding Q3 acceptable.

Key Points

Q8 quantization offers near-lossless quality with 25% memory savings vs 16-bit, preferred for production tasks.
Q4 provides 50% memory reduction and is widely accepted for most chat and creative use cases, with less than 0.5 perplexity loss on average.
Q3 and Q2 are risky for reasoning-heavy or coding tasks but can work for lightweight generation on consumer GPUs (e.g., 8GB VRAM).

Quantization choices directly impact who can run powerful LLMs locally, democratizing AI access for professionals with limited hardware.