I'm worried about losing the nuance of my LLM when I compress it for cheaper runs. Has anyone found a sweet spot where 4-bit or 8-bit quantization still holds up for complex logical reasoning tasks?
3 answers
The loss in reasoning from 16-bit to 8-bit is almost negligible for most general-purpose applications. When you drop down to 4-bit, you do start to see "perplexity" increase, but with modern techniques like QLoRA or GGUF, the degradation is remarkably graceful. The key is to evaluate the model on your specific reasoning benchmarks. For many, the 2x speedup in inference and the ability to run on cheaper hardware far outweighs the 1-2% drop in logic scores. If your application isn't solving unsolved math theorems, 4-bit is usually the "sweet spot" for production.
Does the choice of quantization method (like AWQ vs. GPTQ) change based on whether you are using an NVIDIA or an AMD backend?
We use 6-bit as our standard. It feels like the perfect middle ground where you get the VRAM savings without the "hallucination" spikes of 4-bit.
Monica has a point. We tested 6-bit on our technical documentation bot and the accuracy was nearly identical to the full-weight model during high-load periods.
Sean, absolutely. GPTQ is often more optimized for NVIDIA's architecture, while AWQ is gaining ground for its better preservation of activation outliers. If you're on a non-standard backend, you might find that certain methods introduce weird artifacts in the text generation that weren't there in the original fp16 version.