Quantization tradeoffs in LLM inference — what have you seen in practice?
Posted by Outrageous_Air_2507@reddit | LocalLLaMA | View on Reddit | 2 comments
I wrote a breakdown of quantization costs in LLM inference — but curious what tradeoffs others have hit in practice.
I published Part 1 of a series on LLM Inference Internals, focusing specifically on what quantization (INT4/INT8/FP16) actually costs you beyond just memory savings.
Key things I cover: - Real accuracy degradation patterns - Memory vs. quality tradeoffs - What the benchmarks don't tell you
🔗 https://siva4stack.substack.com/p/llm-inference-learning-part-1-what
For those running quantized models locally — have you noticed specific tasks where quality drops more noticeably? Curious if my findings match what others are seeing.
draconisx4@reddit
In my experience with quantization like INT8, those accuracy hits can sneak up and undermine reliability in real deployments, so it's smart to build in checks for oversight to keep things safe and controlled.
Outrageous_Air_2507@reddit (OP)
Got it. Is there any other tools can be used for prod level model deployment?