How Good Are Low-bit Quantized LLaMA3 Models? An Empirical Study
"LLAMA3 still suffers non-negligent degradation in these scenarios, especially in ultra-low bit-width. "
WHY I STUDIED THIS
By mid-2023, everyone was talking about LLMs — but few people understood the infrastructure tradeoffs underneath. I studied LLM quantization (PTQ, LoRA-FT, GPTQ, AWQ) to understand the real cost of deploying AI at scale: what gets compressed, what breaks, and why. The Google AI strategy analysis gave me the business counterpart — how the biggest players were actually monetizing and positioning these systems. The AI chip analysis (Intel Gaudi3 vs. NVIDIA) added the hardware layer.
This combination — model efficiency + product strategy + compute infrastructure — is what lets me talk to both engineers and executives without losing either audience. It directly shaped how I thought about GenAI deployment during the NAVER internship and the automation architecture at HL Mando.
Insight:
Insight:
Google, Perplexity and OpenAI seek to mould Changing Consumer Behaviors
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#4. AI chip