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Quantization and Low Precision
Quantization holds and runs model weights (and activations) at fewer bits, FP16/BF16, FP8, INT8, INT4, rather than FP32, shrinking memory and accelerating inference for some accuracy cost. It is the primary way to fit a large model onto a given GPU and serve it cheaply, and it sits behind QLoRA fine-tuning and KV-cache compression. AI, ML, and GenAI engineer interviews probe it because 'how do you serve a 70B model affordably?' typically opens with quantization, so the precision ladder and its trade-offs are must-know material.
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PRACTICE THIS IN REAL QUESTIONS
LLM & GenAI FundamentalsWhat is FP8, and how does it differ from INT8 for LLM training and inference?→ML Infrastructure & GPUsWhat is FP8 (and low-precision training/inference), and what are the tradeoffs?→ML Infrastructure & GPUsHow do you quantize or compress the KV cache, and why does it matter for long-context serving?→ML Infrastructure & GPUsYour INT4-quantized model lost too much accuracy. How do you recover it?→ML Infrastructure & GPUsServe a 70B-parameter model with high throughput. Do the memory math and name the optimizations.→MLOps & ML EngineeringYour model's p99 inference latency is too high. How do you bring it down without retraining?→
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