← 🖥️ ML Infrastructure & ServingNEXT IN ML INFRASTRUCTURE & SERVINGGPU Memory and the Serving Stack→
Core
Quantization and Low Precision
Quantization stores and computes model weights (and activations) in fewer bits, FP16/BF16, FP8, INT8, INT4, instead of FP32, cutting memory and speeding inference at some accuracy cost. It is the main lever to fit a large model on a given GPU and to serve it cheaply, and it underlies QLoRA fine-tuning and KV-cache compression. Applied-AI interviews probe it because 'how do you serve a 70B model affordably?' usually starts with quantization, and knowing the precision ladder and its trade-offs is essential.
a free account unlocks the core curriculum tier · no card
RELATED CONCEPTS
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?→
COMPANIES THAT ASSUME THIS