awq
AI infra interview questions tagged awq, across every topic.
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Concepts behind "awq"
The curriculum that explains the ideas these questions test.
Foundational
Weight Formats: FP8 Blocks, MXFP4 and AWQOpen-weights models now ship pre-quantized, and the format is part of the release rather than something you choose afterwards. Block-scaled FP8 gives one byte per parameter with a scale per tile. MXFP4 gives about 0.53 bytes by pairing four-bit values with a shared exponent every 32 elements. Integer schemes like AWQ reach similar sizes with a different error profile. What decides a deployment is not which is most accurate in the abstract but which one the model was released and evaluated in, and which one your engine and hardware can execute natively.🧮 Open Weights & Serving Engines
Advanced
Quantization for InferenceQuantization stores weights, and sometimes activations and the KV cache, in fewer bits, which cuts the bytes a decode step has to stream and the memory a model occupies. Weight-only int4 (GPTQ, AWQ) is a capacity and single-stream latency play; fp8 for weights and activations (W8A8) doubles tensor-core throughput and helps prefill and large batch; fp8 KV cache doubles context per GPU. Each has an accuracy cost you measure rather than assume, and knowing which one to reach for from the bottleneck is the interview question.🚀 Inference & Serving🔒 Premium