Quantization

Quantization reduces the precision of an AI model's numerical weights — typically from 32-bit floating point (FP32) down to 16-bit (FP16), 8-bit integer (INT8), 4-bit (INT4), or even lower — dramatically shrinking memory footprint and accelerating inference at the cost of small accuracy loss. A typical 70-billion-parameter Llama model occupies 140GB at FP16 but only 35GB at INT4 — enabling it to run on a single high-end consumer GPU instead of an entire datacenter rack. Quantization techniques include GPTQ, AWQ (Activation-aware Weight Quantization), GGML/GGUF (used by llama.cpp for CPU inference), bitsandbytes (Hugging Face), and NVIDIA's INT4 calibration tools. Modern approaches preserve 95-99% of original-model accuracy on most benchmarks. The technology is critical for democratizing AI — making advanced models accessible on consumer hardware. AI governance, AI compliance, and AI risk management programs document quantization choices in model cards as part of responsible AI in any quantized enterprise AI deployment.

Centralpoint Handles Quantized and Full-Precision Models Equally: Oxcyon's Centralpoint AI Governance Platform is model-agnostic — whether you serve a quantized Llama on a single GPU or call cloud-hosted GPT-4o, Gemini, or Claude. Centralpoint meters consumption, keeps prompts and skills on-prem, and embeds chatbots into your portals via one JavaScript line.


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