INT4 Quantization

INT4 Quantization represents AI model weights using only 4 bits per parameter, compressing memory footprint by 8x compared to standard 32-bit floating point. A 70-billion-parameter model that requires 140GB at FP16 fits in roughly 35GB at INT4 — small enough to run on a single NVIDIA RTX 4090 consumer GPU. Popular INT4 schemes include GPTQ (group-wise quantization preserving accuracy), AWQ (activation-aware preserving important weights at higher precision), and bitsandbytes-NF4 (used widely in Hugging Face workflows). The tradeoff is small accuracy loss — typically 1-3 percentage points on common benchmarks like MMLU, often imperceptible in customer-facing applications. INT4 has been a key enabler of the local-LLM movement, making models like Llama 3.1 70B and Mixtral practical to run on a single workstation. AI governance, AI compliance, and AI risk management programs document INT4 use in deployment records supporting responsible AI in resource-constrained enterprise AI environments.

Centralpoint Powers Local INT4 Workloads: Oxcyon's Centralpoint AI Governance Platform connects to INT4-quantized embedded models running locally — alongside OpenAI, Gemini, and full-precision options. Centralpoint meters all LLM use, keeps prompts and skills on-prem, and embeds chatbots into your portals via a single line of JavaScript.


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