Quantization (GGUF, AWQ, EXL2, FP8)
Technique that reduces the numerical precision of neural network weights (e.g., from FP16 to INT8, INT4, or FP8) to drastically cut VRAM requirements and accelerate inference.
Comprehensive Architectural Explanation
Quantization compresses neural network parameters from 16-bit or 32-bit floating point representations down to 8-bit, 4-bit, or sub-4-bit integers. Techniques like AWQ (Activation-aware Weight Quantization), GPTQ, EXL2, and GGUF (used in llama.cpp and Ollama) minimize perplexity degradation by preserving salient outlier weights while quantizing non-critical parameters. This allows running a 70B parameter model on a single consumer GPU (24GB VRAM) or Apple Silicon Mac.
Why It Matters in Modern AI Systems
Democratizes local and private AI deployment, drastically reduces cloud inference hardware costs, and increases memory bandwidth efficiency.
Real-World Implementations & Use Cases
Engineering Constraints & Limitations
Over-quantization (e.g., below 3.5 bits per weight) leads to noticeable degradation in complex reasoning, mathematics, and long-context coherence.
Authoritative Academic Papers & Documentation
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Context Window
The maximum quantity of tokens (words, subwords, symbols) a model can ingest and consider simultaneously in a single prompt and response sequence.
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