Atlas · GenAI 2026
Model Quantization
Quantization & compression (AWQ, GPTQ, GGUF, EXL2, FP8)
conceptPeak: 2024Model CompressionAI consensus: 3/3
Prerequisites
Quantization compresses Transformer weights from FP16 to INT4/INT8 — understanding what the weights represent is essential for assessing quality trade-offs
- mediumLinear Algebra
Quantization is approximation of floating-point matrices with lower-precision representations — linear algebra explains the error propagation
Recommended reference
Lin et al. (2024) 'AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration' — best modern quantization paper. Frantar et al. (2023) for GPTQ
Notes from AI deep research
Anthropic Opus
AWQ, GPTQ, GGUF, FP8. Kluczowe dla cost-effective self-hosting. TW Radar: Assess (AutoRound)
OpenAI Deep Research
Koszt i latencja w self-hostingu [OA#20]
Google Deep Think
Formaty 8-bit, 4-bit, 2-bit [G#72]
Related skills
- → is part of: LLM Inference Serving(3/3)
- → is part of: Edge AI(2/3)
- → is part of: Model Fine-Tuning(1/3)
- → is subcategory of: LLM Fine-Tuning(1/3)