Quantization
Shrinking a model by lowering weight precision.
/ quick answer
Quantization converts 16-bit weights to 8-bit, 4-bit, or lower. It shrinks model size 2-4x with a small accuracy hit — the key trick behind running Llama-scale models on a laptop. Shrinking a model by lowering weight precision.
What is Quantization?
Quantization converts 16-bit weights to 8-bit, 4-bit, or lower. It shrinks model size 2-4x with a small accuracy hit — the key trick behind running Llama-scale models on a laptop.
What is an example of Quantization?
A 70B model quantized to 4-bit fits in ~40GB VRAM and runs on a single H100.
Why does Quantization matter for AI and automation?
Shrinking a model by lowering weight precision. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.
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Related concepts
The vocabulary this page depends on.
- →AI Router
A layer that picks the cheapest capable model for each request, saving cost and latency.
- →Inference
Running a trained model to produce outputs.
- →Model Routing
Sending each request to the cheapest model that can handle it.
Related workflows
Turn this into a repeatable process.
- →How to Create a Website with AI
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- →How to Build an AI Content System
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- →How to Start a Niche Website with AI
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- →Personal Research Assistant Workflow
A repeatable system to research any topic deeply in under 30 minutes.
Related tool stacks
The tools that run it in production.
- →AI Research & Knowledge Stack
Default toolset for analysts, founders and creators doing deep research with AI.
Comparisons & alternatives
Pick between the options.
- →Groq vs Together AI
Fast open-model inference: throughput vs breadth.
- →Chroma vs Qdrant vs Pinecone
Open-source local vs managed cloud vector databases.
- →ChatGPT vs Claude
Two leading conversational AI assistants compared across reasoning, writing, coding, and pricing.
- →Lovable vs Bolt
Two AI app builders compared on speed, backend, deployment, and production readiness.