Choosing a Quantization Strategy - AI Stats Docs

Use this workflow to choose quantization variants safely.

Step 1: define your success criteria

Before choosing a quant:

Set quality thresholds (task accuracy, pass rates, structured output validity).
Set latency and cost targets.
Define unacceptable failure modes.

Step 2: pick a high-quality baseline

Start with a stronger baseline (often BF16 or FP16) and measure:

Output quality
Median and tail latency
Token/cost profile

This becomes your comparison anchor.

Step 3: step down gradually

Evaluate progressively lower precision:

FP8 or INT8
FP4 or INT4 only if needed for cost/throughput

Do not skip directly to the smallest variant unless you already have strong evidence it works for your workload.

Step 4: evaluate real workloads

Use production-like prompts:

Long prompts
Multi-step reasoning
Tool calling
Edge cases from support tickets

Step 5: decide per endpoint/use case

One quantization does not need to fit everything. Examples:

Customer support and critical decisioning: higher precision.
Large-volume classification/summarization: lower precision can be acceptable.

Suggested starting points

Workload type	Good starting quant
Quality-critical reasoning	`BF16` / `FP16`
Balanced cost + quality	`FP8` / `INT8`
High-throughput, cost-sensitive	`INT4` / `FP4` (after validation)

Deployment policy

Keep a fallback model/quantization configured.
Monitor quality drift after rollout.
Re-test when provider runtime or model revision changes.

Last modified on February 18, 2026

Quantization Methods Inference Parameters