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 INT8FP4 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
