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Quantization reduces the number of bits used to serve a model, improving performance and reducing cost by 30-50%. However, this can change model numerics which may introduce small changes to the output.
Read our blog post for a detailed treatment of how quantization affects model quality.

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Checking available precisions

Models may support different numerical precisions like FP16, FP8, BF16, or INT8, which affect memory usage and inference speed. Check default precision: 
firectl get model accounts/fireworks/models/llama-v3p1-8b-instruct | grep "Default Precision"
Check supported precisions: 
firectl get model accounts/fireworks/models/llama-v3p1-8b-instruct | grep -E "(Supported Precisions|Supported Precisions With Calibration)"
The Precisions field indicates what precisions the model has been prepared for.

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Quantizing a model

A model can be quantized to 8-bit floating-point (FP8) precision.
  • firectl
  • Python (REST API)
firectl prepare-model <MODEL_ID>
This is an additive process that enables creating deployments with additional precisions. The original FP16 checkpoint is still available for use.
You can check on the status of preparation by running:
  • firectl
  • Python (REST API)
firectl get model <MODEL_ID>
and checking if the state is still in PREPARING. A successfully prepared model will have the desired precision added to the Precisions list.

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Creating an FP8 deployment

By default, creating a deployment uses the FP16 checkpoint. To use a quantized FP8 checkpoint, first ensure the model has been prepared for FP8 (see Checking available precisions above), then pass the --precision flag when creating your deployment:
  • firectl
  • Python (REST API)
firectl create deployment <MODEL> --accelerator-type NVIDIA_H100_80GB --precision FP8
Quantized deployments can only be served using H100 GPUs.