Quantization
Our inference stack supports quantized model execution where reduced numerical precision improves the feasible memory, throughput, or cost envelope without causing unacceptable workload degradation.
What it does
Quantization can reduce the precision used to represent model weights and, depending on the runtime, other parts of inference. The practical effect is lower memory consumption and potentially higher throughput or lower serving cost.
This can determine whether a model fits into available VRAM at all.
Deployment tradeoff
Quantization is not treated as a free optimization. A lower-precision representation changes the numerical behavior of the model. The resulting quality impact depends on the model, quantization method, hardware backend, and workload.
For that reason, the relevant comparison is not whether the quantized model still scores well generally. It is:
Does this exact quantized deployment still clear the production evaluation for the workload we intend to route to it?
A configuration that performs well for classification may not be acceptable for another task with greater reasoning or numerical sensitivity.
What quantization changes
A quantized deployment can affect:
- VRAM requirements
- Maximum feasible model size
- Concurrency
- Batch capacity
- Inference throughput
- Latency
- Hardware compatibility
- Model quality
Those variables interact. Reducing model memory may permit larger batches or additional concurrent workloads on the same hardware. A more aggressive quantization level may create enough quality loss that the apparent infrastructure savings are not economically useful.
Quantization can also interact with model adaptation. Adapter compatibility, base-model representation, runtime support, and evaluation state need to be treated as part of the deployed artifact rather than as independent configuration.
Selection rule
We use quantization when it improves the quality-per-unit-of-compute of the deployed system.
If the memory or cost savings do not materially change the operating envelope, or if workload evaluation shows meaningful degradation, higher precision can remain the correct choice. The goal is not minimum precision. It is the lowest-cost representation that still behaves correctly enough for the workload being served.
Precision is part of the artifact
We treat precision configuration as part of the deployed model version. Two deployments of the same nominal model at different precision levels can have different memory profiles, throughput, and workload behavior. Evaluation therefore needs to reflect the exact runtime configuration rather than a full-precision reference that is never used in production.
Calibration requirements also vary by quantization approach. Some paths can be applied with limited calibration data; others are more sensitive to representative activation distributions or backend implementation.
The infrastructure decision is consequently coupled to evaluation: the model, precision, runtime, and hardware backend form one serving configuration that must clear the workload threshold together.