Precision Control#

The choice of data types is essential to the inference runtime, which can have a huge impact on the performance and other metrics. Usually 2 types of precision are identified:

  1. Model storage precision (IR precision),

  2. Model inference precision.

Inference precision no longer depends on the precision of IR, which means that users have several options to find the balance between model performance and accuracy.

Essentially, the IR precision becomes a way of compressing the model by reducing the precision of the weights, and it does not affect how the devices execute the model. This change clears up a lot of confusion where, for example, you couldn’t execute a high-performance model on the GPU by default, and the behavior between devices was different.

This guide will focus on how to control inference precision. And using lower precision is important for performance because compute bandwidth tends to be higher for smaller data types, and hardware often has special blocks for efficient multiply-accumulate operations with smaller data types only (e.g. Intel Xᵉ Matrix Extensions (XMX) on GPU and Intel Advanced Matrix Extensions (AMX) on CPU do not support f32). Also, I/O operations requires less memory due to the smaller tensor byte size. This guide will focus on how to control inference precision.

Execution Mode#

ov::hint::execution_mode is a high-level hint to control whether the user wants to keep the best accuracy (ACCURACY mode) or if the device can do some optimizations that may lower the accuracy for performance reasons (PERFORMANCE mode)

  • In ACCURACY mode, the device cannot convert floating point tensors to a smaller floating point type, so devices try to keep the accuracy metrics as close as possible to the original values ​​obtained after training relative to the device’s real capabilities. This means that most devices will infer with f32 precision if your device supports it.

  • In PERFORMANCE mode, the device can convert to smaller data types and apply other optimizations that may have some impact on accuracy rates, although we still try to minimize accuracy loss and may use mixed precision execution in some cases.

If the model has been quantized using OpenVINO optimization tools or any other method, the quantized operators will be executed with the target integer precision if the device has hardware acceleration for that type. For example, quantized int8 primitives are executed with int8 precision for both ACCURACY and PERFORMANCE modes if the device provides higher compute bandwidth for 8-bit data types compared to any available floating-point type. On the other hand, devices without hardware acceleration for the int8 data type can keep such operators in floating point precision, and the exact floating point type will be affected by execution_mode and inference_precision properties.

Code examples:

import openvino as ov
import as hints

core = ov.Core()
# in case of Accuracy
    {hints.execution_mode: hints.ExecutionMode.ACCURACY},
# in case of Performance
    {hints.execution_mode: hints.ExecutionMode.PERFORMANCE},
    ov::Core core;
    // in case of Accuracy
    core.set_property("CPU", ov::hint::execution_mode(ov::hint::ExecutionMode::ACCURACY));
    // in case of Performance
    core.set_property("CPU", ov::hint::execution_mode(ov::hint::ExecutionMode::PERFORMANCE));

Inference Precision#

ov::hint::inference_precision precision is a lower-level property that allows you to specify the exact precision the user wants, but is less portable. For example, CPU supports f32 inference precision and bf16 on some platforms, GPU supports f32 and f16, so if a user wants to an application that uses multiple devices, they have to handle all these combinations manually or let OV do it automatically by using higher level execution_mode property. Another thing is that inference_precision is also a hint, so the value provided is not guaranteed to be used by Runtime (mainly in cases where the current device does not have the required hardware capabilities).


All devices only support floating-point data types (f32, f16, bf16) as a value for inference_precision attribute, because quantization cannot be done in Runtime.

Limitation of the bf16 inference precision#

It is important to mention that inferring FP16 and FP32 LLM models with the bf16 runtime precision may result in higher accuracy loss than the pre-determined threshold of 0.5%. Higher accuracy drop may occur when inferring dolly-v2-12b, dolly-v2-3b, and gpt-neox-20b original Pytorch models with bf16, and is caused by a limited precision representation.

To solve the issue, you might use an INT8 model and force the FP32 inference precision. The accuracy of an INT8 model with FP32 is nearly the same as of an FP16 model with f32. Additionally, selective FP32 execution of ops on CPU plugin together with the NNCF bf16 calibration could potentially mitigate the accuracy loss.

However, the solutions mentioned above would, unfortunately, also result in significant performance drop during a large batch size inference task on machines with Intel AMX-BF16 SPR. In such cases, the fused multiply-add operation (FMA) is used instead of AMX. Also, in a compute-bound case, such as the LLM batch inference/serving, these workarounds would drastically reduce the throughput by more than 60%.

Additional Resources#