# Benchmark Inference#

The inference speed of a model depends on:

1. The architecture of the model. Large models with many trees are slower than smaller models with few trees. Also, Gradient boosted tree models are generally much faster than Random Forest models. For example, a Gradient Boosted Trees model with 200 trees will be faster than a Random Forest with 100 trees.

2. The serving API you are using. See comment regarding the speed of each serving API.

3. (In the case of the C++ API) How well you are using the serving API. For example, by reusing the same engine and allocated examples, or by making sure not to reindex the input features, in between inference calls.

4. The speed of your computer. Faster computers run models faster.

The c++ benchmark inference and go benchmark inference tools allow to measure the speed of a model using the C++/CLI and Go APIs, respectively. For example, the following example measures the inference speed of a model using the C++ API:

# Disable CPU power scaling
sudo apt install linux-cpupower
sudo cpupower frequency-set --governor performance

# Benchmark
./benchmark_inference \
--model=/path/to/model \
--dataset=csv:/path/to/dataset \
--batch_size=100 \
--warmup_runs=10 \
--num_runs=50


The result of the benchmark looks as follows:

batch_size : 100  num_runs : 50
time/example(us)  time/batch(us)  method
----------------------------------------

In this report, we see that three different inference engines are compatible with the model. The fastest engine called GradientBoostedTreesQuickScorerExtended makes a prediction for one example in 0.89µs (microseconds).