Welcome to InfraBench, the ultimate leaderboard for evaluating inference providers. Our platform focuses on key metrics such as cost, quality, and compression to help you make informed decisions. Whether you're a developer, researcher, or business looking to optimize your inference processes, InfraBench provides the insights you need to choose the best provider for your needs.
"""
)
with gr.Tabs():
with gr.TabItem("InfraBench Leaderboard"):
median_inference_time_min = math.floor(float(df["Median Inference Time (in s)"].min()))
median_inference_time_max = math.ceil(float(df["Median Inference Time (in s)"].max()))
price_per_image_min = math.floor(float(df["Price per Image"].min()))
price_per_image_max = math.ceil(float(df["Price per Image"].max()))
Leaderboard(
value=df,
search_columns=["Model"],
filter_columns=[
ColumnFilter(
column="Median Inference Time (in s)",
type="slider",
default=[median_inference_time_min, median_inference_time_max],
min=median_inference_time_min,
max=median_inference_time_max,
),
ColumnFilter(
column="Price per Image",
type="slider",
default=[price_per_image_min, price_per_image_max],
min=price_per_image_min,
max=price_per_image_max,
),
],
select_columns=df.columns.tolist(),
datatype="markdown",
)
with gr.Accordion("Citation", open=True):
gr.Markdown(
"""
```bibtex
@article{InfraBench,
title={InfraBench: A Leaderboard for Inference Providers},
author={PrunaAI},
year={2025},
howpublished={\\url{https://huggingface.co/spaces/PrunaAI/InferBench}}
}
```
"""
)
with gr.TabItem("About"):
gr.Markdown(
"""
# About InfraBench
InfraBench is a leaderboard for inference providers, focusing on cost, quality, and compression.
