Nvidia RTX 3060 · yolov9-t

Frigate 0.16Community build

Posted Aug 2025·GitHub #19741 ·db-140

I run this too, add my numbers Opens the submit form prefilled with this hardware, model and version

Inference

10–14 ms

as reported

Est. capacity

~83 det/s

1000 ÷ 12 ms · estimate

Cameras

—

not reported

CPU

—

not reported

Power

—

not reported

Parts

prices show only once verified by hand

Accelerator

Nvidia RTX 3060

ONNX (Nvidia / GPU), TensorRT (Nvidia, legacy) · 4 builds on DetectFPS · reported as “Nvidia RTX 3060 12GB”

price not tracked yet

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Host

—

not reported

—

CPU

—

not reported

—

Cameras

—

resolution not reported

—

Tracked totalno verified prices yet

Setup

Detector

ONNX (Nvidia / GPU)

Detector instances

—

Model, as reported

yolov9-t

Model family · input

yolov9-t · input not stated

Camera resolution

—

Cameras

—

Frigate version

0.16 (0.16 series, 2 behind newest)

—

Numbers

dashes mean the source didn't say

Inference

—

Inference range

10–14 ms

Total fps

—

CPU

—

Power

—

Used in stats

Yes

Notes and flags

TensorRT execution provider. Improved from 28-34 ms (default ONNX) to 10-14 ms.

Source

Where

github.com/blakeblackshear/frigate/discussions/19741

Date, as the source wrote it

2025-08 (month precision)

Added to DetectFPS

from a public post; numbers copied as written, not re-tested

What this build implies

~83 detections per second

1000 ÷ 12 ms. No camera count was reported, so use the calculator to see what that covers: at 5 fps and Typical activity, that is about 19 cameras comfortably.

Open in the calculator

Hardware

Nvidia RTX 3060

Detector type

ONNX (Nvidia / GPU), TensorRT (Nvidia, legacy)

Builds on DetectFPS

Median, this model

12 ms (1 build)

Price

price not tracked yet

Shown because this guide lists Nvidia RTX 3060 in its hardware (hardware_ids). Cross-device guides are only linked when they name this device or its host.

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