Profile Job Results
Jobs
jgdd00meg
Results Ready
Name
edgetam_video_decoder
Target Device
- Arduino VENTUNO Q
- Ubuntu 24.04
- Qualcomm® Dragonwing™ IQ-8275 | QCS8275
Creator
ai-hub-support@qti.qualcomm.com
Input Specs
image_embeddings: float32[1, 64, 64, 256]high_res_features1: float32[1, 256, 256, 32]high_res_features2: float32[1, 128, 128, 64]sparse_embedding: float32[1, 3, 256]Completion Time
9/19/2026, 4:55:17 PM
Options
--qairt_version latestVersions
- Lite RT: 1.4.2
- QAIRT: v2.50.0.260828221209
- QNN TfLite Delegate: v2.50.0.260828221209
- Qualcomm Linux: 24.04
- AI Hub: aihub-2026.09.11.0
Estimated Inference Time
6.60 ms
Estimated Peak Memory Usage
0 ‑ 32 MB
Compute Units
NPU
897
| Stage | Time | Memory |
|---|---|---|
First App Load | 4.81 s | 158‑159 MB |
Subsequent App Load | 325 ms | 16‑17 MB |
Inference | 6.60 ms | 0‑32 MB |
| Lite RT | Value |
|---|---|
| number_of_threads | 4 |
| QNN Delegate | Value |
|---|---|
| backend_type | kHtpBackend |
| log_level | kLogLevelWarn |
| htp_options.performance_mode | kHtpBurst |
| htp_options.precision | kHtpFp16 |
| htp_options.optimization_strategy | kHtpOptimizeForInferenceO3 |
| htp_options.useConvHmx | true |
Sign up to run this model on a hosted Qualcomm® device!
Run on device