@yoheinakajima: got object detection down to below 0.35 sec latency locally
Summary
A developer shares their achievement of reducing object detection latency to below 0.35 seconds on local hardware, highlighting progress in AI performance optimization.
View Cached Full Text
Cached at: 09/24/26, 12:25 PM
got object detection down to below 0.35 sec latency locally https://t.co/wImH6aQpoU
Similar Articles
@LinusEkenstam: Now this is bonkers
AI capabilities for expression recognition, object detection, and finger counting have achieved less than 1 second latency, highlighting notable real-time performance gains.
@yoheinakajima: fast enough for a live emotion detector
Yohei Nakajima tweeted about a technology fast enough for live emotion detection, emphasizing its real-time performance.
@DataChaz: @NVIDIA just dropped LocateAnything, making object detection ~10x faster by fixing one core bottleneck: How the model w…
NVIDIA released LocateAnything, an open-source model that achieves ~10x faster object detection by predicting all coordinates simultaneously instead of sequentially, reaching 12.7 FPS on a single H100 and outperforming 32B parameter models.
New local model reaching near frontier on PII removal at 9 ms CPU inference
Introduces ScreenLeak, a benchmark for measuring PII redaction in computer-use AI data, and presents two local models (v45_phase3 for text and rfdetr_v8 for images) achieving near-frontier performance at low latency.
@yoheinakajima: glance-vlm speedlab is now open source! read: https://glance.yohei.me/speed/ try: https://github.com/yoheinakajima/glan…
The article presents an open-source study on optimizing latency for local vision-language models through benchmarking and techniques like native batching and MLX quantization, achieving significant speedups while maintaining decision accuracy on Apple hardware.