Super AI Coding

Background

Surging global AI demand—particularly for large-scale model training and inference—has driven strong demand for high-performance storage (e.g., HBM and fast SSDs) as well as high-capacity HDDs. Fueled by the expansion of AI infrastructure among tech companies, storage prices have risen sharply, with industry forecasts indicating this trend will continue through 2026.

* Data Source: CFM (China Flash Market) Official Website

Pain Points

High Storage Cost

Low Quality image

High Storage Cost

With rising hard drive prices, the cost of storing the same amount of video has risen sharply, which directly increases expenses for users.

Coding	Video Capacity	Storage	Costs
H.265	8MP*16 Channels (14-day Cycle)	14TB	500 USD
H.265	8MP*16 Channels (14-day Cycle)	14TB	1000 USD	After Price Increase
Super AI Coding	8MP*16 Channels (14-day Cycle)	7TB	500 USD	After Price Increase

Low Quality image

Rising HDD prices reduce storage capacity per budget, forcing users to adopt lower-bitrate video for longer retention. However, excessive bitrate reduction degrades quality, causing blocking, motion artifacts, and visual distortion.

Coding	Video Capacity	High Bit Rate
H.265	Image blurriness, mosaic artifacts, breathing effects, and blocking artifacts	Clear image with clear details
H.265	Fast-motion screen tearing and motion trailing	Smooth
Storage	Small storage	Big storage
Bandwidth	Low	High

Value

Super AI Coding implements intelligent bit rate allocation through 3 technologies, which reduces the overall bit rate while ensuring high-quality images of key objects.

Notes: The following data is based on typical scenarios: For half of the operating time, the scene maintains moderate pedestrian and vehicle flow, with only minimal motion activities captured in the monitoring frame.

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