It's AI's fault again, isn't it?

Understanding GPU memory requirements is essential for AI workloads, as VRAM capacity--not processing power--determines which models you can run, with total memory needs typically exceeding model size ...

Shimon Ben-David, CTO, WEKA and Matt Marshall, Founder & CEO, VentureBeat As agentic AI moves from experiments to real production workloads, a quiet but serious infrastructure problem is coming into ...

The growing imbalance between the amount of data that needs to be processed to train large language models (LLMs) and the inability to move that data back and forth fast enough between memories and ...

But there’s one spec that has caused some concern among Ars staffers and others with their eyes on the Steam Machine: The GPU comes with just 8GB of dedicated graphics RAM, an amount that is steadily ...

Meta released a new study detailing its Llama 3 405B model training, which took 54 days with the 16,384 NVIDIA H100 AI GPU cluster. During that time, 419 unexpected component failures occurred, with ...

Zotac Korea say ongoing memory shortages and the subsequent component price hikes are an existential threat to graphics card ...

Why GPU memorymatters for CAD,viz and AI. Even the fastest GPU can stall if it runs out of memory. CAD, BIM visualisation, and AI workflows often demand more than you think, and it all adds up when ...

In effect, memory becomes a record of the agent's reasoning process, where any prior node may be recalled to inform future ...

NVIDIA is warning users to activate System Level Error-Correcting Code mitigation to protect against Rowhammer attacks on graphical processors with GDDR6 memory. The company is reinforcing the ...