DeepSeek Aims at Memory Shortage With Latest AI Model but Might Sacrifice Performance

DeepSeek says V4 cuts single-token inference FLOPs to 27% of V3.2 and uses just 10% of its KV cache at a 1-million-token context window.

The model uses MLA-based compression to reduce memory load during inference, especially in the decode phase.

If the claims hold, V4 could ease GPU memory pressure and reduce reliance on scarce HBM and DRAM for large language models.

The trade-off is possible accuracy loss on detailed long-context prompts and other compression-sensitive tasks.