Deepseek's new multimodal model is built mainly to cut the operating costs of long contexts. The biggest gain is in memory use, though Deepseek also promises better model performance.
According to the technical report, Deepseek has a clear goal with V4.1-Flash: shrink the so-called KV cache. This buffer holds the parts of a context a model has already processed, so it doesn't have to recompute everything at each new step. For agents that work across many steps, it grows fast and strains GPU memory, SSDs, and data bandwidth. That drives up deployment costs.
At its core, the language model has 552 billion parameters and processes contexts of up to one million tokens. The company says the buffer in fast GPU memory now needs only about a quarter of the space its predecessor Deepseek-V4-Flash used. The permanently offloaded part, which sits on SSD or in the host's memory, shrinks to roughly an eighth. Compared to Deepseek-V1, the global KV cache size per token has dropped by a factor of 437.
Deepseek has shrunk the global KV cache sharply since V1. | Image: Deepseek
Less compute on the input side
