New Deepseek model V4.1-Flash cuts memory needs for AI agents

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.Ad Deepseek gets there through several techniques that work together. A central one splits the model in two halves. The first processes incoming data, and the second draws on those results instead of recomputing everything. When reading an input, the model activates only 8 billion parameters per token, but 16 billion during the actual text output.Ad Deepseek says this nearly halves the compute needed to process input. It’s aimed squarely at agents, which constantly process new inputs through frequent tool calls. Deepseek also stores the main KV cache in FP4 instead of FP8. According to the report, that nearly halves the memory footprint of this part of the cache. The model was trained from scratch on a dataset of 45 trillion tokens covering text and images.