Optimization and Innovation of DeepSeek V4's KV Cache KV Cache is a video memory cache used to store historical context features when a large model generates answers, which is equivalent to the model's short-term memory scratch paper.
The biggest problem with traditional large models is that the longer the text, the KV cache will expand exponentially.
When it comes to ultra-long documents with one million tokens, the cache will reach the TB level, and dozens of A100 graphics cards must be stacked to run the document.
The cost is extremely high and cannot be implemented.
The core breakthrough of DeepSeek‑V4 is to completely reconstruct the attention structure, using the dual scheme of CSA local compression + HCA global ultra-high compression to directly compress the KV cache to 7% to 10% of the previous generation model.
Coupled with MoE sparse activation and Engram hot and cold data tiered storage, infrequently used cold data is sinked to the CPU and hard disk, leaving only the real-time KV in the graphics card memory.
Finally, a key qualitative change was achieved: a single 80G A100 and H100 graphics card can fully run 1 million Token contexts.
For agent development, privatized deployment, and enterprise long document knowledge base, the cost of multi-card clusters is directly cut, and ultra-long text analysis, full warehouse code understanding, and batch contract analysis can all be implemented at low cost.
