DeepSeek V4: Million-Token Context That Actually Works Most long-context models are benchmarks in search of a use case. DeepSeek V4 flips the script—it delivers 1 million tokens not as a spec-sheet checkbox, but as an operational reality you can actually deploy. The breakthrough is not just the context length. It is how they got there without torching your inference budget. The KV Cache Problem Nobody Talks About Everyone wants to brag about context windows. Few mention that a naive 1M token implementation would need 83.9 GiB of KV cache per sequence using standard attention. That is not a deployment. That is a denial-of-service attack on your GPU memory. DeepSeek's fix is a hybrid attention architecture that compresses the KV cache by nearly 9x. They use shared key-value vectors across layers, compressed KV streams, and sparse attention on compressed tokens. The sliding window for nearby context stays at 128 tokens—enough for local coherence without the memory bomb.…