DeepSeek-V4 Tackles Million-Token Context on NVIDIA HGX B200
DeepSeek-V4, launched by Together AI, is reshaping how AI handles ultra-long context windows by introducing a 1-million-token capacity. Rather than simply a model architecture breakthrough, V4 transforms this into a systems-level challenge, focusing on efficient inference and memory management. This innovation runs on NVIDIA HGX B200 hardware, leveraging advanced techniques like compressed Key-Value (KV) layouts, prefix caching, and hybrid attention mechanisms to address the bottlenecks of long-sequence processing. Architectural Shifts: Compressing the Token Axis At the core of DeepSeek-V4s advancements is a hybrid attention mechanism that compresses the token axis before KV storage. Key techniques include Compressed Sparse Attention (CSA), Heavily Compressed Attention (HCA), and Sliding Window Attention (SWA). This approach reduces the size of the KV cache—a critical factor for managing long-context workloads. For context, a traditional 70-billion-parameter model in BF16 precision can require substantial KV cache per token, becoming unmanageable at million-token lengths. V4s compression techniques shrink this footprint significantly, making 1M-token contexts feasible without overwhelming memory or bandwidth. Specifically, the compressed cache allows NVIDIA HGX B200 hardware to manage up to 3.7 million tokens in testing—well beyond prior limits. Serving Challenges: Multiple Cache Layouts DeepSeek-V4s design necessitates managing three distinct cache types—CSA, HCA, and SWA—within the inference engine. Each cache type has