DeepSeek V4 Architecture: High‑Efficiency Long‑Context Model Design

DeepSeek V4, announced by DeepSeek in April 2026, is the latest flagship model focused on "extremely efficient long‑context intelligence". It reduces reliance on top‑tier hardware through algorithmic innovations while supporting a 1 million‑token context window.

Application Layer

Full‑scenario coverage: optimized for million‑level long contexts such as ultra‑long codebase analysis, extensive document parsing, and complex agent workflows.

Economic revolution: the Flash and Pro variants offer highly competitive API pricing, turning long‑text inference from an expensive experiment into an accessible tool.

Agent enhancement: improves logical reasoning, function calling, and information consistency across multi‑turn dialogues.

Model Layer

Mixture‑of‑Experts (MoE) evolution: adopts a hybrid expert model.

V4‑Pro: ~1.6 T total parameters, ~49 B activation parameters.

V4‑Flash: ~284 B total parameters, ~13 B activation parameters.

Hybrid Compression Attention (CSA + HCA): replaces full KV‑cache storage with a two‑stage compression, cutting GPU memory usage by 90%.

Manifold‑Constrained Hyper‑Connection (mHC): replaces traditional residual connections to improve gradient stability and long‑range dependency handling in ultra‑deep, large‑scale models.

Training Layer

Muon optimizer: a novel orthogonal gradient update method that outperforms AdamW in convergence speed and accuracy.

FP4 Quantization‑Aware Training (QAT): integrates 4‑bit floating‑point weights during training, giving the model inherent high compression and inference speed.

Auxiliary‑loss‑free routing: improves MoE load‑balancing without auxiliary loss functions, avoiding conflicts between training objectives and expert balance.

Multi‑Token Prediction (MTP): extends the V3 approach to predict multiple future tokens, enhancing the model's foresight capability.

Inference Layer

Very low GPU memory load: hybrid attention and deep inference optimizations dramatically reduce cost when handling a million‑token context.

Native FP4 inference: custom operators (e.g., DeepGEMM) achieve near‑lossless accuracy while delivering step‑up throughput on NVIDIA Blackwell (SM100) and Hopper (SM90) GPUs.

Prefix caching (ShadowRadix): a highly optimized cache that accelerates first‑token response in multi‑turn conversations.

Infrastructure Layer

Domestic compute optimization: deep integration with Huawei Ascend 950PR and other Chinese chips, decoupling algorithmic logic from CUDA‑specific libraries to achieve performance close to top‑tier GPUs.

Distributed communication optimization: employs more efficient expert parallel (EP) and context parallel (CP) schemes, applying topology‑level improvements to alleviate cross‑node bottlenecks caused by million‑token contexts.