DeepSeek V4 Launches: 1 Trillion Parameters, 1M Context, Open-Weight

DeepSeek's Most Ambitious Release Yet

Chinese AI startup DeepSeek released DeepSeek V4 on February 17, coinciding with the Lunar New Year. The model features 1 trillion total parameters, a 1-million-token context window, and three architectural innovations: mHC (Manifold-Constrained Hyper-Connections), Engram conditional memory, and Sparse Attention. It is released as an open-weight model.

Technical Highlights

mHC architecture: Addresses fundamental Transformer stability issues, improving large-scale training

Engram memory: Enables efficient long-context management across sessions

Sparse Attention: Reduces inference costs while handling extended context

1M-token context: Can process entire codebases in a single pass for true multi-file reasoning

Performance Claims

DeepSeek's internal benchmarks report that V4 surpasses Claude 3.5 Sonnet and GPT-4o on coding tasks, achieving over 80% on SWE-bench. The company claims inference costs are 10–40× lower than comparable Western frontier models.

Runs on Consumer Hardware

As an open-weight release, V4 is designed to run on dual NVIDIA RTX 4090s or a single RTX 5090 — making state-of-the-art coding AI accessible outside cloud infrastructure. The model is available for immediate download by developers globally.

Source: Introl, Vertu