DeepSeek V4 Pro 1.6T

Overview

DeepSeek's frontier MoE: 1.6T total / 49B active params, MIT-licensed, 1M context, with CSA+HCA hybrid attention and three reasoning modes. The absolute open-weight ceiling as of April 2026.

When to pick this model

• Research labs benchmarking against closed frontier models
• Workloads where MIT licensing on frontier quality is the goal
• Million-token context tasks (whole codebases, books, archives)
• Multi-mode reasoning workflows (Non / High / Max)
• Datacenter deployments that can absorb ~1 TB VRAM

VRAM requirements by quantization

Quantization	VRAM required
Q4_K_M (recommended)	960 GB
Q5_K_M	1150 GB
Q8_0	1700 GB
FP16 (no quantization)	3200 GB

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

Strengths

• The most capable open-weight model available, period
• MIT license at frontier scale
• 1M context window
• Three configurable thinking modes (Non / High / Max)
• Hybrid CSA+HCA attention for efficient long-context

Limitations

• 960+ GB VRAM in Q4 — server farm only
• No community quantizations yet at release
• Three-mode reasoning adds inference complexity
• 32T+ token pretraining means very high training carbon footprint

Architecture & training

Architecture: MoE 1.6T/49B active · CSA+HCA hybrid attention · mHC · Muon optimizer · mixed FP4+FP8

Training: 32T+ tokens pre-training.

Quick start

# HuggingFace : deepseek-ai/DeepSeek-V4-Pro

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.