Research #15 · Interactive Deep Dive

DeepSeek V4: Architecture & Performance Deep Dive

A hybrid attention mechanism combining CSA and HCA, 384 experts activated sparsely, and aggressive KV cache compression that enables 1M-token context on commodity hardware. How the "math-first, silicon-second" playbook reshapes the inference economics landscape.

ByYumei Dou
PublishedApril 27, 2026
FormatInteractive Research Report
CoverageDeepSeek · NVIDIA · Alibaba · ByteDance

This report is based on publicly available open-source information from DeepSeek. All architectural analysis represents reasonable inference and research based on the DeepSeek-V4-Pro model card on Hugging Face and the published config.json.

Executive Summary

On April 24, 2026, DeepSeek released V4, its fourth-generation large language model, representing a significant leap in efficient AI architecture design. The model introduces a hybrid attention mechanism combining Chunked Shared Attention (CSA) and Hash-based Chunked Attention (HCA), a novel Mixture-of-Experts configuration with 384 experts, and aggressive KV cache compression that enables 1 million token context windows on commodity hardware.

V4-Pro Parameters
1.6T
49B active
V4-Flash Parameters
284B
13B active
Context Window
1M
Pro + Flash
V4-Pro Price
$1.74
/M input · $6.94/M output
V4-Flash Price
$0.14
/M input · $0.55/M output
Release
Apr 24
Open weights · 2026

Key Innovations

CSA/HCA Hybrid Attention mHC Residual Connection Muon Optimizer 384 MoE Experts (6+1 active) TileLang Kernel DSL GRPO Post-Training FP4 Quantization Multi-Latent Attention (MLA)

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This research was produced by InAI Capital Advisor as part of our ongoing coverage of the global AI investment landscape. The analysis represents proprietary research conducted through expert network consultations and primary technical evaluation.