Build with DeepSeek V4 Using NVIDIA Blackwell and GPU-Accelerated Endpoints

2026-04-24 · Source: NVIDIA Technical Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Robotics & Autonomous Systems · Depth: Advanced, short

Summary

DeepSeek has launched its fourth generation of flagship models, DeepSeek-V4-Pro and DeepSeek-V4-Flash, designed for highly efficient million-token context inference. DeepSeek-V4-Pro features 1.6T total parameters and 49B active parameters, while DeepSeek-V4-Flash is a smaller 284B-parameter model with 13B active parameters, optimized for speed. Both models support a 1M-token context window, enabling advanced long-context applications like coding, document analysis, and agentic AI. The V4 family utilizes a DeepSeek MoE architecture with a novel hybrid attention mechanism, combining Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA), to achieve a 73% reduction in per-token inference FLOPs and a 90% reduction in KV cache memory compared to DeepSeek-V3.2. Initial tests show DeepSeek-V4-Pro on NVIDIA GB200 NVL72 achieving over 150 tokens/sec/user.

Key takeaway

For AI Architects and NLP Engineers building agentic AI systems, DeepSeek-V4's 1M-token context window and optimized architecture fundamentally alter inference economics. Your infrastructure strategy should prioritize platforms like NVIDIA Blackwell, which demonstrate strong out-of-the-box performance, to achieve the lowest token cost for deploying these high-performance, long-context models. Consider using NVIDIA GPU-accelerated endpoints or NIM for rapid prototyping and deployment.

Key insights

DeepSeek V4 models optimize long-context inference via a hybrid attention mechanism, significantly reducing FLOPs and KV cache memory.

Principles

• Long context is critical for agentic AI workflows.
• Attention and KV cache are major bottlenecks in large context windows.

Method

Hybrid attention combines Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA) to dynamically compress KV entries and sparsify attention matrices, reducing memory and computational overhead.

In practice

• Deploy DeepSeek V4 for long-context coding and document analysis.
• Integrate DeepSeek V4 into agentic workflows using NVIDIA NemoClaw or AI-Q Blueprint.

Topics

• DeepSeek V4 Models
• Long-Context Inference
• Hybrid Attention Architecture
• KV Cache Optimization
• Agentic AI Workflows

Code references

• NVIDIA-AI-Blueprints/aiq
• PicoNVIDIA/data-explorer-launchable

Best for: AI Architect, NLP Engineer, CTO, AI Engineer, Machine Learning Engineer, MLOps Engineer

Related on AIssential

• DeepSeek-V4: a million-token context that agents can actually use — DeepSeek V4 optimizes large language models for agentic workflows through architectural and training innovations.
• NVIDIA Extreme Co-Design Delivers New MLPerf Inference Records — Co-designed hardware, software, and models are crucial for maximizing AI factory throughput and minimizing token cost.
• ChinAI #356: DeepSeek as Road Builder [修路人] — DeepSeek V4 achieves near-frontier AI capabilities with significant compute efficiency, particularly for long-context inference.
• DeepSeek V4: One Million Tokens, Three Thinking Modes, and the First Real Hands-On Reports — DeepSeek-V4 optimizes long-context LLMs via novel attention and training stability, offering tiered reasoning and cost-effective variants.
• As agentic AI pushes rivals to raise prices and cap usage, Deepseek ships a good-enough model for almost nothing — Deepseek's V4 models offer massive context windows and competitive performance at significantly reduced costs via architectural innovation.
• DeepSeek launches V4 model with one million token context — DeepSeek-V4 offers enhanced AI capabilities, including a one-million-token context, optimized for domestic chips.

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Editorial summary, takeaway, and curation by AIssential. Original article published by NVIDIA Technical Blog.