<div id="readability-page-1"><div><p>Open source license. Local deployment and operation are supported by dual RTX 4090 graphics cards or a single RTX 5090 graphics card. What is DeepSeek V4? Building on the success of its predecessors (such as V3 and R1), DeepSeek V4 combines massive scalability with extreme cost-effectiveness. The following are its main technical parameters:</p><p><img src="https://imageproxy.pbkrs.com/https://img.jinse.com.cn/7450504_watermarknone.png?x-oss-process=image/auto-orient,1/interlace,1/resize,w_1440,h_1440/quality,q_95/format,jpg" width="600" height="178" original-src="https://imageproxy.pbkrs.com/https://img.jinse.com.cn/7450504_watermarknone.png"/></p><h2>Three Breakthrough Architectural Innovations</h2><p>The true strength of DeepSeek V4 lies not only in its powerful computing power, but also in three specific architectural breakthroughs that make training and running trillion-parameter models possible and cost-controllable.</p><h3>1. Engram Conditional Memory</h3><p>The biggest challenge faced by a million-token context window is accurately retrieving information without a sharp increase in computing costs. DeepSeek solves this problem through the Engram architecture.</p><p> This system separates static facts (such as API signatures or specific patterns) from dynamic reasoning. In the &#34;Needle-in-a-Haystack&#34; benchmark (finding specific facts among 1 million tokens), Enggram dramatically improved accuracy from a normal 84.2% to an astonishing 97%. 2. Manifold-Constrained Hyperconnected Structures (mHC) As AI models grow in size, they often experience training instability issues (such as gradient explosion). Manifold-constrained hyperconnected structures (mHC) are a mathematical framework that constrains the amplification of signals, keeping it within 2x (compared to up to 3000x in the unconstrained state). This allows DeepSeek to stably train a trillion-parameter model with only 6.7% of the computational cost. 3. DeepSeek DSA Mechanism and Lightning Indexer To efficiently handle the context of 1 million tokens, V4 uses a sparse attention mechanism (DSA) instead of traditional dense attention. The Lightning Indexer can quickly scan the context and locate relevant fragments, and the model only needs to perform attention computation on these specific tokens. In long context scenarios, this solution can reduce computational cost by approximately 50%. Benchmark Comparison: DeepSeek V4 vs GPT-5.4 and Claude 4.5 DeepSeek V4 focuses on software engineering and deep inference capabilities. According to internal benchmark data, the model exhibits outstanding performance. Note: This data is currently based on leaked internal information and will require independent third-party verification after the model's official release. API Pricing: Cutting-Edge AI with the Best Cost-Effectiveness</p><p>Western models such as GPT-5.4 and Claude Opus offer powerful performance, but are expensive to use. For example, GPT-5.2/5.4 costs approximately $1.75 to $15.00 per million input tokens, while Claude Opus 4.5 costs $5.00 per million.</p><h2>API Pricing: Cutting-Edge AI with the Best Cost-Effectiveness</h2><p>Western models such as GPT-5.4 and Claude Opus 4.5 cost approximately $1.75 to $15.00 per million input tokens.</p><h2>API Pricing: Cutting-Edge AI with the Best Cost-Effectiveness</h2><p>Western models such as GPT-5.4 and Claude Opus 4.5 cost approximately $ ...1.75 to $15.00 per million input tokens, while Claude Opus 4.5 costs approximately $5.00 per million input tokens.</p><h2>API Pricing: Cutting-Edge AI with the Best Cost-Effectiveness</h2><p>Western models such as GPT-5.4 and Claude Opus 4.5 cost approximately $1.75 to $15.00 per million input tokens, The expected API pricing for DeepSeek V4 is highly disruptive: This means that, with comparable performance, DeepSeek V4 will cost approximately 1/10 to 1/50 of its Western competitors. Geopolitics and Hardware: Shifting to the Huawei Ecosystem One of the most far-reaching features of DeepSeek V4 lies in its hardware support system. Due to strict US export controls on NVIDIA's high-end GPUs (such as the B300 and H200), DeepSeek has deeply optimized V4, making its inference process primarily rely on domestically produced Chinese chips. While the initial training phase of the model likely still uses NVIDIA hardware (such as the H800), the model has been highly optimized for Huawei's Ascend 950PR and Cambricon's MLU chip. Reportedly, the computing performance of Huawei's Ascend 950PR is 2.87 times that of NVIDIA's H20 (currently a chip legally allowed to be exported to China). This marks a significant milestone for China in achieving self-sufficiency in AI chips. Release Date: When will DeepSeek V4 be released? DeepSeek V4 has experienced several delays. Initially rumored to launch in mid-February 2026 during the Lunar New Year, the release was delayed due to significant engineering challenges in training on the new hardware architecture. On March 9, 2026, an unreleased &#34;V4 Lite&#34; version (approximately 200 billion parameters) appeared on the DeepSeek platform, effectively validating the feasibility of its core architecture. It is now widely expected that the full trillion-parameter version will be officially released in April 2026. Following release, DeepSeek is expected to release the model weights under the Apache 2.0 open-source license. Thanks to the efficiency of the MoE architecture and quantization techniques (INT8/INT4), this ultra-large-scale model is expected to run natively on consumer-grade hardware, such as dual RTX 4090 graphics cards or a single RTX 5090 graphics card. </p><h2>DeepSeek V4 Frequently Asked Questions</h2><h3>1. Is DeepSeek V4 better than ChatGPT (GPT-5.4)?</h3><p>According to internal benchmark results, in complex software engineering tasks (SWE-bench) and codebase-level programming tasks, DeepSeek V4 performs comparably to GPT-5.4 and Claude 4.5, and even slightly outperforms them, especially with its million-token context window. However, after the model is officially released, independent testing is still needed to determine the absolute winner.</p><h3>2. Can I run DeepSeek V4 locally?</h3><p>Yes, provided that the model weights are open-sourced as planned.</p><h3> Because the MoE architecture activates only about 32 billion parameters at a time, the quantized version (INT4) can theoretically run locally on a single 32GB RTX 5090 or dual RTX 4090 graphics cards. 3. Why is DeepSeek V4 so low-cost? Its low cost stems from the efficient MoE architecture (activating only a portion of the model's parameters), low training costs (estimated at around $10 million, while similar Western models cost over $100 million), and the application of the DeepSeek DSA mechanism—a technology that halves the computational power required for long contexts. 4. What does V4's so-called &#34;native multimodal&#34; mean? Unlike earlier models that used a &#34;late-stage fusion&#34; approach by &#34;stitching&#34; visual plugins onto text models, V4 is jointly trained from the outset on text, images, video, and audio. This gives it superior cross-modal reasoning capabilities, such as seamlessly generating corresponding interface code directly from hand-drawn architecture diagrams. Conclusion: DeepSeek V4 is not just an incremental update, but a breakthrough at the architectural level. It proves that intelligent optimization (Engram memory architecture, mHC hyperconnection structure, DSA mechanism) can completely rival high-cost, brute-force parameter scaling methods. With its native multimodal capabilities, million-token context window, and disruptive pricing strategy, DeepSeek is forcing enterprises and independent developers to re-evaluate their AI infrastructure. Once the API and open-source weights are launched as planned in April 2026, the overall landscape of artificial intelligence development may undergo a fundamental transformation.</h3></div></div>

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HUAWEI

<p>DeepSeek V4 is an upcoming AI model featuring 1 trillion parameters, designed for high efficiency and multimodal operations. It competes with Western models like GPT-5.4 and Claude Opus 4.5, offering API pricing significantly lower (1/10 to 1/50) than its competitors. Key innovations include Engram Conditional Memory for improved accuracy, Manifold-Constrained Hyperconnected Structures for stable training, and a sparse attention mechanism for cost reduction. The model is optimized for Chinese hardware due to US export controls, primarily utilizing Huawei's chips for inference.</p>

DeepSeek V4: Everything You Need to Know About the New Trillion-Parameter AI Model