老爹鞋性价比高的鞋子推荐男士 DeepSeek AI: Open

In the rapidly evolving world of generative AI and budget-friendly machine learning, one Hangzhou startup is proving you don’t need billion-dollar budgets to build top-tier LLMs. Founded in July 2023, DeepSeek AI delivered open-weight models for under $6 million—a feat that, according to reporting citing the Financial Times, says “upended AI economics,” and which Nature hailed as a “shock to Silicon Valley.” According to a Bloomberg report, DeepSeek’s R1 model matched GPT-4 on benchmarks at 20× lower cost. All Deepseek Models in Their Latest Version

DeepSeek-Prover-V2-671B

DeepSeek R1

DeepSeek V3

Janus Pro

Coder V2

OpenThinker

FlashMLA

Master Your DeepSeek Knowledge 🤖 What Is DeepSeek AI? 💸 How DeepSeek Ses Millions 🧠 Training Innovations 🧬 Architecture Highlights 📉 Market Impact ⚔️ DeepSeek vs. GPT-4/Gemini 🔐 Privacy & Bans 📈 Investor Sentiment 🏢 Enterprise Adoption 🔮 Future Outlook ❓ FAQs 1. What Is DeepSeek AI?

DeepSeek AI is a Beijing-and-Hangzhou-based R&D firm founded by Liang Wenfeng in July 2023. By leveraging a pre-export stockpile of Nvidia A100 and H800 GPUs—and a small, multidisciplinary team—DeepSeek pioneered open-weight LLMs under an MIT license, democratizing access to cost-effective generative AI worldwide.

2. How Does DeepSeek Se You Millions? 2.1 Inference-Time Computing Activates only the most relevant neuron clusters per query, reducing compute cycles by up to 90 %. Drives inference costs below $0.001 per request via dynamic weight activation. 2.2 Domain-Specific Fine-Tuning

Pre-train on large multilingual corpora, then fine-tune on industry datasets—minimizing over-parameterization and maximizing accuracy on specialized tasks.

3. Training Innovations: RL & Reward Engineering Reinforcement Learning: Rule-based reward models for logical reasoning and math benchmarks (AIME, Putnam). Reward Engineering: Hybrid rule-based and model-based rewards to align chain-of-thought with final answers. Distillation: Compressing 671 B-parameter capabilities into 1.5 B–7 B-parameter distilled models for edge deployment. Emergent Behior Networks: Synthetic expert-model data to spur natural reasoning patterns without manual prompt engineering. 4. Architecture Highlights: MoE & MLA Mixture-of-Experts Layers (MoE): Shared & routed experts balance capacity and minimize wastage. Multi-Head Latent Attention (MLA): Extends context windows to 128 K tokens with low overhead. K-V Caching: Stores key/value pairs between tokens to oid recomputation and boost throughput. Mixed-Precision: 8-bit and custom 12-bit floats reduce memory without sacrificing accuracy. 5. What Market Impact Has DeepSeek Caused? Nvidia & ASML: Shares fell on fears of reduced demand for high-end GPUs and EUV tools. Energy Sector: Stocks dipped amid speculation that energy-efficient inference will lower data-center power bills. App Store: DeepSeek’s mobile chatbot surged to #1 in “Productivity” within 48 hours, dethroning ChatGPT. 6. DeepSeek vs. GPT-4 & Google Gemini Provider Training Cost Cost/Query Active Params DeepSeek R1/V3 $6 M