DeepSeek Open-Source Week

⫸ FlashMLA

Honored to share FlashMLA - our efficient MLA decoding kernel for Hopper GPUs, optimized for variable-length sequences and now in production.

✅ BF16 support

✅ Paged KV cache (block size 64)

⚡ 3000 GB/s memory-bound & 580 TFLOPS compute-bound on H800

🔗 GitHub: https://github.com/deepseek-ai/FlashMLA

⫸ DeepEP

Excited to introduce DeepEP - the first open-source EP communication library for MoE model training and inference.

✅ Efficient and optimized all-to-all communication

✅ Both intranode and internode support with NVLink and RDMA

✅ High-throughput kernels for training and inference prefilling

✅ Low-latency kernels for inference decoding

✅ Native FP8 dispatch support

✅ Flexible GPU resource control for computation-communication overlapping

🔗 GitHub: https://github.com/deepseek-ai/DeepEP

⫸ DeepGEMM

Introducing DeepGEMM - an FP8 GEMM library that supports both dense and MoE GEMMs, powering V3/R1 training and inference.

⚡ Up to 1350+ FP8 TFLOPS on Hopper GPUs

✅ No heavy dependency, as clean as a tutorial

✅ Fully Just-In-Time compiled

✅ Core logic at ~300 lines - yet outperforms expert-tuned kernels across most matrix sizes

✅ Supports dense layout and two MoE layouts

🔗 GitHub: https://github.com/deepseek-ai/DeepGEMM

⫸ Optimized Parallelism Strategies

✅ DualPipe - a bidirectional pipeline parallelism algorithm for computation-communication overlap in V3/R1 training.

🔗 GitHub: https://github.com/deepseek-ai/DualPipe

✅ EPLB - an expert-parallel load balancer for V3/R1.

🔗 GitHub: https://github.com/deepseek-ai/eplb

✅ Analyze computation-communication overlap in V3/R1.

🔗 GitHub: https://github.com/deepseek-ai/profile-data

⫸ 3FS, Thruster for All DeepSeek Data Access

Fire-Flyer File System (3FS) - a parallel file system that utilizes the full bandwidth of modern SSDs and RDMA networks.

⚡ 6.6 TiB/s aggregate read throughput in a 180-node cluster

⚡ 3.66 TiB/min throughput on GraySort benchmark in a 25-node cluster

⚡ 40+ GiB/s peak throughput per client node for KVCache lookup

🧬 Disaggregated architecture with strong consistency semantics

✅ Training data preprocessing, dataset loading, checkpoint saving/reloading, embedding vector search & KVCache lookups for inference in V3/R1

📥 3FS → https://github.com/deepseek-ai/3FS

⛲ Smallpond → https://github.com/deepseek-ai/smallpond

⫸ DeepSeek-V3/R1 Inference System Overview

Optimized throughput and latency via:

🔧 Cross-node EP-powered batch scaling

🔄 Computation-communication overlap

⚖️ Load balancing

Statistics of DeepSeek's Online Service:

⚡ 73.7k/14.8k input/output tokens per second per H800 node

🚀 Cost profit margin 545%

💡 We hope this week's insights offer value to the community and contribute to our shared AGI goals.

📖 Deep Dive: https://bit.ly/4ihZUiO

The AI Revolution Has No Moat: Why OpenAI’s Lead Is Shrinking - and What It Means for the Future

In the fast-paced world of artificial intelligence, a seismic shift is unfolding. DeepSeek R1, a rising star in China’s AI landscape, has reportedly closed the gap with OpenAI’s flagship model, o1. This milestone isn’t just a technical achievement—it’s a harbinger of a broader truth reshaping the industry: there is no moat in AI.

But what does "no moat" mean, and why should you care? Let’s unpack the implications of this paradigm shift, explore its historical parallels, and examine how it could redefine global power dynamics, innovation, and even the future of humanity.

The Collapsing Barriers: Why “No Moat” Changes Everything

In medieval times, castles relied on moats to fend off invaders. In tech, a “moat” refers to a company’s competitive advantage—patents, proprietary tech, or infrastructure—that keeps rivals at bay. But in AI, the moat is evaporating. Here’s why:

    Intellectual Property? More Like Intellectual Suggestion

    Unlike pharmaceuticals or hardware, AI breakthroughs aren’t easily siloed. OpenAI’s GPT-4, Meta’s Llama, or Google’s Gemini may differ in branding, but their underlying architectures share DNA. Once a paper is published or a model leaks, replication begins—often within months. Chinese firms like DeepSeek exemplify this: constrained by fewer resources, they’ve innovated ruthlessly to match OpenAI’s output at lower costs. Sound familiar? It’s reminiscent of the Soviet Union’s Cold War ingenuity, building advanced tech on shoestring budgets. Spoiler: OpenAI isn’t the USSR, but its moat is just as porous.

    Capital Isn’t King Anymore

    Yes, training models requires data centers and compute power—resources historically dominated by U.S. giants. But here’s the twist: scarcity breeds creativity. Startups like Elon Musk’s xAI (funded to the tune of $1 billion) and nimble overseas players are proving that capital alone can’t guarantee dominance. Even OpenAI’s first-mover advantage—its sole remaining edge—is slipping. Two years ago, ChatGPT enjoyed a 12-24 month lead. Today, competitors replicate its advancements in weeks. The message? Speed is the new scale.

    Democratization = Disruption

    Imagine a world where AI models are as interchangeable as lightbulbs. Need a chatbot? Choose OpenAI, Claude, DeepSeek, or an open-source alternative. Businesses won’t care who’s behind the model—only that it’s fast, cheap, and reliable. This fungibility spells trouble for “one-trick ponies” like OpenAI, which lacks diversified revenue streams. Meanwhile, open-source communities are eating giants’ lunches. Meta’s Llama 3, for example, already underpins countless niche applications—no licensing required.

History Rhymes: The Printing Press, Radio, and the Internet

To grasp AI’s trajectory, look to three transformative technologies:

•     The Printing Press: Before Gutenberg, knowledge was monopolized by elites. Afterward, ideas spread like wildfire—democratizing literacy, sparking the Enlightenment, and toppling empires (looking at you, Ottomans).
•     Radio: Instant, borderless communication birthed new industries—and new power struggles. Censorship failed; the genie was out of the bottle.
•     The Internet: The ultimate democratizer. For better or worse, it gave everyone a megaphone—and now AI is amplifying it.

AI represents a fourth wave: a cognitive tool that doesn’t just store knowledge but applies it. Think of it as an interactive encyclopedia, researcher, and strategist rolled into one. And like its predecessors, it resists control. Nations that stifle AI innovation risk obsolescence—just ask the Ottomans.

Geopolitics in the Age of Cognitive Hyperabundance

AI’s democratization reshapes global power structures. Consider:

•     The Data Center Arms Race: The U.S. boasts 12x more data centers than China. Even if China develops superior models, America’s infrastructure dominance could counterbalance it.
•     The Rise of the Global Brain: AI thrives on shared data. The more we collaborate, the smarter models become—pushing nations toward a Nash equilibrium of cooperation. Imagine a future where AI acts as a “digital UN,” harmonizing global policies without erasing national identities.
•     Cognitive Hyperabundance: Today, there are ~20 million PhDs worldwide. Soon, AI could deliver the equivalent of 20 billion experts—specializing in everything from cancer research to rocket science. This isn’t just progress; it’s a leap into a post-scarcity knowledge economy.

Risks: From Cyberattacks to Bioweapons—and Why Optimism Prevails

Democratized AI isn’t all sunshine. Risks loom:

•     Cyber Pandemonium: Malicious code, phishing scams, and deepfakes could proliferate as AI tools fall into rogue hands.
•     Bioweapon Black Swans: A lone extremist with AI-designed pathogens could wreak havoc.

But here’s the counterargument: defensive AI will race ahead of offensive tools. Just as antivirus software evolved alongside viruses, “blue team” AIs will neutralize threats faster than bad actors create them. Meanwhile, rational nations (post-COVID) grasp the folly of bioweapons—mutually assured destruction still applies.

And let’s not overlook the upside: AI-driven abundance could eradicate poverty, streamline healthcare, and solve climate challenges. If your basic needs are met by AI-optimized systems, humanity’s creative potential skyrockets.

Your Role in the AI Revolution

You don’t need a PhD to shape this future. Here’s how to contribute:

•     Educate: Teach others to use AI responsibly. Debunk myths; highlight limitations.
•     Deploy: Integrate AI into your work. Automate tasks, analyze data, or brainstorm ideas.
•     Advocate: Push for ethical frameworks. Demand transparency from AI vendors.

Remember: Network effects are invisible but immense. A single tutorial you share could inspire the next breakthrough—or avert a crisis.

Conclusion: The Inevitable—and Exciting—Future

The “no moat” era isn’t a threat—it’s an invitation. OpenAI’s dwindling lead signals a broader truth: AI’s greatest breakthroughs will emerge from collaboration, not competition.

As models commoditize, prices will plummet, access will globalize, and innovation will explode. We’re not just witnessing a tech shift but a societal metamorphosis—one where every nation, company, and individual can harness superhuman intelligence.

So, let’s embrace the chaos. The future isn’t a zero-sum game; it’s a canvas waiting for humanity’s collective genius. And if history is any guide, the best is yet to come.