Re ✨ Key Features of DeepSeek App:
🔐 Easy login: E-mail/Google Account/Apple ID
☁️ Cross-platform chat history sync
🔍 Web search & Deep-Think mode
📄 File upload & text extraction
🌟 2/3
via Twitter @DeepSeek
🔐 Easy login: E-mail/Google Account/Apple ID
☁️ Cross-platform chat history sync
🔍 Web search & Deep-Think mode
📄 File upload & text extraction
🌟 2/3
via Twitter @DeepSeek
Re ⚠️ Important Notice:
✅ 100% FREE - No ads, no in-app purchases
🛡️ Download only from official channels to avoid being misled
📲 Search "DeepSeek" in your app store or visit our website for direct links
🌟 3/3
via Twitter @DeepSeek
✅ 100% FREE - No ads, no in-app purchases
🛡️ Download only from official channels to avoid being misled
📲 Search "DeepSeek" in your app store or visit our website for direct links
🌟 3/3
via Twitter @DeepSeek
🚀 DeepSeek-R1 is here!
⚡ Performance on par with OpenAI-o1
📖 Fully open-source model & technical report
🏆 MIT licensed: Distill & commercialize freely!
🌐 Website & API are live now! Try DeepThink at http://chat.deepseek.com today!
🐋 1/n
via Twitter @DeepSeek
⚡ Performance on par with OpenAI-o1
📖 Fully open-source model & technical report
🏆 MIT licensed: Distill & commercialize freely!
🌐 Website & API are live now! Try DeepThink at http://chat.deepseek.com today!
🐋 1/n
via Twitter @DeepSeek
Re 🛠️ DeepSeek-R1: Technical Highlights
📈 Large-scale RL in post-training
🏆 Significant performance boost with minimal labeled data
🔢 Math, code, and reasoning tasks on par with OpenAI-o1
📄 More details: https://github.com/deepseek-ai/DeepSeek-R1/blob/main/DeepSeek_R1.pdf
🐋 4/n
via Twitter @DeepSeek
📈 Large-scale RL in post-training
🏆 Significant performance boost with minimal labeled data
🔢 Math, code, and reasoning tasks on par with OpenAI-o1
📄 More details: https://github.com/deepseek-ai/DeepSeek-R1/blob/main/DeepSeek_R1.pdf
🐋 4/n
via Twitter @DeepSeek
Re 🌐 API Access & Pricing
⚙️ Use DeepSeek-R1 by setting model=deepseek-reasoner
💰 $0.14 / million input tokens (cache hit)
💰 $0.55 / million input tokens (cache miss)
💰 $2.19 / million output tokens
📖 API guide: https://api-docs.deepseek.com/guides/reasoning_model
🐋 5/n
via Twitter @DeepSeek
⚙️ Use DeepSeek-R1 by setting model=deepseek-reasoner
💰 $0.14 / million input tokens (cache hit)
💰 $0.55 / million input tokens (cache miss)
💰 $2.19 / million output tokens
📖 API guide: https://api-docs.deepseek.com/guides/reasoning_model
🐋 5/n
via Twitter @DeepSeek
To prevent any potential harm, we reiterate that @deepseek_ai is our sole official account on Twitter/X.
Any accounts:
- representing us
- using identical avatars
- using similar names
are impersonations.
Please stay vigilant to avoid being misled!
Any accounts:
- representing us
- using identical avatars
- using similar names
are impersonations.
Please stay vigilant to avoid being misled!
X (formerly Twitter)
DeepSeek (@deepseek_ai) on X
Unravel the mystery of AGI with curiosity. Answer the essential question with long-termism.
📢 Terminology Correction: DeepSeek-R1’s code and models are released under the MIT License.
🎉 Excited to see everyone’s enthusiasm for deploying DeepSeek-R1! Here are our recommended settings for the best experience:
• No system prompt
• Temperature: 0.6
• Official prompts for search & file upload: bit.ly/4hyH8np
• Guidelines to mitigate model bypass thinking: bit.ly/4gJrhkF
The official DeepSeek deployment runs the same model as the open-source version—enjoy the full DeepSeek-R1 experience! 🚀
• No system prompt
• Temperature: 0.6
• Official prompts for search & file upload: bit.ly/4hyH8np
• Guidelines to mitigate model bypass thinking: bit.ly/4gJrhkF
The official DeepSeek deployment runs the same model as the open-source version—enjoy the full DeepSeek-R1 experience! 🚀
🚀 Introducing NSA: A Hardware-Aligned and Natively Trainable Sparse Attention mechanism for ultra-fast long-context training & inference!
Core components of NSA:
• Dynamic hierarchical sparse strategy
• Coarse-grained token compression
• Fine-grained token selection
💡 With optimized design for modern hardware, NSA speeds up inference while reducing pre-training costs—without compromising performance. It matches or outperforms Full Attention models on general benchmarks, long-context tasks, and instruction-based reasoning.
📖 For more details, check out our paper here: https://arxiv.org/abs/2502.11089
Core components of NSA:
• Dynamic hierarchical sparse strategy
• Coarse-grained token compression
• Fine-grained token selection
💡 With optimized design for modern hardware, NSA speeds up inference while reducing pre-training costs—without compromising performance. It matches or outperforms Full Attention models on general benchmarks, long-context tasks, and instruction-based reasoning.
📖 For more details, check out our paper here: https://arxiv.org/abs/2502.11089
arXiv.org
Native Sparse Attention: Hardware-Aligned and Natively Trainable...
Long-context modeling is crucial for next-generation language models, yet the high computational cost of standard attention mechanisms poses significant computational challenges. Sparse attention...
🚀 Day 0: Warming up for #OpenSourceWeek!
We're a tiny team @deepseek_ai
exploring AGI. Starting next week, we'll be open-sourcing 5 repos, sharing our small but sincere progress with full transparency.
These humble building blocks in our online service have been documented, deployed and battle-tested in production.
As part of the open-source community, we believe that every line shared becomes collective momentum that accelerates the journey.
Daily unlocks are coming soon. No ivory towers - just pure garage-energy and community-driven innovation.
We're a tiny team @deepseek_ai
exploring AGI. Starting next week, we'll be open-sourcing 5 repos, sharing our small but sincere progress with full transparency.
These humble building blocks in our online service have been documented, deployed and battle-tested in production.
As part of the open-source community, we believe that every line shared becomes collective momentum that accelerates the journey.
Daily unlocks are coming soon. No ivory towers - just pure garage-energy and community-driven innovation.
🚀 Day 1 of #OpenSourceWeek: 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
🔗 Explore on GitHub: https://github.com/deepseek-ai/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
🔗 Explore on GitHub: https://github.com/deepseek-ai/FlashMLA
🚀 Day 2 of #OpenSourceWeek: 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: github.com/deepseek-ai/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: github.com/deepseek-ai/DeepEP
🚀 Day 3 of #OpenSourceWeek: 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
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
🚀 Day 4 of #OpenSourceWeek: Optimized Parallelism Strategies
✅ DualPipe - a bidirectional pipeline parallelism algorithm for computation-communication overlap in V3/R1 training.
🔗 https://github.com/deepseek-ai/DualPipe
✅ EPLB - an expert-parallel load balancer for V3/R1.
🔗 https://github.com/deepseek-ai/eplb
📊 Analyze computation-communication overlap in V3/R1.
🔗 https://github.com/deepseek-ai/profile-data
✅ DualPipe - a bidirectional pipeline parallelism algorithm for computation-communication overlap in V3/R1 training.
🔗 https://github.com/deepseek-ai/DualPipe
✅ EPLB - an expert-parallel load balancer for V3/R1.
🔗 https://github.com/deepseek-ai/eplb
📊 Analyze computation-communication overlap in V3/R1.
🔗 https://github.com/deepseek-ai/profile-data
🚀 Day 5 of #OpenSourceWeek: 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 → github.com/deepseek-ai/3FS
⛲ Smallpond - data processing framework on 3FS → github.com/deepseek-ai/smallpond
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 → github.com/deepseek-ai/3FS
⛲ Smallpond - data processing framework on 3FS → github.com/deepseek-ai/smallpond
🚀 Day 6 of #OpenSourceWeek: One More Thing – 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: bit.ly/4ihZUiO
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: bit.ly/4ihZUiO
🚀 DeepSeek-V3-0324 is out now!
🔹 Major boost in reasoning performance
🔹 Stronger front-end development skills
🔹 Smarter tool-use capabilities
✅ For non-complex reasoning tasks, we recommend using V3 — just turn off “DeepThink”
🔌 API usage remains unchanged
📜 Models are now released under the MIT License, just like DeepSeek-R1!
🔗 Open-source weights: https://huggingface.co/deepseek-ai/DeepSeek-V3-0324
🔹 Major boost in reasoning performance
🔹 Stronger front-end development skills
🔹 Smarter tool-use capabilities
✅ For non-complex reasoning tasks, we recommend using V3 — just turn off “DeepThink”
🔌 API usage remains unchanged
📜 Models are now released under the MIT License, just like DeepSeek-R1!
🔗 Open-source weights: https://huggingface.co/deepseek-ai/DeepSeek-V3-0324