Image created with gemini-2.5-flash-image with claude-sonnet-4-5. Image prompt: Photorealistic Times Square at blue-hour twilight with street flooded in shallow water, every billboard and screen displaying glowing bioluminescent jellyfish neural networks, sonar maps, and deep-sea research imagery in electric blues and teals, small yellow submersible robots on the wet pavement, mysterious oceanic atmosphere mixed with vibrant NYC energy, reflections of neon signs shimmering in the water.
SPCT/GRM use for RL confirmed. Also: Terminus, as well as 3.2, are trained with specialist model distillation. Target specialities: «mathematics, competitive programming, general logical reasoning, agentic coding, agentic search». https://x.com/teortaxesTex/status/1972611028442861675
Some relevant details about the RL phase for DeepSeek’s latest models: – finetune separate specialist models with RL for mathematics, competitive programming, general logical reasoning, agentic coding, and agentic search, which are used to generate data for final training – https://x.com/iScienceLuvr/status/1972627894636814585
DeepSeek-V3.1-Terminus launches with improved agentic tool use and reduced language mixing errors | VentureBeat https://venturebeat.com/ai/deepseek-v3-1-terminus-launches-with-improved-agentic-tool-use-and-reduced
DeepSeek has launched V3.2 Exp with their new DeepSeek Sparse Attention (DSA) architecture that claims to reduce the impact of the quadratic scaling of compute with context length We’ve independently benchmarked V3.2 Exp as achieving similar intelligence to DeepSeek V3.1 https://x.com/ArtificialAnlys/status/1973230103854456993
Getting ready to try DeepSeek-V3.2-Exp from @deepseek_ai ? vLLM is here to help! We have verified that it works on H200 machines, and many other hardwares thanks to the hardware plugin mechanism. Check out the recipes https://x.com/vllm_project/status/1972664010702221399
DeepSeek-V3.2 shows: – Chinese chips are rising: Day-0 support for Huawei Ascend & Cambricon; – ML compiler: DeepSeek uses TileLang, letting you write Python → compile to optimized kernels on diverse hardware. E.g., 80 lines of Python can reach 95% of FlashMLA’s (CUDA written https://x.com/Yuchenj_UW/status/1972698599554490856
How does @deepseek_ai Sparse Attention (DSA) work? It has 2 components: the Lightning Indexer and Sparse Multi-Latent Attention (MLA). The indexer keeps a small key cache of 128 per token (vs. 512 for MLA). It scores incoming queries. The top-2048 tokens to pass to Sparse MLA. https://x.com/vllm_project/status/1972617272901644345
As @_xjdr says this is not yet profiled fully but we can provisionally accept that DSA had just enabled 1M contexts for ≈everyone. Some “”prototype””, hah. They always underhype their work. Btw, you can drop “”sparse””. Just DeepSeek Attention, it’s cleaner. https://x.com/teortaxesTex/status/1973209017603465324
DeepSeek V3.2 breakdown 1. Sparse attention via lightning indexer + top_k attention 2. Uses V3.1 Terminus + 1T continued pretraining tokens 3. 5 specialized models (coding, math etc) via RL then distillation for final ckpt 4. GRPO. Reward functions for length penalty, language https://x.com/danielhanchen/status/1972613546119991791
Some notes on DeepSeek’s v3.2’s sparse attention mechanism DSA can be thought of as a noncontiguous sliding window where each token only attends to 2048 other tokens. This means that both memory to load and FLOPs is constant at O(2048) (for decode at least). https://x.com/nrehiew_/status/1973193918662713510
💻 API Update 🎉 Lower costs, same access! 💰 DeepSeek API prices drop 50%+, effective immediately. 🔹 For comparison testing, V3.1-Terminus remains available via a temporary API until Oct 15th, 2025, 15:59 (UTC Time). Details: https://x.com/deepseek_ai/status/1972604777474458033
Official release of DeepSeek-V3.2-Exp with DeepSeek Sparse Attention + massive price cuts! DeepSeek Sparse Attention (DSA) makes inference cheaper (especially long-context) by learning which past tokens matter for each new token and running full attention only on those. DSA https://x.com/scaling01/status/1972613330209485282
🚀 Introducing DeepSeek-V3.2-Exp — our latest experimental model! ✨ Built on V3.1-Terminus, it debuts DeepSeek Sparse Attention(DSA) for faster, more efficient training & inference on long context. 👉 Now live on App, Web, and API. 💰 API prices cut by 50%+! 1/n”” / X https://x.com/deepseek_ai/status/1972604768309871061
🛠 Open Source Release 🔗 Model: https://x.com/deepseek_ai/status/1972604780469239843
DeepSeek-V3.2-Exp/DeepSeek_V3_2.pdf at main · deepseek-ai/DeepSeek-V3.2-Exp https://github.com/deepseek-ai/DeepSeek-V3.2-Exp/blob/main/DeepSeek_V3_2.pdf
deepseek-ai/DeepSeek-V3.2-Exp · Hugging Face https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Exp
Ollama’s cloud models just got an update! ollama run kimi-k2:1t-cloud Welcome Kimi K2 by @Kimi_Moonshot for the 1 trillion parameter model! ollama run deepseek-v3.1:671b-cloud DeepSeek V3.1 Terminus by @deepseek_ai for the updated updated that is better in language”” / X https://x.com/ollama/status/1971750071483167010
Thank you @NVIDIADC for supporting vLLM for @deepseek_ai model launch. Blackwell is now the go to release platform for new MoEs, and we cannot do it without the amazing team from NVIDIA.”” / X https://x.com/vllm_project/status/1973238773090754735
My entry for @AgentSploots contest. Choose @Vader_AI_ and learned so much creating this short video Used a combo of Suno, Veo3, Kling, Grok, and AI in Photoshop. Was very impressed where AI Music is at with Suno and it’s Song editing tools One day a @virtuals_io agent will https://x.com/JDHyper/status/1949962624508084439
