

Do you actually realize what's happening? Deepseek just dropped a 1.6 trillion parameter open-source model featuring a 1 million token context window. OpenAI is charging $200/month. but China is giving it away completely free. Let that sink in.

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Do you actually realize what's happening? Deepseek just dropped a 1.6 trillion parameter open-source model featuring a 1 million token context window. OpenAI is charging $200/month. but China is giving it away completely free. Let that sink in.
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Very cool! Can we export easily to http://
hf.co/datasets?

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Here is a high-level overview of my Local RAG / AI Knowledge Stack All hosted locally on a single RTX 3070 8GB btw Who is interested in a more in-depth breakdown? What would you like for it to cover?

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Thanks for inviting me @garrytan
, was awesome to chat and loved the inspirational space! Great to see so many startups building with @googlegemma models!

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Super cool to see Nemotron-Personas-Korea hit #1 on @huggingface
. It's also the first Korean persona dataset. Huge shoutout to the team and the dev community pushing open datasets forward
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Happy Friday!
— NVIDIA AI (@NVIDIAAI) 24 avril 2026
We just put DeepSeek-V4-Pro up on https://t.co/es07MrTxSs. It’s the world’s largest open source model at 1.6T parameters, and you can run it for free running on NVIDIA Blackwell GPUs.
Try the NVIDIA NIM API → https://t.co/zeWX4Y7Ipd pic.twitter.com/lNFsziIts4
Happy Friday! We just put DeepSeek-V4-Pro up on http://
build.nvidia.com. It’s the world’s largest open source model at 1.6T parameters, and you can run it for free running on NVIDIA Blackwell GPUs. Try the NVIDIA NIM API → https://
build.nvidia.com/deepseek-ai/de
epseek-v4-pro?ncid=so-twit-300913
…

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Day-0 support for @deepseek_ai V4 Pro and Flash on vLLM — a new generation of DeepSeek model, purpose-built for tasks up to 1M tokens. Alongside the release, we're publishing a first-principles walkthrough of the new long-context attention and how we implemented it in vLLM. x.com/deepseek_ai/st…

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DeepSeek V4 by @deepseek_ai just dropped! SGLang is ready on Day 0 with a full stack of optimizations from architectures to low-level kernels. We also deliver a verified RL training pipeline in Miles (by @radixark) for V4 at launch: Native "ShadowRadix" Design: DeepSeek V4's
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Ideal fix would be for the HF models directory to grow a direct understanding of the structure of those kinds of repos and treat them as individual models that can be listed separately, including filter by quantization type
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Will play around! Open source execution over chat is what ML work has been waiting on.