EmbeddedLLM @EmbeddedLLM
Your open-source AI ally. We specialize in integrating LLM into your business. Joined October 2023-
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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 docs.vllm.ai/projects/recip… for more details 😍 Note: currently…
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 docs.vllm.ai/projects/recip… for more details 😍 Note: currently… https://t.co/9B3ImM72Cn
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.
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://t.co/QzzPRvAaNa
🚀 New in vLLM: dots.ocr 🔥 A powerful multilingual OCR model from @xiaohongshu hi lab is now officially supported in vLLM! 📝 Single end-to-end parser for text, tables (HTML), formulas (LaTeX), and layouts (Markdown) 🌍 Supports 100 languages with robust performance on…
🚀 New in vLLM: dots.ocr 🔥 A powerful multilingual OCR model from @xiaohongshu hi lab is now officially supported in vLLM! 📝 Single end-to-end parser for text, tables (HTML), formulas (LaTeX), and layouts (Markdown) 🌍 Supports 100 languages with robust performance on… https://t.co/G4hMCrSHJY
Missed our latest vLLM office hours? We covered hybrid models as first-class citizens in @vllm_project. ✅ Hybrid model support in v1 ✅ Mamba, Mamba2, linear attention ✅ Performance from v0 → v1 ▶️ Recording: youtube.com/live/uWQ489ONv… 📑 Slides: docs.google.com/presentation/d…
We keep pushing the limits of speculative decoding (SD) in LLM inference -- check out our latest NeurIPS’25 paper: Lookahead Reasoning (LR). The high-level rationale is pretty simple: SD alone isn’t enough now: as GPUs get stronger (H200 -> B200 -> Rubin CPX), we'll be able to…
We keep pushing the limits of speculative decoding (SD) in LLM inference -- check out our latest NeurIPS’25 paper: Lookahead Reasoning (LR). The high-level rationale is pretty simple: SD alone isn’t enough now: as GPUs get stronger (H200 -> B200 -> Rubin CPX), we'll be able to…
Day-0 support on one of the most anticipated model releases🚀🚀🚀 Detailed deployment guide coming soon at vllm-recipes docs.vllm.ai/projects/recip…
Day-0 support on one of the most anticipated model releases🚀🚀🚀 Detailed deployment guide coming soon at vllm-recipes docs.vllm.ai/projects/recip… https://t.co/dwXiEVZHEM
Congrats to @deepseek_ai ! DeepSeek-R1 was published in Nature yesterday as the cover article, and vLLM is proud to have supported its RL training and inference🥰
Disaggregated Inference at Scale with #PyTorch & #vLLM: Meta’s vLLM disagg implementation improves inference efficiency in latency & throughput vs its internal stack, with optimizations now being upstreamed to the vLLM community. 🔗 hubs.la/Q03J87tS0
@zephyr_z9 😂 Although AMD is now working pretty well for small to medium sized models
Welcome Qwen3-Next! You can run it efficiently on vLLM with accelerated kernels and native memory management for hybrid models. blog.vllm.ai/2025/09/11/qwe…
Welcome Qwen3-Next! You can run it efficiently on vLLM with accelerated kernels and native memory management for hybrid models. blog.vllm.ai/2025/09/11/qwe… https://t.co/M19eWoXAj2
Deep dive into optimizing weight transfer step by step and improving it 60x!
Deep dive into optimizing weight transfer step by step and improving it 60x!
⚡️ Efficient weight updates for RL at trillion-parameter scale 💡 Best practice from Kimi @Kimi_Moonshot vLLM is proud to collaborate with checkpoint-engine: • Broadcast weight sync for 1T params in ~20s across 1000s of GPUs • Dynamic P2P updates for elastic clusters •…
⚡️ Efficient weight updates for RL at trillion-parameter scale 💡 Best practice from Kimi @Kimi_Moonshot vLLM is proud to collaborate with checkpoint-engine: • Broadcast weight sync for 1T params in ~20s across 1000s of GPUs • Dynamic P2P updates for elastic clusters •…
vLLM Singapore Meetup — Highlights Thanks to everyone who joined! Check out the slides by @vllm_project DarkLight1337 with tjtanaa / @EmbeddedLLM V1 is here: faster startup, stronger CI & perf checks. Scaling MoE: clear Expert Parallelism (EP) setup for single/multi-node +…
vLLM is proud to support the great Kimi update from @Kimi_Moonshot , better tool-calling, longer context, and more! Check the deployment guide at huggingface.co/moonshotai/Kim… 🔥
vLLM is proud to support the great Kimi update from @Kimi_Moonshot , better tool-calling, longer context, and more! Check the deployment guide at huggingface.co/moonshotai/Kim… 🔥 https://t.co/ysxqSJVTBU
Amazing blogpost from @gordic_aleksa explaining internals of vLLM😍
Amazing blogpost from @gordic_aleksa explaining internals of vLLM😍
Go LMCache 🚀
Big energy at the @vllm_project Meetup in Singapore! WEKA’s Ronald Pereira shared how NeuralMesh Axon + Augmented Memory Grid can boost vLLM inferencing. Shoutout to @EmbeddedLLM + @AMD for the strong collaboration.
🚀 Exciting news: DeepSeek-V3.1 from @deepseek_ai now runs on vLLM! 🧠 Seamlessly toggle Think / Non-Think mode per request ⚡ Powered by vLLM’s efficient serving — scale to multi-GPU with ease 🛠️ Perfect for agents, tools, and fast reasoning workloads 👉 Guide & examples:…
🚀 Exciting news: DeepSeek-V3.1 from @deepseek_ai now runs on vLLM! 🧠 Seamlessly toggle Think / Non-Think mode per request ⚡ Powered by vLLM’s efficient serving — scale to multi-GPU with ease 🛠️ Perfect for agents, tools, and fast reasoning workloads 👉 Guide & examples:… https://t.co/AiiFbGH8Go
Great example from @skypilot_org showing how to use vLLM to serve Kimi K2 from @Kimi_Moonshot 😀
Great example from @skypilot_org showing how to use vLLM to serve Kimi K2 from @Kimi_Moonshot 😀
🚀 GLM-4.5 meets vLLM @Zai_org 's latest GLM-4.5 & GLM-4.5V models bring hybrid reasoning, coding & intelligent agent capabilities—now fully supported in vLLM for fast, efficient inference on NVIDIA Blackwell & Hopper GPUs! Read more 👉 blog.vllm.ai/2025/08/19/glm…
John M @Piedrasai
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Gowtham Kumar Reddy @Gowtham1926
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Koge @Koge754219
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LillianSilas @KZILe40T2ku6K
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Hamzé 🦀 @Hamzeml
3K Followers 7K Following I write the bugs that future AIs will be paid to fix. AI Maximalist & Architect of Artisanal Technical Debt! Rust 🦀 supremacy!
Ahmed Omar @Ahmed2Omar20
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murphy @murphy000912
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lipi @lipiisme
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Nilesh Kokane @nils360
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Rahul Atlury @atlury
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Violet @FlyWY2H6Iasxp6D
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Amara @Twortaln9490
37 Followers 2K Following I’m not a one in a million kind of girl, I’m a once in a lifetime kind of woman.
You Jiacheng @YouJiacheng
9K Followers 2K Following a big fan of TileLang 关注TileLang喵！关注TileLang谢谢喵！ https://t.co/utshC0jrCO 十年老粉
Zach Wilson @EcZachly
48K Followers 1K Following Founder @ https://t.co/CWvLDHU2Lx $150k/month | https://t.co/F5VqLpyMZn $5k/month | ADHD | 10 yrs big data experience | ex @meta, @netflix, and @airbnb
Edward Z. Yang @ezyang
14K Followers 1K Following I work on PyTorch at Meta. Chatty alt at @difficultyang.
Michael Rabone @michaelrabone
15K Followers 807 Following Daily AI resources and educational posts. Support me with a coffee, patreon or purchase my style code bundle. Links are in my Linktree. DM's are open.
Junchen Jiang @JunchenJiang
413 Followers 319 Following CS Prof @ UChicago https://t.co/U01oOWGnip (Fast distributed LLM inference) https://t.co/hoetjwXKIt (Best KV cache layer)
Maxsun Official @MaxsunOfficial
8K Followers 40 Following Focus on quality, focus on excellence.💚 | Official X Maxsun Global Account | Product & Collaboration request, please DM us or visit: https://t.co/CRIOIN1L6F
Max Ng @maxnghello
61 Followers 53 Following AI theoretical research. Author of 1st Latent reasoning paper | Teacher-student one-model | Chi-ENG LLM 🏆#1@CIFAR-100|#2@CIFAR-10|#1@OpenWebText2022
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WEKA @WekaIO
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Linus LinusMediaGroup @linusgsebastian
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Binyuan Hui @huybery
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Daniel Romero @HyperTechInvest
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NeRF&3DGS and Beyond @jasonmeyang
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Zhenjun Zhao @zhenjun_zhao
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Gabriele Berton @gabriberton
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Nobuhiro Sue @nobusue
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MJ @mjtechguy
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Chaoyue He @CYH37
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