CloudThrill

📖 #Blog "𝑇ℎ𝑒 7 𝑃𝑎𝑟𝑎𝑚𝑒𝑡𝑒𝑟𝑠 𝑇ℎ𝑎𝑡 𝑆ℎ𝑎𝑝𝑒 𝐿𝐿𝑀 generation" 💎 𝗧𝗼𝗽-𝗞, 𝗧𝗲𝗺𝗽𝗲𝗿𝗮𝘁𝘂𝗿𝗲 & 𝗙𝗿𝗶𝗲𝗻𝗱𝘀 𝗗𝗲𝗺𝘆𝘀𝘁𝗶𝗳𝗶𝗲𝗱 👉 Full post: [https://lnkd.in/eNRnFnKw] ✍🏻 by Kosseila H. Every LLM generation you've ever seen was shaped by parameters most people never touch. This post breaks down the 7 levers that actually control what your model says, and how to tune them for your use case. Includes an 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲 𝗽𝗹𝗮𝘆𝗴𝗿𝗼𝘂𝗻𝗱 to visualize each one in real time. 🎛️ 𝗜𝗻 𝘁𝗵𝗶𝘀 𝗽𝗼𝘀𝘁, 𝘄𝗲 𝗰𝗼𝘃𝗲𝗿: ✅ 𝗧𝗲𝗺𝗽𝗲𝗿𝗮𝘁𝘂𝗿𝗲 — precision vs. creativity, and when to use each ✅ 𝗧𝗼𝗽-𝗞 & 𝗧𝗼𝗽-𝗣 — how your model actually picks the next token ✅ 𝗙𝗿𝗲𝗾𝘂𝗲𝗻𝗰𝘆 & 𝗣𝗿𝗲𝘀𝗲𝗻𝗰𝗲 𝗣𝗲𝗻𝗮𝗹𝘁𝗶𝗲𝘀 — killing repetition and forcing novelty ✅ 𝗦𝘁𝗼𝗽 𝗦𝗲𝗾𝘂𝗲𝗻𝗰𝗲𝘀 — hard output boundaries ✅ 𝗠𝗶𝗻-𝗣 𝗦𝗮𝗺𝗽𝗹𝗶𝗻𝗴 — the smarter dynamic alternative to Top-P ✅ 🎚️ 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲 LLM Output Simulator — to visualize everything #AI #LLM #InferenceOptimization #GenerativeAI #MLEngineering

#OpenSource VS. #OpenWeight Minimax 𝗠𝗶𝘀𝗹𝗲𝗮𝗱𝗶𝗻𝗴 𝗰𝗹𝗮𝗶𝗺: The 𝗠𝗶𝗻𝗶𝗠𝗮𝘅 𝗠𝟮.𝟳 𝗺𝗼𝗱𝗲𝗹 weights are publicly available, but under a license prohibiting commercial use without authorization. This does not meet the Open Source Initiative's definition of open source, which requires allowing commercial use. 💡Know what's locally free from what's free for business.

🚨CloudThrill is excited to announce that it is now officially listed on Canada’s AI Supplier Source List 🇨🇦🍁 👏🏻👏🏻👏🏻 Read full statement: 👉 [https://lnkd.in/evjAeyAj] Proud to support responsible, sovereign, and production-grade AI systems for the public sector. #CanadaGov #AI #GovTech #ResponsibleAI

#BlogSeries 𝗗𝗶𝗳𝗳𝘂𝘀𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹𝘀 𝟭𝟬𝟭: 𝗙𝗿𝗼𝗺 𝗡𝗼𝗶𝘀𝗲 𝘁𝗼 𝗣𝗶𝘅𝗲𝗹𝘀 🖼️ 👉𝐑𝐞𝐚𝐝 𝐡𝐞𝐫𝐞: https://lnkd.in/gV7AWkk8 by Kosseila H. All the questions you never dared to ask about diffusion models, and more. This is a (thorough) deep-dive on diffusion models with tailored visuals, clear, & simple explanations. Written to bridge the gap between research papers and AI builders. No PHD needed. 💡 In this deep-dive you'll learn: ✅ What is diffusion ?— the physics, the logic, examples ✅ The Denoising Loop — where the magic happens ✅ Diffusion solvers demystified: DDPM➡DDIM➡ Flow Matching ✅ CFG & Negative Prompts — your Image generation compass ✅ The Diffusion models journey— from the eyes(CLIP) to the brain (Imagen) ✅ VideoPoet & Lumiere — the game-changing video models Read it once and you'll never look at an AI image the same way again. #Multimodal #VLM #Diffusionmodel #Dit #AIVideo #AIPapers

📖 #BlogAlert "𝑁𝑉𝐼𝐷𝐼𝐴 CMX " 💎 𝗡𝗩𝗜𝗗𝗜𝗔 𝗖𝗠𝗫 𝘃𝘀. 𝗜𝗻𝘁𝗲𝗹 𝗢𝗽𝘁𝗮𝗻𝗲: 𝗦𝗼𝗹𝘃𝗶𝗻𝗴 𝘁𝗵𝗲 𝗞𝗩 𝗖𝗮𝗰𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺? 👉 Full post: [https://lnkd.in/eKvDnyFn] ✍🏻 by Kosseila hd The new inference wall isn't compute. It's 𝗖𝗼𝗻𝘁𝗲𝘅𝘁. NVIDIA shipped a new hardware answer called 𝗖𝗠𝗫, but Intel had one first, and killed it in 2022. This post breaks down how 𝗖𝗠𝗫 compares to 𝗢𝗽𝘁𝗮𝗻𝗲 𝗣𝗠𝗲𝗺 across architecture, scale, persistence and what that means for LLM inference infrastructure. 💫 𝗜𝗻 𝘁𝗵𝗶𝘀 𝗽𝗼𝘀𝘁, 𝘄𝗲 𝗰𝗼𝘃𝗲𝗿: ✅ Why 𝗞𝗩 𝗖𝗮𝗰𝗵𝗲 is the new inference bottleneck ✅ 𝗞𝗩 𝗖𝗮𝗰𝗵𝗲 𝗼𝗳𝗳𝗹𝗼𝗮𝗱𝗶𝗻𝗴 :how it works ✅ Software solutions (LMCache Lab) ✅ What 𝗡𝗩𝗜𝗗𝗜𝗔 𝗖𝗠𝗫 actually is and where it sits in the stack ✅ Why 𝗢𝗽𝘁𝗮𝗻𝗲 was architecturally ahead — and why the medium matters ✅ Where 𝗔𝗠𝗗 fits in this picture ✅ CMX vs. Optane PMEM: Full Comparison ✅ Why the 𝗽𝗲𝗿𝗳𝗲𝗰𝘁 𝘃𝗲𝗻𝗱𝗼𝗿-𝗮𝗴𝗻𝗼𝘀𝘁𝗶𝗰 should exist #AI #LLM #InferenceOptimization #NVIDIA #KVCache #Optane #CMX

Happy #Easter! Wishing you a joyful day filled with renewal and inspiration.  🐇 #HappyEaster #Spring #NewBeginnings

#OpenSouce VS. #OpenWeight 🟥Open Weight doesn't mean Open Source. Recently Mistral AI released an #OpenWeight Text to speech model #Voxtral But this model was not open-sourced. The model weights are released as open-weights under the 𝗖𝗖 𝗕𝗬-𝗡𝗖 𝟰.𝟬 𝗹𝗶𝗰𝗲𝗻𝘀𝗲 (Creative Commons Attribution-NonCommercial), which 𝗲𝘅𝗽𝗹𝗶𝗰𝗶𝘁𝗹𝘆 𝗽𝗿𝗼𝗵𝗶𝗯𝗶𝘁𝘀 𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗶𝗮𝗹 𝘂𝘀𝗲. It cannot be used for any paid products or services. 💡Know what's locally free from what's free for business.

🚀#BlogSeries #vLLMOmni  𝐯𝐋𝐋𝐌-𝐎𝐦𝐧𝐢 𝗜𝗻𝘁𝗿𝗼: New standard for multimodal inference 🖼️ 👉 Check it out: [https://lnkd.in/gfGmMBrh] by Kosseila H. 👋🏻 Modern AI is a mess of text, video, and audio. But most inference engines weren't built for it. 💎 The answer ? 𝘃𝗟𝗟𝗠-𝗢𝗺𝗻𝗶: one core engine where LLMs meet Diffusion Transformers. If you've been curious about vLLM's 𝗢𝗺𝗻𝗶 extension, this one is for you🫵🏻. Dive in to learn about: ✅ Two in One Design: Text (AutoRegression) x Dit (Diffusion Transformers). ✅ Architecture: Native Disaggregation, Omni-Modality serving ✅ Omni-Pipelines: text, image, video, and audio in a flow. ✅ Diffusion Acceleration: from cashing to Ring-Attention. ✅ Online Inference Examples — code templates The "Rolls Royce" of inference just went multimodal. 🏎️ #vLLM #vLLMOmni #DiffusionTransformers #LLM #MultimodalAI

Happy GTC week!

#OpenSourceAI for the win 💚!! There’s a confirmed report that #Nvidia will spend $26 billion over the next five years building the world’s best open source models. Jensen presumably has GTC remarks incoming on this. America is genuinely back in the open source AI race, and it’s Nvidia leading the charge 👀.