Agentic orchestration layers allow AI agents, enterprise systems, and data connections to work together across functions. As cognitive bottlenecks shrink, that allows decision-making to speed up, coordination to improve and new operating models to form. mck.co/4fJb564

Caring a lot about harnesses rn feels similar to caring about tabs vs spaces

Read the news ⬇️ nvidianews.nvidia.com/news/n…

Speaking of audacity… who won your RTX Pro 6000 giveaway Ahmad???

Ok got the facts straight from NVIDIA. This box at the NVIDIA fair is an empty shell to highlight the concept. NVIDIA is planning to launch a switch for sparks. However the final design is currently to be decided - it's not certain if it will look like this. And as for launch date- not known at the moment. Well, I told them that if they can make it like this one the marketing has been already done and it will sell like crazy 😇

DSV4.1-Flash-1017 across 1 @ RTX Pro 6000 MaxQ and 2 @ DGX Sparks (ASUS GX10s). Full NVFP4. Currently at 54 tok/s without DSpark single. Lots of cooking left to do here.

Big Announcement: Meet Imagination Studio🎬🎵 by @u1tra_instinct and Me! Our local open source all in one creative platform on your own hardware has been fully redesigned and rebuilt from the ground up. Video,Audio, Music, Image. Releasing ASAP with latest+best local models.

Marvell’s Dave Lazovsky is holding a tiny module that uses photonics to move high-bandwidth memory (HBM + DDR) as far as 50 meters away from the GPU. this can sit up to 50 meters from a GPU instead of the usual 1.5 mm. For the first time, memory no longer has to live right next to compute, letting AI clusters share massive capacity without being locked to the accelerator package. Latency stays in the hundreds of nanoseconds, close enough for practical memory use. Basically, Memory and compute now scale on separate axes instead of being locked together.

When talking to aussies or drinking my swear words per sentence increases dramatically on its own

🤯 A localmaxxer hit ~381 tok/s on a SINGLE RTX 3090 with Qwen3.8-27B. This developer has turned a 24GB RTX 3090 into a monster Qwen inference box - w/some creativity. Four days ago 👉 ⚡ ~82 tok/s single-user Then 👉 ⚡ ~114 tok/s with optimized MTP ⚡ ~138 tok/s with DFlash2 + lookup drafting Now 👉 🔥 ~381 tok/s on ONE request How? The recipe combines ... 🧠 Qwen3.8-27B 🎮 1× RTX 3090 24GB @ 250W ⚙️ heavily optimized vLLM ⚡ DFlash2 speculative decoding 🔎 lookup-augmented drafting 📚 prefix caching 🧮 16-token verification blocks 💾 quantized KV / heads / activations DFlash2 normally proposes 7 tokens. The developer realized the verification block doesn't have to stop there. If Qwen is answering from a document already sitting in the prompt, the system can fill the remaining draft positions using tokens found directly in that context. 🎯 So the target model can verify 16 tokens at once. On a ~25K-token document reproduction task: Previous DFlash2 👉 ~260 tok/s Longer verification + context lookup 👉 🔥 ~382 tok/s Acceptance: 🤯 15 of 16 tokens per verification step That is where the crazy number comes from. ⚠️ On ordinary real-world chat prompts, the same setup is around ~133 tok/s Still extremely fast for a dense 27B model on an RTX 3090. The 381 tok/s mode shines when the answer largely comes from material already in context so these are best use cases 📚 RAG / document Q&A 💻 Coding assistants applying edits 📝 Quoting or rewriting documents 🔎 Extracting information from long prompts And another optimization 👉 With prefix caching, a second question against the same 25K-token document reportedly goes from: 🐌 22.4 sec TTFT → ⚡ 0.56 sec TTFT Because the model doesn't need to process the whole document again. 🎯 It's specifically a mode for RAG front ends and coding agents. Follow iamMess on Reddit or syv-ai on GitHub 🔗 Reddit: r/LocalLLaMA/comments/1vtup5s/ 🔗 GitHub: /syv-ai/qwen38-27b-rtx3090

Canadian friends looking to do local Ai. 👀👀👀 No affiliation. Just sharing a great deal. DYOR! ebay.ca/itm/277975151573?mke…

It's better than an RTX 6000 for a third of the price.

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