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

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.

JUST IN: Canadian man wins city council election after telling voters he has no idea what he's doing, doesn't enjoy politics, & is unqualified for office.

Nvidia RTX 5090 shipment worth $470,000 goes missing in Brazilian airport tomshardware.com/pc-componen…

1M-token context on one DGX Station GB300: two models, measured. We gave GLM-5.3-Flash and DeepSeek V4.1 Flash 1017 needle-in-a-haystack prompts of 125K, 250K, 500K and 1M tokens, each on a single GB300. Every prompt was read cold (0% prefix-cache hits). Needles found (out of 6): GLM-5.3-Flash NVFP4: 6/6 at 125K, 6/6 at 250K, 6/6 at 500K, 4/6 at 1M DeepSeek V4.1 Flash 1017: 4/6, 4/6, 5/6, 4/6 Time to first token: GLM: 5.0 s, 10.2 s, 21.6 s, 48.8 s DeepSeek: 4.7 s, 9.8 s, 20.9 s, 51.2 s Prefill speed stays above 19.8K tok/s even at 1M (GLM 25.6K to 20.7K, DeepSeek 27.0K to 19.8K). Decoding after a 1M-token prompt: 188 tok/s on both. The test: each length has 6 items hidden in public news text. 4 are single needles at different depths, and 2 are two-hop chains: find which vault an agent uses, then find that vault's code. What the misses tell you: - Every miss on both models is a two-hop chain. Single needles never missed at any length. - GLM's two misses at 1M weren't wrong answers. Both spent the whole 1,500-token reply budget reasoning and never answered. A bigger budget might pass; we haven't tested that. - DeepSeek's older checkpoint misses the same items, so this isn't a regression in 1017. Setup: - GLM on SGLang with the DFlash2 drafter, all in GB300 memory. It needs --mem-fraction-static 0.88 to fit a 1M-token KV cache (1,147,584 tokens). - DeepSeek on vLLM with the DSpark drafter and cold experts in Grace memory. Caveat: 6 items per length is a small sample. Treat this as a capability check, not a benchmark. Full method, scripts and logs: github.com/RonanLabsAI/dgx-s… @Zai_org, @deepseek_ai

🚀 DeepSeek-V4.1-Flash-1017 is here! Stronger benchmark performance, faster task completion, and shorter outputs than the previous version—a Pareto improvement in performance and cost. Now live on our website, app, and API. 🛠️ Optimized for @DeepSeekHarness, Tencent CodeBuddy, WorkBuddy, and more. Upgrade to DeepSeek Harness v0.2.1-rc.1 or later for the best experience with the new model. 1/4

Chinese bride gifts her partner a flagship GPU at wedding — he breaks down in tears She handed him an ASUS ROG Astral RTX 5090 that costs around $12,000

The Tesla Roadster Kickflip!

New Roadster is beyond movies, beyond imagination, beyond human, yet still made by humans on Earth

I've migrated to the @SpaceXSI unified X subscription. Level Super. You loose access to third party models in Cursor, but not a problem for me, I use OpenRouter in case. The real problem is that I tried to use it 5 minutes and I reached 6% of weekly usage. 74K tokens and a lot of images (far too many) simply because UI generates ton of them just scrolling with the mouse. So be aware of this!

I've never seen the open-source world so defensive of their code being copied than since they started having AI write it for them.

For the cost of 8 years of $200 Claude subs, you can force your team to use shitty quantized versions of DeepSeek v4 Flash running at 30tps. Incredible deal!