搜索“RAG”

Technical Blog sessionStorage.removeItem('facets.search.text'); sessionStorage.removeItem('facets.search.text-copy'); sessionStorage.removeItem('facets.search.filters'); Experiment with Qwen3.8-Flash-Next on NVIDIA GB300 NVL72 for Agentic Coding Qwen3.8-Flash-Next is a multimodal mixture-of-experts model with 125B parameters, 6B activated per token, and a native 262,144-token context window extensible to 1M tokens with YaRN. The architecture combines Gated DeltaNet and Qwen Sparse Attention, whe

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model performed well at its task of detecting Alzheimer’s disease from blood samples, but the company didn’t know why. “We reverse-engineered Pleiades and found it was using DNA fragment-length patterns to make its predictions—a signal humans hadn’t used to detect Alzheimer’s before,” recalls Ho. In other words, the team had discovered that Pleiades was using a completely new biomarker for the disease. “As far as we know, it’s the first significant finding in the natural sciences discovered pur

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he details: In tests, OAI's 700-watt chip topped Nvidia’s own options rated at 1,200 watts, answering up to 3.6x faster with up to 1.9x more work per watt. The company said it leveraged its own upcoming Astra model and Codex to create Jalapeño, taking nine months from first design to manufacturing-ready. OAI won't sell the chip, with hardware VP Richard Ho saying it has "so much need for it" internally, and still leans on Nvidia to train new models. Two more generations are already in the works,

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Technical Blog sessionStorage.removeItem('facets.search.text'); sessionStorage.removeItem('facets.search.text-copy'); sessionStorage.removeItem('facets.search.filters'); Related Resources Developer Tools & Techniques English 中文 Aug 25, 2026 Shadow engine recovery in NVIDIA Dynamo moves most recovery work off the serving path by keeping a fully initialized shadow engine idle on the same GPUs as the active engine. The GPU Memory Service shares existing weights between engines without creating anot

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flows. After our initial release, many users found it especially useful for cybersecurity. Attackers are already using AI; OpenWorker is committed to giving defenders the same leverage. Running an agent requires both (i) A model and (ii) A harness (the software around the model). Because the OpenWorker harness is fully open source, security teams can audit it to make sure we haven't built any backdoors that exfiltrate your code and data to some company or even a foreign adversary. OpenWorker now

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Technical Blog sessionStorage.removeItem('facets.search.text'); sessionStorage.removeItem('facets.search.text-copy'); sessionStorage.removeItem('facets.search.filters'); Aug 24, 2026 NVIDIA Groq 3 LPX is the interactive AI inference accelerator for the NVIDIA Vera Rubin platform . Artificial Analysis benchmarked Gemma 4 31B on Groq 3 LPX at 100K context and measured a median speed of 3,431 output tokens per second. The accelerator uses a deterministic execution model with compiler-scheduled chip

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On"; Stylize settings now explain themselves in context, and House style works without Raw; Shift+Enter makes a newline again, and the down arrow returns to your written prompt; Dragging an image now opens the Images sidebar with drop targets for each reference role, floating over the page instead of pushing it; Video mode: dropping images on the Start and End Frame pills now works correctly Gallery and lightbox Redesigned the gallery filters as toggle chips; Sidebar tabs stay visible in the li

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ther accelerators that expose similar functionality (AMD and Intel both have comparable IPC mechanisms); Ops & reliability: metrics, status tooling, security hardening, and CI coverage This is very much a community effort. The full plan is tracked publicly in sgl-project/sglang 33522 — contributions and feedback are very welcome, and there is plenty of impactful work to pick up. Acknowledgements Ant Ling Infra Team, Ant Group: Michael Qiu qiudayu.qdy@antgroup.com Alibaba: Siyu Liu liusy58@smail.

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the throughput offered by most proprietary cloud models (around 140 tok/s, depending on the dataset). Across various multi-tool scenarios, DSpark reduces the latency by 57% on average for LFM2.5-2.6B. For LFM2.5-1.2B-Instruct , we see much more variance in dataset acceptance rates, so speedup varies by as much as 52% depending on the underlying text distribution. For LFM2.5-8B-A1B , the acceptance rate increases compared to two dense models, yet on-device we get only an 18% improvement on avera

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y knowledge for security reasons. Getting real impact from AI means pairing frontier reasoning with retrieval tools that can safely reach your most sensitive material. Traditional RAG falls short Traditional, one-shot RAG retrieves a fixed set of text chunks and asks a model to answer in a single pass. This works when the answer appears in one of the top results, but falters when the model must navigate a long report, follow references, compare multiple documents, or verify the underlying eviden

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Technical Blog sessionStorage.removeItem('facets.search.text'); sessionStorage.removeItem('facets.search.text-copy'); sessionStorage.removeItem('facets.search.filters'); Aug 19, 2026 NVIDIA SkillEvaluator measures how verified skills affect agent performance through static checks and live task runs with and without each skill. More than 300 verified skills across over 30 NVIDIA products were evaluated on two agent harnesses, showing average Skill Lift of 41 points for Correctness and 39 points f

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he CS-1 through CS-3 machines, which had one 16U chassis per rack with one WSE and its host inside. In theory you could put two in a rack with room for scale out networking and storage – if you could cool it. The important thing, according to Lie, is that the Nexus rack is optimized for large scale clusters and has 50 percent fewer components and can be deployed up to 3X faster than prior systems from Cerebras. Here is an image of the backpacks exploding out of the back of the Nexus rack: Note:

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et me zone districts, lay down roads, and grow a town with a live economy that reacts to what I build. Create a 3D physics playground: let me spawn spheres, boxes, and cylinders, drag them around, stack them into towers, and knock them over. Sliders for gravity, bounciness, and friction. Build a beat machine in my browser: a 16-step drum sequencer with classic, 808, techno, and lo-fi kits, tempo and swing controls, and genre presets. Let me beatbox into the mic and turn it into a pattern. Build

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hip model, built for long-running agents and ambitious interactive and visual work. It offers 500k context window and configurable reasoning efforts (low, medium, high, xhigh). Now, Amazon Bedrock users can leverage the model through Amazon Bedrock. Capabilities & Pricing Grok 4.6 is available for: Input : $2 per 1 million tokens Cached input : $0.50 per 1 million tokens Output : $6 per 1 million tokens For more on the model, read the Grok 4.6 announcement . Build with Grok 4.6 on Amazon Bedrock

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concurrency, keeping cache-hit rate high in agentic training. Fully Async RL For long-context, tool-use, and agentic workloads, rollout time is mainly determined by a handful of stragglers. A synchronous schedule exacerbates the straggler issue, because the trainer has to sit idle until the slowest trajectory in the batch returns, and the rollout engines have to wait until the optimizer finishes model update. Miles' fully asynchronous RL eliminates such mutual-blocking by allowing rollout engine

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. There is no reconciler or layout engine in the path. When something renders wrong, the bug is in a function that returns strings. With a rock solid foundation, we then could leverage the extension system to make Auggie Auggie. The extension model Extensions are modules that extend Pi's behavior by leveraging a simple API to intercept with lifecycle events, add UI, customize rendering, and more. Our proprietary context engine is itself just an extension. It registers the codebase-retrieval tool

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Technical Blog sessionStorage.removeItem('facets.search.text'); sessionStorage.removeItem('facets.search.text-copy'); sessionStorage.removeItem('facets.search.filters'); Serve Qwen3.8-2.4T-A95B, a 2.4T-Parameter Model, with Configurable Reasoning on NVIDIA GB300 NVL72 Aug 12, 2026 Alibaba released the open weights for Qwen3.8-2.4T-A95B , a 2.4 trillion parameter mixture-of-experts model with 95 billion activated parameters per token, a hybrid full and linear attention architecture, and a context

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change detection). Tiny is a lightweight encoder but still highly performant; for your own applications, you can swap it for a larger variant at the cost of higher compute and storage. Similarity search: Finding "more like this" Pick a query pixel, extract its embedding, and compute cosine similarity against every other pixel. The result is a heatmap showing where the landscape looks most and least like your query pixel. This query sits near the Merced urban center in California. Urban fabric a

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ive decoding however the prefill worker is sliced. A PP16 prefill worker and a wide-EP decode worker do not agree on how KV is partitioned, and requiring a shared TP layout would drag prefill back onto decode's topology and forfeit everything above. The staging buffer changes what the two sides agree on. Prefill writes completed chunks into staging buffers and publishes a per-peer watermark; decode scatters out of them into whatever layout it uses, prefetching chunk by chunk. The contract is a c

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rowseComp results at 25 turns, comparing frontier models versus budget models across search engines. Varying the engine while holding the model constant changed the score by an average of 10 points while the average gap between frontier and cost-efficient models was larger at 15 points. Across engines, cost varied most for frontier models, where the priciest engine cost 2.5x the cheapest, versus 1.5x for cost-efficient models. The reason a comparison like this is possible at all is that the serv

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