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vLLM, SGLang and TensorRT-LLM? FeatureDeepInfervLLMSGLangTensorRT-LLM MaintainerDeepSeekvLLM projectSGLang projectNVIDIA LicenseApache 2.0Apache 2.0Apache 2.0Apache 2.0 Model coverage1 (DeepSeek-V4.1-Flash)200+ architecturesLLMs, VLMs, diffusionSupport matrix HardwareNVIDIA SM100, Huawei Ascend 950NVIDIA, AMD, Intel, CPUs, plugins incl. Ascend, TPUNVIDIA, AMD, TPU, Intel, Apple, Ascend, Moore ThreadsNVIDIA GPUs Expert parallelismYes, with EPLBYesYesWide-EP Speculative decodingDSpark, confidence

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8,192 Ironwood TPUs. Tuning ladder: In a TorchTitan fine-tuning case, step time fell from 11.49s to 2.78s. DeferAndFuse alone gave 1.15x. Full torch.compile gave 3.80x. Model coverage: Google continuously tests about 5,000 Hugging Face models in eager and compiled modes. Fleet serving: Zenteiq grew daily output from 1.47B to 5.97B tokens per 1,000 TPU v6e chips. That gain came from llm-d routing on GKE, not TorchTPU alone. What integrations ship with TorchTPU? Hugging Face: Transformers v5 depl

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from different sources into a consistent structure and meaning for downstream use. Agentic Identity Resolution: Deterministic, probabilistic, and LLM-based matching help connect fragmented records. Golden record survivorship determines which attributes to retain in the unified profile. Stateful, incremental processing: Micro-batch updates keep profiles current as data changes, while manual data steward overrides allow teams to apply human judgment where needed. Profile Agents and Campaign Agent

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parent category without a recursive SQL common table expression, and the large language model (LLM) sees a single dimensional path rather than a join graph. A representative SML fragment for the product hierarchy: dim product.yml unique name: dim product object type: dimension label: Product hierarchies: - unique name: product category label: Product Category levels: - unique name: department dataset: category key columns: [dept name] - unique name: category dataset: category key columns: [id]

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re of wallet is a compound metric, built from other metrics: Levels and conditions carry through from the components unchanged. Share of wallet is a ratio, so it doesn’t add or average. The share across an AWS Region isn’t the average of each customer’s share, and the metric description tells the agent that. See Strategy Conditional Metrics. Beyond base, advanced, and compound, Mosaic ships governed time-intelligence metrics as first-class expressions. Previous Year, Previous Quarter, Year-to-Da

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lifetime customer value) that defines a reusable spend aggregation queries can join in when they need it. In practice, this modeling is done visually in Denodo Design Studio. You drag Aurora, Amazon Redshift, and the on-premises PostgreSQL source onto a canvas, draw joins between them, and rename or re-type fields (for example, mapping cust ref to customer id) through a form rather than hand-written DDL. Attaching the security policies covered later in this post also happens inside the same UI.

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nomous Data Foundation for the AI era. The platform discovers and classifies cloud resources across AWS, Azure, and GCP. It provides continuous deduplicated backup that reduces storage costs by up to 50 percent in Eon’s internal measurements. It also detects ransomware and rogue-agent damage and delivers live grounded context for AI agents. All of this runs from a single read-only AWS Identity and Access Management (IAM) role, storing data in Apache Parquet and Apache Iceberg formats. Founded by

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tly, and v1 Mini answered 75.9% correctly. Speed Big Bench Audio asks 1,000 reasoning questions by voice and scores the spoken answer. V1 Max starts speaking in 0.57 seconds on average, and v1 Mini in 0.62 seconds. The Pareto frontier for accuracy and speed Accuracy and speed are usually seen as a trade-off. The models that score highest on Big Bench Audio take the longest to answer, and the fastest ones give up a lot of accuracy. v1 Max excels in both of these metrics. Conversational dynamics a

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ynthesizing and sharing the information you’ve given it. That way, at your next appointment, your doctor could maximize their time with you. How can AMIE potentially change the average doctor’s appointment? Anil: AMIE is more targeted and more thorough than a standard doctor’s office intake form. Let’s say I come in with back pain. While an intake form might ask me to describe the problem and check a few boxes, AMIE can ask follow-ups. What kind of pain? When did it start? Where is it in my back

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rticipation?” The response was reply-all chaos that's still unfolding today. Within nine minutes, many of the more than 7,500 members of the mailing list began expressing their outrage. Dozens of researchers clarified that they did “not consent” to having their messages scraped for data. Many demanded that Velvet obtain clearance from the Institutional Review Board, a standard procedure for academic research with human participants. Several left the listserv entirely, with some announcing their

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sen Horowitz 的合伙人，专注于开发者工具、基础设施、AI 和创意工具。 X Linkedin 是 Andreessen Horowitz 的管理合伙人，同时也是 Growth 和 Infrastructure 投资团队的普通合伙人。 X Linkedin 扩展 a16z Growth Fund 和 Platform David George 和 Raghu Raghuram 想要更多 a16z Infra 内容？ 分析和新闻，涵盖重塑 AI 和基础设施的最新趋势。了解更多 Infra 投资 TypeSafe AI Infra 投资 Preference Model Jennifer Li Bio + Health 投资 General Medicine Infra 投资 Preference Model Jennifer Li Bio + Health 投资 General Medicine Crypto 投资 Underdog 的创造者 Conway Infra 投资 doxxnet Joel de la Garza 和 Martin Casado Infra 投资 Pr

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s into preserved training progress and where the rest goes, and with Multi-Tier Checkpointing on GKE, which keeps checkpoints in local memory and in-cluster replicas with Cloud Storage as the durable tier, so large jobs save often and resume quickly. On precision, the float8 recipe that today ships for Llama 3 70B, built on torchao's Float8Linear and v7x's native fp8 matmuls, extends to the rest of the model set. On hardware, Google has announced its eighth-generation TPUs, TPU 8t for pre-traini

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t's KV cache has to travel from the prefill host to the decode host.Tiered KV offload moves the part of the KV cache that no longer fits in HBM to the host's DRAM and secondary storage with Google Cloud Managed Lustre, and brings it back when a later request reuses the prefix. Either way, the transfer has to overlap computation or it adds directly to latency. Both engines use TPU Sync for this. It is an open-source C++ library for KV cache transfer and management on TPU. It reads the device buff

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35B multimodal AI. Running locally on an iPhone. Now at over 20 tokens/sec. 🚀 MiniCPM-V 4.7 achieves 20.51 tokens/sec average decoding speed on iPhone 18 Pro Max (12GB) — a 6.07× speedup over the same device’s unoptimized SSD expert-loading baseline. ⚡ Behind the speed: Lookahead Expert Scheduling (LES), optimized expert loading, fine-grained quantization, and DSpark speculative decoding. 📱 Watch the new on-device demo below. Powerful multimodal AI, right in your pocket. MiniCPM OpenBMB

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入场券”，之后要一场一场把比赛打下来。客户不为情怀买单，只认数据库拿不拿得下、出了问题有没有人管。 这也是他做了二十多年CPU最深的一个体会：交付比表态重要，产品比指标实在。一颗处理器的价值，不取决于它本身多强，而取决于有多少人在它上面做出了东西。 玄铁不再满足于做性能标杆，而要交一份“量产答案”，用60亿颗出货证明路走得通，用XT-LINK、E908A、Dragonwell和2796个测试用例，把“能用”一寸一寸改成“敢用”。处理器从来不是造出来就算成功，是用起来才算数。 此内容为集微网原创，著作权归集微网所有，爱集微，爱原创 相关推荐 达摩院玄铁出席RISC-V中国峰会，首发车规产品路线图 玄铁 Titan | 以高性能向量引擎加速端侧 AI 推理 Day 0 适配 | 玄铁 RISC-V 处理器支持 Qwen3.8-27B，“Opus 级”Agent 能力落地 南芯科技与阿里达摩院签署合作备忘录 达摩院玄铁：CPU 负载计数器（Sscpuutil），实现动态调频下的精准 CPU 负载校正 “能跑”到“敢用”，达摩院要让RISC-V趟过量产深水区 15小时前 存储超级周期何时见顶？

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ve To Handle, With Many Bonds Secured By GPUs and Infrastructure With Questionable Post-Bubble Value With endless delays and hundreds of billions of dollars of chips sitting in storage, AI data centers have dramatically turned from the panacea for all infrastructure investment to fixed-income nightmare fuel. For whatever reason, nobody thought that lending hundreds of billions of dollars to a selection of different dodgy SPVs and neoclouds to build the world’s most ambitious infrastructure proje

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ies and Exchange Commission Anthropic raises $65B in Series H funding at $965B post-money valuation Anthropic has raised $65 billion in Series H funding led by Altimeter Capital, Dragoneer, Greenoaks, and Sequoia Capital.May 28, 2026May 28, 2026 Anthropic raises $65B in Series H funding at $965B post-money valuation Anthropic has raised $65 billion in Series H funding led by Altimeter Capital, Dragoneer, Greenoaks, and Sequoia Capital. Anthropic opens Milan office to support Italian enterprise,

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on 上下文管理器 Eager 执行、StableHLO 降级和 JIT 编译 遵循 此 PyTorch 指南, 我们通过一个新的 C++ 后端扩展了 PyTorch 调度器，注册为 PrivateUse1. 此外，我们使用了标准的 at::Tensor 机制来定义张量存储的设备特定实现（通过在 c10::UniqueVoidPtr 内使用 c10::StorageImpl). TorchTPU eager 执行的一个独特特性是，它可以通过多种模式将 Aten 算子调度到 TPU： 立即, 有效实现逐算子调度； 延迟 一直到自动确定的 物化点 （例如，一个 .to("cpu") 语句），并与截至该点被延迟的所有其他算子一起聚合/编译； 介于两者之间的任何情况。 虽然这种灵活性确实给设计带来了额外的复杂性，但我们从一开始就刻意加入它，因为我们希望尝试不同的策略，以在基本的逐算子调度模式之上提升 eager 性能。（此外，事实证明这种灵活性对于处理视图语义和 torch.compile 非常方便，正如本文档后面所讨论的。） 总之，TorchTPU 提供 3 种 eager 模式，可以通过环

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oundation. “The strength of our ecosystem is how we work together to tackle AI’s toughest scaling challenges. By participating directly in our open source community, Nebius is leveraging and advancing PyTorch Foundation projects like vLLM and PyTorch to drive both high-throughput inference performance and large-scale model training.” As a platinum member, Nebius is granted one seat to the PyTorch Foundation Governing Board. The Board sets policy through our bylaws, mission and vision statements,

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l (MCP), OpenChain, OpenSearch, OpenSSF, OpenStack, PyTorch, Ray, RISC-V, SPDX and Zephyr, provide the foundation for global infrastructure. The Linux Foundation is focused on leveraging best practices and addressing the needs of contributors, users, and solution providers to create sustainable models for open collaboration. For more information, please visit us at linuxfoundation.org. The Linux Foundation has registered trademarks and uses trademarks. For a list of trademarks of The Linux Found

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