搜索“NVIDIA”

ks It won't kill you, or humanity, to get off the AI upgrade treadmill New models arrive so fast, we already have more new tech than it's possible to understand in a career Cisco, Nvidia team up to build fatter, faster pluggable If you thought 1.6 Tbps was fast, just wait, Cisco's USFP targets 12.8 Tbps in a single optical pluggable TOP STORIES Anthropic asks users to stop being mean to Claude; '123456' password linked to Denmark data breach affecting 8.8M people; Microsoft and Anthropic play in

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GDN、Lightning Attention 等新一代注意力机制提供高效实现。 其核心价值在于，将这些原本复杂且工程实现门槛较高的算法，转化为可直接调用的高性能算子，使开发者无需深入底层优化，也能在大模型中高效使用线性注意力能力，显著提升长上下文场景下的推理效率与吞吐表现。 cuLA 基于 CuTe DSL 与 CUTLASS C++ 进行手工优化，针对 NVIDIA 新一代 GPU 架构进行了深度适配。同时，cuLA 在接口层与 flash-linear-attention（FLA）保持一致，开发者只需修改一行 import 即可完成替换，实现低成本接入。当前 cuLA 以独立库形式迭代，未来将通过 FLA 的 kernel dispatch 机制实现统一调度，进一步降低使用门槛并提升工程可维护性。 为什么做 cuLA 线性注意力（Linear Attention）正在成为长序列建模的核心范式，通过将复杂度从 降到 ，使百万级 token 的建模成为可能。GLA 、KDA (Kimi Delta Attention) 、GDN 、Lightning Attention 等变体进一步

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see the list of currently available models for Hosted Training, do: Beyond our own INTELLECT-3 model, many awesome small and large models are available from organizations such as NVIDIA, Arcee, Hugging Face, Allen AI, Z.AI, Alibaba Qwen, and many more launching soon. We've also added experimental multimodality support and have deployed our first models to train on environments with image input. Runs are configured via a simple .toml file: To start a training, run: You can then view logs, traini

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的编码任务。 显而易见的问题是：你需要什么样的电脑？ 在本地运行大型语言模型所需的计算资源远高于普通软件。关键的限制因素是内存——具体来说，在大多数系统上是 RAM，如果使用独立显卡加速则是 VRAM。 Block 的文档建议，32 GB 的 RAM 可提供“较大模型和输出的可靠基线”。对于 Mac 用户来说，这意味着电脑的统一内存是主要瓶颈。对于使用独立 NVIDIA 显卡的 Windows 和 Linux 用户来说，GPU 内存（VRAM）对加速更为重要。 “你不需要运行最大的模型就能获得出色的结果，”Sareen 强调说。实用建议是：先用较小的模型测试你的工作流程，然后根据需要扩大规模。 作为参考，Apple 入门级 8 GB RAM 的 MacBook Air 在运行大多数能力较强的编码模型时会很吃力。但 32 GB 的 MacBook Pro——在专业开发者中越来越常见——可以轻松应对。 搭配本地 LLM 的 Goose 并非 Claude Code 的完美替代品。这一比较涉及开发者应当了解的实实在在的权衡。 模型质量：Anthropic 的旗舰模型 Claude 4.5 O

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e, Jianwei Zhang, for open-sourcing the model; Flash Linear Attention team, including Yu Zhang, etc. for reviewing the gated deltanet attention kernels and improving the numerics; NVIDIA, including Vadim Gimpelson for testing the models; IBM Research, including Thomas Parnell for hybrid memory management and CUDA graph optimizations; Red Hat, including Tyler Michael Smith, Doug Smith, Tarun Kumar, and Elvir Crncevic for testing the model and tuning MoE kernels; Community partners: Meta, Roblox,

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thank Cyrus Leung for his support in helping shape the overall concept of extending vLLM beyond text generation. Finally, we would like to thank the TerraTorch team at IBM, especially Paolo Fraccaro and Joao Lucas de Sousa Almeida, for their help with integrating the generic TerraTorch backend in vLLM. Related Posts Scaling Multi-GPU Video Captioning with PyNvVideoCodec and vLLM How to leverage NVIDIA Hardware Video Decoders to Achieve Multi-GPU Scaling in Video Captioning and Description tasks.

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are excited to release the Prime Collective Communications Library, a low-level communication library built for distributed training across the globe. Communication libraries like NVIDIA’s NCCL assume fast, stable connections—ideal for supercomputers, not the public internet. PCCL is built with fault tolerance as a first-class citizen. There is no bad time to kill a PCCL peer, even with multiple concurrent all-reduces or during peer-reconfiguration. Every possible state of the system is designed

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M Granite, and the many countless people & labs in the community. GGML Thank you to the GGML team for the tensor library that powers Ollama’s inference. Being able to access GGML directly from Go has given a portable way to design custom inference graphs and tackle harder model architectures not available before in Ollama. Hardware partners Thank you to hardware partners for collaborating and helping to improve inference on a large number of devices - NVIDIA, AMD, Qualcomm, Intel, and Microsoft.

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riss Guessous, Aston Zhang. We also thank the AMD team for their support in enabling these models on MI300X: Hongxia Yang and Weijun Jiang. The vLLM team’s performance benchmarks were run on hardware generously provided by Nebius and NVIDIA. Related Posts TML Inkling on vLLM: Day-0 Support with Optimized Performance vLLM brings day-0 support to TML Inkling, a 1T-parameter multimodal model, with MTP, long-context serving, parallelism, and up to 380 tokens per second per user on NVIDIA GB200 GPUs.

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ues, Andrew D. White. Work at FutureHouse is supported by the generosity of Eric and Wendy Schmidt. The results and models reported in this work used compute resources from the National AI Research Resource Pilot, including support from NVIDIA, including NVIDIA’s DGX Cloud product which includes the NVIDIA AI Enterprise Software Platform. ‍ Read the preprint: arxiv.org/abs/2412.21154 See the Aviary environment code: github.com/Future-House/aviary See the agent code: github.com/Future-House/ldp ‍

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ation and hinders inference performance. One year ago, when vLLM was first released, we mainly optimized for relatively large models on GPUs with limited memory (e.g. Llama 13B on NVIDIA A100-40G). As faster GPUs with larger memory (like NVIDIA H100) become more available and models become more optimized for inference (e.g. with techniques like GQA and quantization), the time spent on other CPU parts of the inference engine becomes a significant bottleneck. Specifically, our profiling results sh

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LLM is a fast, easy-to-use, open-source serving engine for large language models. vLLM has support for more than 40 types of open-source LLMs, a diverse set of hardware platforms (Nvidia GPU, AMD GPU, AWS Inferentia, Google TPU, Intel CPU, GPU, Gaudi, …) and all kinds of inference optimizations. Learn more about vLLM here. For the new Llama 3.1 series, vLLM can run the models with a full 128K context window. In order to support a large context window, vLLM automatically enables chunked prefill.

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l expand exponentially, unlocking new use cases from everyday tasks to scientific research. Despite an explosion in demand, the AI compute market remains primitive and fragmented. NVIDIA's incentives contribute to this fragmentation, while providers like AMD, Groq and others further broaden the landscape to pick choose from with specialized chips. The market still lacks the efficiency seen in exchanges. Fragmented Pricing: H100 GPU costs fluctuate wildly from 1.2to1.2 to 1.2to18 per hour across

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ingFace Transformers (HF), the most popular LLM library and HuggingFace Text Generation Inference (TGI), the previous state of the art. We evaluate in two settings: LLaMA-7B on an NVIDIA A10G GPU and LLaMA-13B on an NVIDIA A100 GPU (40GB). We sample the requests’ input/output lengths from the ShareGPT dataset. In our experiments, vLLM achieves up to 24x higher throughput compared to HF and up to 3.5x higher throughput than TGI. The Secret Sauce: PagedAttention In vLLM, we identify that the perfo

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ted, multi-GPU fashion). Their computational and memory optimizations make inference much faster than popular frameworks like PyTorch or Tensorflow. On top of this, we use Triton, Nvidia's inference server, which is super fast and scalable. Distillation: For further speedup, we perform knowledge distillation of CodeGen model with 2B parameters down to a lightweight and fast student model with roughly 1B parameters. The student model is trained to reproduce the larger model closely while having f

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