搜索“NVIDIA”

s by wide margins Flexible architecture for any environment: Devstral is available in multiple sizes. The open-weight Devstral Small (24B, Apache-2.0) runs efficiently on a single Nvidia RTX 4090 or Mac with 32 GB RAM—ideal for self-hosted, air-gapped, or experimental workflows. The larger Devstral Medium is available through enterprise partnerships and our API for more advanced code understanding and planning capabilities. Open model for extensibility: Teams can fine-tune Devstral Small on prop

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nodes, resulting in many more collectives (NCCL) running across the scale-out network (InfiniBand or RoCE) instead of the scale-up network (NVLink). This is a major motivation for NVIDIA’s NVL72 design, where the scale-up network expands beyond standard 8-way servers. Expert choice routing, introduced by Google in 2022, flips the logic: experts choose the top-N tokens. Taking the same T x E tensor produced by the router, in expert choice routing a topN softmax is run on the T dimension which pro

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ng Mo and Kaichao You for their significant contributions. We also appreciate the efforts of the MiniMax engineering team, especially Gangying Qing, Jun Qing, and Jiaren Cai, whose dedication made this work possible. Related Posts Day 0 Support for Qwen3.8-2.4T-A95B on vLLM Day-0 vLLM support for Qwen3.8-2.4T-A95B: a 2.4-trillion-parameter hybrid MoE model served out of the box, with FP8/BF16 checkpoints plus NVFP4 and MXFP4 quantized weights, and co-developed kernels on NVIDIA and AMD hardware.

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This observation aligns with the practices of similar strong, small reasoning models. For example, DeepSeek-R1-Distill-Qwen-1.5B [2] was trained on approximately 1M samples, with Nvidia’s OpenMath-Nemotron-1.5B [5] further training this model on an additional 3M samples. We found Nvidia’s OpenMathReasoning [5] and Llama Nemotron Post-Training [6] datasets particularly effective among other openly available datasets, including Hugging Face’s Open-R1 [7] and Bespoke Lab’s OpenThoughts (e.g., Open

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e billions of people online now instead of tens or hundreds of millions, so yes, it’s grown a lot bigger a lot faster: we’re standing on the shoulders of giants. (This is also why Nvidia can ramp its sales so fast - it’s riding on the contract manufacturing base built over the last few decades). That doesn’t mean this is a bad comparison: as I wrote a long time ago, unfair comparisons are often the best kind, but you do need to know it’s unfair. Stepping back, Eric Schmidt told Sheryl Sandberg t

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enMath, and MMMU) shows impressive efficiency gains. Gemma 3 27B scored 1338 on LMArena's Elo leaderboard, outperforming DeepSeek-V3 (1318) and o3-mini (1304) while using a single NVIDIA H100 GPU versus competitors requiring multiple accelerators. Processing speed proves crucial for voice applications. The 1B variant handles 2,585 tokens per second during prefill, creating sub-second response times that feel natural in conversation. This efficiency translates directly to cost savings and better

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g Post-Training Quantization (PTQ), leveraging a large and diverse calibration dataset to ensure robust 8-bit inference performance. The inference system is finely tuned to run on NVIDIA NIM with the TensorRT engine, utilising full FP8 kernels for improved throughput and efficiency. This optimised set up is available for use in our API store and can be tried in the dashboard. Known Limitations While this model supports 22 languages across a variety of tasks, performance can vary depending on the

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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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lock a workflow that’s both flexible and scalable. Whether you’re prototyping a new model, deploying a custom creation, or scaling a multimodal app, this combination accelerates your path from research to production. Related Posts Day 0 Support for Qwen3.8-2.4T-A95B on vLLM Day-0 vLLM support for Qwen3.8-2.4T-A95B: a 2.4-trillion-parameter hybrid MoE model served out of the box, with FP8/BF16 checkpoints plus NVFP4 and MXFP4 quantized weights, and co-developed kernels on NVIDIA and AMD hardware.

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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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e vLLM inference providers, contributions to many bug fixes, improvements, and key design discussions. We also want to thank the Llama Stack team from Meta and the vLLM team for their timely PR reviews and bug fixes. Related Posts Day 0 Support for Qwen3.8-2.4T-A95B on vLLM Day-0 vLLM support for Qwen3.8-2.4T-A95B: a 2.4-trillion-parameter hybrid MoE model served out of the box, with FP8/BF16 checkpoints plus NVFP4 and MXFP4 quantized weights, and co-developed kernels on NVIDIA and AMD hardware.

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lications (e.g., powering Amazon Rufus and LinkedIn AI features). Our bi-monthly meetups have become strategic gatherings for partnerships with industry leaders like IBM, AWS, and NVIDIA, marking our progress toward becoming the universal serving solution for the open-source AI ecosystem. Read on for more details about vLLM's 2024 achievements and 2025 roadmap! This blog is based on the 16th session of the bi-weekly vLLM Office Hours. Watch the recording here. Community Contributions and Growth

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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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o 8,192 tokens, 16x longer than the mainstream models. Efficiency Here’s the memory (max batch size, BS) and Inference (in thousands of tokens per second) efficiency results on an NVIDIA RTX 4090 for ModernBERT and other decoder models: The first thing you might notice is that we’re analysing the efficiency on an affordable consumer GPU, rather than the latest unobtainable hyped hardware. First and foremost, ModernBERT is focused on practicality, not hype. As part of this focus, it also means we

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mixed-precision for enhanced inference efficiency. Training Infrastructure The model was trained on Yotta's Shakti cluster, utilizing 1,024 GPUs over a 5-day period. We leveraged NVIDIA's NeMo framework for the training process, benefiting from its kernel fusion and other optimizations for large-scale language model training. Evaluation The evaluation of large language models for Indic languages presents unique challenges due to the scarcity of standardized benchmarks. To address this, we have

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e AMD MAD repository, disabling it can prevent potential GPU hangs and improve overall efficiency. This can be achieved with the following command:; Tuning NCCL Communication: The NVIDIA Collective Communications Library (NCCL) is used for inter-GPU communication. For MI300X, the AMD vLLM fork performance document suggests setting the NCCL MIN NCHANNELS environment variable to 112 to potentially enhance performance In our tests, enabling these two configurations yielded a slight performance impr

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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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initiatives on the services of US American companies is a bad idea. Instead, they opted to self-host various open-source large language models like Llama and Mistral on their own NVIDIA hardware. And on top of that, they'd use open-source software and employ in-house AI experts for integration and custom development. Not bad! However, the whole marketing surrounding the project announcement was rather cringe. A little birdie sent me a DM with a Facebook screenshot that told us who was responsib

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