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rved H100 memory than the others. Thus, it has less memory for our customers to use; Cloud D A10s have frequent hardware-side clock slowdowns (HW SLOWDOWN and HW POWER BRAKE); The NVIDIA A10s in one of Cloud D’s US regions have more frequent uncorrectable ECC errors. Unfortunately, this isn’t something you find out quickly; Cloud D has the best price/performance. Its bare metal servers are beasts Typically, our provider ranking is capacity and price led, but we additionally maintain internal adj

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aluate tool-call parameters or business logic errors – cases where the text itself appears harmless, but the execution outcome could be dangerous 2. Programmable guardrails (e.g., NVIDIA NeMo Guardrails) To address these limitations, programmable frameworks, such as NVIDIA NeMo Guardrails, must serve as the core structural backbone[7]. No matter how advanced a model may be, it cannot manage the entire logic of a complex system independently. Image 3. NeMo Guardrail Architecture[7] NeMo defines d

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so you can use it directly inside your IDE. Devstral 2 is optimized for data center GPUs and requires a minimum of 4 H100-class GPUs for deployment. You can try it today on build.nvidia.com . Devstral Small 2 is built for single-GPU operation and runs across a broad range of NVIDIA systems, including DGX Spark and GeForce RTX. NVIDIA NIM support will be available soon. Devstral Small runs on consumer-grade GPUs as well as CPU-only configurations with no dedicated GPU required. For optimal perfo

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saw on various social media platforms that people were pronouncing the team “dead” (as a one-hit wonder). I am sure the DeepSeek team has also been busy navigating the switch from NVIDIA to Huawei chips. By the way, I am not affiliated with them or have spoken with them; everything here is based on public information. As far as I know, they are back to using NVIDIA chips. Finally, it’s also not that they haven’t released anything. There have been a couple of smaller releases that trickled in thi

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ins the ranks of frontier instruction-fine-tuned open-source models. Mistral Large 3 is one of the best permissive open weight models in the world, trained from scratch on 3000 of NVIDIA’s H200 GPUs. Mistral Large 3 is Mistral’s first mixture-of-experts model since the seminal Mixtral series, and represents a substantial step forward in pretraining at Mistral. After post-training, the model achieves parity with the best instruction-tuned open-weight models on the market on general prompts, while

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e utilization. Host overhead appears in the middle: progress is halted at the level of the entire GPU while it waits for the CPU to prepare work for it. This metric is reported by nvidia-smi and other tools as “GPU Utilization”. We prefer the more descriptive “GPU Kernel Utilization” — what fraction of your GPU’s capacity to run CUDA kernels are you using? It is 100% if at least one CUDA kernel was running on the GPU at every point in time during measurement. If there is always work for the syst

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transformer components, whereas I see the models discussed here (Qwen3-Next and Kimi Linear) as transformers with SSM components. However, since I have listed IBM Granite 4.0 and NVIDIA Nemotron Nano 2 in the transformer-SSM box, an argument could be made for putting them into a single category.) If you are working with or on LLMs, for example, building applications, fine-tuning models, or trying new algorithms, I would make these models my go-to. They are tested, proven, and perform well. More

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t and can return both partial and final transcription results. However, we found that in practice using Pipecat’s local VAD and turn detection to segment audio and passing that to NVIDIA’s parakeet-tdt-0.6b-v3 was faster than the open weights streaming implementations we tried or implementing our own VAD alongside the model. While this doesn’t provide the real-time feel of partial transcripts, when it comes down to total voice-to-voice latency, the only thing that matters is the final transcript

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helping me onboard the vLLM project and explain all the details of vLLM’s toolcall functionality. vLLM plays an important role in LLM serving, and diving deep into vLLM helps me understand the nuts and bolts of LLMs. 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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tes with the lightning indexer CUDA kernels in DeepGEMM, as well as the new sparse attention kernel in FlashMLA. We are also excited about Blackwell support. In collaboration with NVIDIA, you can run this model directly on B200 and GB200! Usage Guide Once installed, on 16×H100, 8×H200, or 8×B200, you can run the model with tensor parallelism (expert parallelism has a slight bug we are fixing): To deploy at scale, we look forward to sharing our one-click Kubernetes deployment using llm-d later th

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serve Claude to millions of users via our first-party API, Amazon Bedrock, and Google Cloud's Vertex AI. We deploy Claude across multiple hardware platforms, namely AWS Trainium, NVIDIA GPUs, and Google TPUs. This approach provides the capacity and geographic distribution necessary to serve users worldwide. Each hardware platform has different characteristics and requires specific optimizations. Despite these variations, we have strict equivalence standards for model implementations. Our aim is

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ready for the GPUs to be moved in to create the largest single datacenter in the world, yet again. xAI has to raise the capital for those GPUs, but they have the allocations from Nvidia to have it fully training large scale models early next year. Elon came up with a new genius trick to beat rivals at time-to-market. Colossus 2 will be an even more impressive achievement than xAI’s first cluster. Let’s dig in. The first half of this report will dig into the Colossus 2 prowess. The second half w

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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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I race. These moves have led to backlash, including with China cutting rare earth minerals and magnets off from the US. Secretary of Commerce Howard Lutnick says the resumption of Nvidia GPU sales to China were required to restart China's shipments of their linchpin supply chain materials. But constraints have also led to adaptation, and Chinese companies have adapted. High batch sizes and disaggregated serving are but two examples. Despite advances, in the case of DeepSeek, most of their tokens

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(GPU 0 for prefill and GPU 1 for decode), and then transfer the KV cache between them: Note: I've also experimented with LMCache [11], the fastest production-ready connector (uses NVIDIA's NIXL as the backend), but it's still at the bleeding edge and I ran into some bugs. Since much of its complexity lives in an external repo, SharedStorageConnector is a better choice for explanation. These are the steps in vLLM: Instantiation — During engine construction, connectors are created in two places: I

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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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its, making cost, efficiency, power, performance per TCO, and reliability central to the discussion on effective training. The Hopper vs Blackwell comparisons are not as simple as Nvidia would have you believe. In this report, we will start by present the results of benchmark runs across over 2,000 H100 GPUs, analyzing data on model flops utilization (MFU), total cost of ownership (TCO) and cost per training 1M tokens. We will also discuss energy use, examining the energy in utility Joules consu

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future of HBM. We will cover the revolutionary change coming to HBM4 with custom base dies for HBM, what various different accelerators are doing with custom HBM including OpenAI, Nvidia, and AMD, the shoreline area problem, memory controller offload, repeater PHYs, LPDDR + HBM combos, and various beachfront expansion techniques. We will also discuss SRAM tags, compute under memory, supply chain implications, and Samsung. A Brief Overview of HBM As AI models grow in complexity, AI systems requir

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system together. To share your story or usage with vLLM, please submit a PR at the blogpost repository. We would like to thank Ze Long, Vikram Sharma Mailthody, Jeremy Iverson, and Sandarbh Jain from NVIDIA for their helpful discussions. Lucas Wilkinson from Red Hat helped polishing the draft. Related Posts How vLLM developers debug hanging and complex CUDA kernels by triggering GPU core dumps, identifying stuck kernels, and mapping failures back to source code lines for faster kernel debugging.

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odel is designed for lower latency, local, or specialized use-cases. 120B parameter model gpt-oss-120b model is designed for production, general purpose, high reasoning use-cases. NVIDIA and Ollama collaborate to accelerate gpt-oss on GeForce RTX and RTX PRO GPUs NVIDIA and Ollama are advancing their partnership to boost model performance on NVIDIA GeForce RTX and RTX PRO GPUs. This collaboration enables users on RTX-powered PCs to accurately leverage the capabilities of OpenAI’s gpt-oss model.

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