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le model. Jul 29, 2026 Product Mercury 2: the first reasoning model fast enough to pick up the phone Mercury 2 brings real-time reasoning to voice agents. TTFT <300 ms on standard NVIDIA GPUs, so your agents can actually hold a conversation. Jul 14, 2026 Product Mercury 2: the first reasoning model fast enough to pick up the phone Mercury 2 brings real-time reasoning to voice agents. TTFT <300 ms on standard NVIDIA GPUs, so your agents can actually hold a conversation. Mercury 2 is now available

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ool for creating entertainment experiences, but also for creating simulated environments for robot training. The acquisition is likely to help AMD compete with long-standing rival Nvidia in creating an ecosystem for AI-specific chips. While Nvidia already has a suite of open-weight world models like Cosmos, AMD has only offered text- and video-based models to the public. World models are seen as vital in efforts to deploy generative AI models on robotic platforms, from autonomous vehicles to ind

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, memory access, and communication. It prioritizes faster token generation per user, rather than maximum batched throughput, while vLLM handle prefill. Ultra-High Interactivity on NVIDIA GPUs? - TileRT InferenceX On AgentX, FP8 TileRT MI355X achieves 2x the P90 interactivity than the best FP4 MI335X config. Comparing to GB300 NVL72, it brings a 40% uplift in interactivity. These are serving metrics that used to require specialized hardware. However, TTFT is suboptimal and there is still room for

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models are evolving,” Su says in a statement. “Fei-Fei and the World Labs team bring exceptional research leadership and model expertise. Together, we can use that insight to develop the hardware, software and systems that will power the next generation of AI and strengthen the open AI ecosystem.” The World Labs deal is just the latest multi-billion dollar AI-related acquisition as of late, with Nvidia announcing earlier this month that it would be acquiring Hugging Face for nearly $13 billion .

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ary. Holotron4 Nano Our post-training stack is designed to adapt to new foundation models and produce agents that generalize across interfaces and environments. As a member of the NVIDIA Nemotron Coalition , we applied our latest stack to the Nemotron 3 Nano Omni model as a follow-up to Holotron 3. The same recipe turns Nemotron 3 Nano Omni into Holotron4 Nano, a generalist agentic model that significantly improves over the base model on GUI workflows and in environments exposing MCP, APIs or co

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E-RL-Verified PrimeIntellect/R2E-Gym-Subset-Verified PrimeIntellect/Reverse-Text-RL PrimeIntellect/Scale-SWE-Verified harborframework/harbor-mix harborframework/terminal-bench-2.1 nvidia/Nemotron-RL-instruction following-structured outputs Spaces mentioned in this article 1 The ultimate guide to RL environments: building and scaling them in the LLM era Building and scaling RL environments for LLM training More Articles from our Blog Async GRPO with LoRA across HF Jobs: a bucket, a proxy, and no

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leaving the dialogue’s substance still to be defined even as both sides claimed the diplomatic win. (CNBC/Bloomberg/FT/SCMP) Alibaba unveiled an in-house AI chip aimed squarely at Nvidia and committed to 20 gigawatts of AI data-center capacity by 2032, and the market loved it. The announcement, made at Alibaba Cloud’s Apsara Conference alongside a wave of new Qwen agent and cloud products, sent Alibaba’s shares jumping. (Bloomberg/CNBC) DeepSeek is closing in on a $7.5 billion funding round and

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Doesn’t Need New Rules Yet Steven Sinofsky, Theo Jaffee, and Sofia Puccini; Is Software Losing Its Head? Seema Amble, Elena Burger, and Steven Sinofsky; Steven Sinofsky on AI PCs, NVIDIA, and the Future of Computing Theo Jaffee and Steven Sinofsky Erik Torenberg is a general partner at Andreessen Horowitz, where he focuses on investing and running our marketing & ecosystem orgs. The a16z Show discusses the most important ideas within technology with the people building it. Each episode aims to p

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ets are pretty efficient. We’re going to have more and more people coming into the space. The second thing that you need is chips. You need the silicon to run these things. Today, Nvidia is the best in the world at that, but you’ve got a whole bunch of others, whether that’s AMD or Qualcomm or all the hyperscalers, that are building their own chips in order to power these things. While we have a massive shortage in the availability of silicon and the ability to actually power it up and turn it o

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licy training requires tightly coupled workers that progress in lockstep, much like pre-training or SFT. Any latency spike or straggling worker can stall the entire job or trigger NVIDIA Collective Communications Library (NCCL) timeouts. RL systems must balance these two workloads because any mismatch in their rates can leave hardware idle or introduce training instability. Figure 1: The asynchronous RL loop, where rollout workers generate experience through distributed inference while policy-tr

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hese are the instance types we used. Other GPU instances and cluster sizes work as well, provided the workers have enough GPU memory for the model. Workers: 3x ml.g7e.12xlarge (2x NVIDIA RTX PRO 6000 Blackwell GPUs each, 6 GPUs total); Head: ml.r5d.16xlarge (512 GB RAM, manages Ray GCS, dashboard, and LoRA adapter consolidation); Policy model: Qwen3-VL-8B with LoRA (rank 32), sharded across the 6 GPUs using PyTorch FSDP; Rollout engines: 6 colocated vLLM instances, one per GPU; Shared storage: A

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ook instance, or a local environment with the Amazon SageMaker Python SDK installed; Sufficient service quota for a supported GPU instance. This walkthrough uses ml.g6.4xlarge (1x NVIDIA L4 GPU, 24 GB), which is sufficient for the 1.7B model; A short reference audio clip of the target speaker (a few seconds is sufficient) and a transcript of the words spoken in it Step 1: Deploy the model from JumpStart Construct a JumpStartModel with the model ID and deploy it. The GPU memory override is the mo

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so has to be tailored to the specific error correction code – like surface code or color codes – that is being used. Advancements are being made. GPU acceleration, such as through Nvidia’s CUDA-Q platform, is coming into play, as is the use of AI. Recent examples include IBM using a large language model (LLM) framework to sort through thousands of code variations to determine the right one for a particular environment. Google two years ago created AlphaQubit, which uses a transformer-based neura

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n warning about losing “control of the future to AI.” FLUX-maker Black Forest Labs released FLUX 3 Action, an open-weight model that steers robot arms from camera footage, beating Nvidia’s Cosmos 3 on a simulation leaderboard at under half its size. The U.S.’s AI build-out could attract $10.3T by 2032, creating jobs but fueling inflation and squeezing housing construction, reports the Wall Street Journal. OpenAI hired Patreon co-founder Sam Yam to lead its new Creator Product division, tapping a

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: pytorchReplicaSpecs: Worker: replicas: 2 restartPolicy: OnFailure template: spec: containers: - name: pytorch image: .dkr.ecr.us-east-2.amazonaws.com/ :latest resources: limits: nvidia.com/gpu: 8 vpc.amazonaws.com/efa: 32 command: - torchrun - --nproc per node=8 - --nnodes=2 - /workspace/train fsdp.py - --dataset-path=/csi/data/c4 - --checkpoint-path=/csi/checkpoints/llama-fsdp - --batch-size=24 - --seq-length=4096 - --num-workers=64 - --prefetch-factor=4 volumeMounts: - mountPath: /csi name:

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asis [00:03:27]: One of the first projects that, we built with, one of my two co-founders, Chris. This was taking, pix2pixHD, which was one of the early image-to-image models that NVIDIA released back in 2016 or 2017. and it was a model that would take a semantic map of a scene and then generate a photorealistic, let’s call it, output. very early days, so it was not very high-fidelity outputs, but it w I think was the first image-generation model that could generate at 1K resolution. And it was

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n the prefill-only roadmap. Acknowledgements This post was developed in collaboration with the SGLang and LinkedIn teams. Additional contributors to the performance optimizations: NVIDIA: Po-Han Huang, for the Qwen3.5 MIS contribution; TikTok: Hongyu Lu The benchmark clients are available in chuanrui/sglang-benchmark, under lmsys blog/jev bench. The examples below use Qwen3-0.6B on one H200 and cover the pointwise experiments: a closed-loop concurrency sweep with candidate-count breakdowns and a

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As Jensen Huang puts it, AI can help fight climate change — but only if it inflicts “an enormous amount of pain and suffering” first. The Nvidia CEO discussed the future of energy and AI’s impact on our planet in the latest episode of The Ezra Klein Show . But his comments boil down to the same accelerationist spin we’ve seen from a variety of tech leaders as well as President Donald Trump. They promise that AI will be such a gift to humanity that it’s worth the damage caused by data centers cur

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posable emails and trade crypto. UNSC session : @ClementDelangue described Hugging Face’s own agent cyberattack. He said closed APIs blocked his defenders, so the team switched to NVIDIA’s build of GLM 5.2 . He called for mandatory sharing of agent traces. Altman and Amodei warned about loss of control and misuse. Bengio urged immediate action. Kratsios rejected a global regulator. Related safety research : Redwood argues latent “neuralese” reasoning would erode chain-of-thought oversight. Separ

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be done in parallel, on a SonK cluster, or sequentially, if the provider wanted to take the nodes back and re-provision them); Blackwell, not Hopper (B200, B300, GB200, GB300) for NVIDIA, or MI355X for AMD are considered acceptable. H100 is now over 4 years old! We tested the cluster through 3 phases, described below. Of course, we always complement this testing by contacting customers of all these providers and taking their feedback into account. Phase 1: Audit First, the Audit covers yes/no qu

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