搜索“NVIDIA”

AI chip. We’re going to build up from here to how you would use it to run all the other operations you might want. Dwarkesh Patel This might be the wrong time to ask, but whenever Nvidia reports that this chip can do X many FP4 or half as many FP8, it seems to imply those circuits are fungible, that there’s not a dedicated FP4 versus FP8. But the way you’re mapping it out here, it seems like if it has to be mapped out in the logic, you would need a dedicated FP4 multiply-accumulate and then a de

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at $1.5 per million input tokens and $7.5 per million output tokens. Open weights are on Hugging Face under a modified MIT license. It is also available for prototyping, hosted on NVIDIA GPU-accelerated endpoints on build.nvidia.com and as a scalable containerized inference microservice, NVIDIA NIM. Build the future of agentic systems with us. We're hiring across research, engineering, and product to push agentic systems further. See our open roles. Mistral Medium 3.5 Modified MIT Merged dense m

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ntarily ended the session. In addition to achieving strong kernel generation results on PPUs, Qwen3.7-Max also generates high-quality, production-grade kernels across a variety of NVIDIA GPUs. For example, on KernelBench L3, Qwen3.7-Max is able to produce accelerated kernels for 96% of the scenarios, compared to 98% for Opus-4.6, 78% for GLM 5.1, 80% for Kimi K2.6, 54% for DeepSeek V4 Pro, and 48% for Qwen3.6-Plus. This result highlights two properties of Qwen3.7-Max as a foundation model poweri

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ore of their AI-generated content. This builds on our ongoing work to make content origins clearer, like open-sourcing our SynthID text watermarking technology and partnering with NVIDIA to watermark AI-generated video from their Cosmos world foundation models . To help more organizations identify AI-generated media, we’re launching a new AI Content Detection API on Google Cloud’s Gemini Enterprise Agent Platform. This gives businesses a powerful way to spot AI content made by both Google and ot

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r on the open-weight market. It is developed by Zyphra, and one of the interesting details around the release is that the model was trained on AMD GPUs rather than the more common NVIDIA GPU (or Google TPU) setup. The main architecture detail, though, is Compressed Convolutional Attention (CCA), used together with grouped-query attention. Unlike MLA-style designs that mainly use a latent representation as a compact KV cache format, CCA performs the attention operation directly in the compressed

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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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ost memory, not device memory. Even though most of the inference engine artifacts are host-side, the setup process creates device resources that must be checkpointed and restored. Nvidia’s recent driver versions include an elegant solution to this problem. In short, the driver checkpoints device memory in host memory so that it can be checkpointed to disk by a host-side checkpointing system. Then, once the host-side system has restored the host memory, including the device checkpoint, the driver

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ort for most popular open-weights models today, and is designed for drop-in portability across inference engines. We are collaborating with leading open-source partners, including NVIDIA, vLLM, and SGLang, to ensure renderers can become a useful reference standard across inference and RL infrastructure for the broader ecosystem. How renderers works A renderer is a structured translation layer between human-facing conversation objects and the token sequences a model actually consumes and produces

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AI + ML High-speed connectivity without NVLink baggage Astera Labs unveiled an alternative to Nvidia's NVSwitch for building rack-scale AI systems on Tuesday, claiming it will work with nearly any accelerator. The AI fabric switch, codenamed Scorpio X, crams 320 lanes of PCIe 6.0 connectivity into a single ASIC with 5.12 TB/s of bidirectional bandwidth. Historically, PCIe switches have been used in a variety of applications including scale-out compute fabrics. CPUs alone either didn't offer enou

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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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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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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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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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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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it runs continuously across every user query, agent step, and API call. And while training is a bounded investment, inference scales with every new user and use case you ship. At NVIDIA GTC 2026, NVIDIA CEO Jensen Huang framed this shift plainly: “People pay for information, but people mostly pay for work. Agentic systems get work done.” That shift from AI as a novelty to AI as a workhorse is exactly what’s reshaping infrastructure priorities. For Together AI, none of this is new. The inference

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ngine replica (32G) per training rank on CPU memory. These optimizations minimize network redundancy and allow inference servers to resume rollout significantly faster. Background NVIDIA's NCCL optimizes primitives like all-gather and broadcast by auto-detecting hardware topology and coordinating data flow via ring or tree algorithms. As the default communication backend for PyTorch FSDP, DeepSpeed, and Megatron-LM, it is the industry standard for symmetric training. However, it relies on collec

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models ahead — stay tuned. Download Model Card As tested on April 13, 2026. Compared to MAI-Image-2 when normalized by latency and GPU usage. Throughput per GPU vs MAI-Image-2 on NVIDIA H100 at 1024×1024; measured with optimized batch sizes and matched latency targets. Results vary with batch size, concurrency, and latency constraints; As tested on April 13, 2026. Compared to Gemini 3.1 Flash (high reasoning), Gemini 3.1 Flash Image and Gemini 3 Pro Image: Measured at p50 latency via AI Studio

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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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on up. In March 2025, our kernels team had grown to about 15 people: a mix of ML researchers seeking systems challenges and GPU veterans moving into AI. We'd just gotten access to NVIDIA's new Blackwell GPUs, the latest generation of hardware with fundamentally different capabilities from their predecessors. The challenge was specific: NVIDIA's teams had spent a year developing optimized kernels for Blackwell, with dozens of engineers and intimate knowledge of the hardware. We had a week. We nee

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ce. To enable big data processing, we support high-throughput inference engines such as vLLM and SGLang. We measure output throughput (total output tokens / wall time) on a single NVIDIA H100 SXM5 GPU using a sustained load setting: at each concurrency level, we continuously maintain the target number of in-flight requests (as soon as one request completes, another is sent to replace it). Each model is benchmarked with SGLang 0.5.9 across concurrency levels from 1 to 4,096, using 1,024 input tok

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