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K3, which it's calling the world's largest open-weight model, and framed it as closing the gap with—and in some benchmarks rivaling—OpenAI and Anthropic. The release landed hard: Nvidia and other US Big Tech names sold off on renewed competitive fears, and the shockwaves compounded a brutal week for chip stocks. (Bloomberg/CNBC) CXMT prices Asia’s biggest listing of the year. Beyond Kimi K3 (above), the agent race kept accelerating this week. Alibaba and Honor announced a partnership to build a

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produced keeps climbing, and so does the cost of compute behind it. Together's research, including work on attention mechanisms and the Mamba architecture now used in models like NVIDIA’s Nemotron, is aimed squarely at that problem: faster inference and better unit economics for every workload on the cluster, from early-stage teams to companies already operating at scale. YC has long been one of the largest seed funders of research-driven companies, and securing compute has become essential to

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ree settings called reasoning-off, regular, and medium-effort, analogous to DeepSeek V4 in the previous section. Medium-effort is the cheaper reasoning mode compared with regular. NVIDIA introduces this mode during SFT using examples generated by GPT-OSS-120B in its medium-effort mode, and then further optimizes it during RLVR. About 2.5% of the RLVR prompts use medium-effort (this corresponds to length-based adjustments applied to their rewards). 6.2.1 Using Nemotron reasoning budgets during in

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e turned into a reward model that judges Japanese as well as one trained on native Japanese data? For training data the commercial-use constraint did most of the choosing. We used NVIDIA’s Nemotron-Cascade RLHF preference data [15], which is released under CC BY 4.0 for commercial use and bundles a curated subset of well-known English preference sets (HelpSteer2 [4], HelpSteer3 [5], WildGuard [6], and others). For evaluation we used the usual reward benchmarks (RewardBench [1], M-RewardBench [3]

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2 nanometer chips are more expensive than 3 nanometer chips, and certainly are riskier to make. But someone has to drive the ramp to better yields and AMD is doing it now just as Nvidia did it with 4 nanometer TSMC processes a few years back. Wei reminded everyone that it takes five to seven years to build these fabs, by the way. So it does not happen overnight. Which is why the annual capex for TSMC is smaller than the commitments to spend. To give you some perspective, TSMC spent $29.8 billio

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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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-to-any pipeline. You can use either the 16 bit "thinkingmachines/Inkling" on Hopper or later GPUs, or the quantized NVFP4 checkpoint "thinkingmachines/Inkling-NVFP4" on Blackwell Nvidia GPUs. Make sure to have the latest version of transformers (5.14.0 was released today) ( pip install -U transformers ). After initializing the pipeline, you can pass in the prompt as follows. Going one level lower, you can use Auto classes. For inference, you can use the AutoModelForMultimodalLM class for models

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ting its work in creating the vendor-neutral networking layer for quantum computing, from developing the architecture to its Universal Quantum Switch prototype, unveiled in April. Nvidia clearly sees a quantum future where QPUs share the stage with GPUs and CPUs, along with the quantum-focused product lineup it’s putting together. “We don’t build quantum computers, and yet we are deeply integrated into the quantum computing industry and we create libraries,” Nvidia co-founder and chief executive

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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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nowledgements We would like to express our gratitude to the following organizations and individuals, for their contribution to the support and optimization of GLM 5.2 NVFP4 model. Nvidia: Julien Lin, Zhiyu Cheng, Po-Han Huang, Ryan Stewart, Triston Cao and many others – For helping with Day-0 support of GLM5.2 NVFP4 GLM Team: Yuxuan Zhang – For implementing and verifying IndexShare in SGLang Appendix Reproduction For reproducing the performance results, please refer to custom scripts under this

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en in Figure 14). Let's decode the name of the kernel: fmha (fused multi-head attention): All the primitive ops in attention is "fused" in one op now. cutlassF : built on CUTLASS (NVIDIA's open-source templates for tensor-core GEMMs), F for forward. bf16 aligned : runs in bfloat16 (no FP32 upcast, unlike math). 64x64 : the tile size. rf (register file): the working set is kept in registers, the fastest memory on the chip. sm80 : compiled for Ampere (the A100's compute capability 8.0). This is th

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to crawl for jobs, while OpenRouter provides the API for one of several default free AI models that A-J will run through, starting with Llama and falling back to free versions of Nvidia’s Nemotron, Google’s Gemma 4, and Alibaba’s Qwen3 when all else fails, or quotas run out. Claude Code and the Anthropic API can be used in place of OpenRouter if you’ve got tokens to spare. For those concerned about A-J broadcasting personal details to the web, Gupta writes that it’s designed to be private, prov

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n the inference side are cheap by construction. Engines are self-contained and hold no state beyond the current weights, so a dead engine costs only its in-flight sessions. We use NVIDIA Dynamo to manage the engine lifecycles and route inference: each agent sandbox has its own proxy that records tokens in and out, so if a replica goes down, we don’t lose the full trajectory, and Dynamo reroutes it to a different worker. When Dynamo reschedules the replica on healthy nodes, our weight controller

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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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Inference runs on a single 24–32 GB GPU. Check out the documentation and the paper for the technical details. FastWAM VLAs: the model zoo keeps growing GR00T N1.7 We upgraded our NVIDIA GR00T integration to GR00T N1.7, the newest open generation of NVIDIA's cross-embodiment foundation model. N1.7 swaps the previous VLM for Cosmos-Reason2-2B (built on Qwen3-VL) feeding a flow-matching action head, and our integration is parity-tested against NVIDIA's original Isaac-GR00T implementation: same inp

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beer, too. It is not clear what models Meta Platforms might load on its infrastructure, but clearly it will do whatever key customers want. If it does have excess capacity on its Nvidia and AMD GPU clusters, it can pretty much run any model its customers want it to run. That will not necessarily be the case on its homegrown MTIA accelerators, which have a fairly aggressive roadmap between now and the end of next year. The Bloomberg report also says that Meta Platforms is considering offering mo

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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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hpc Yes, that simple question is, in the modern Nvidia world that has come to dominate AI training and to a certain extent HPC simulation and modeling, heretical. But given that CPUs are in many cases starting to look more like GPUs, with their hybrid vector and matrix math engines, mixed precision support, in some cases HBM stacked and high bandwidth memory as well as fatter DRAM main memory, and integrated interconnects, it is also a logical question. And so when Jack Dongarra, of the Universi

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