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e performance much quicker than if you just train from scratch. Anastasis [01:32:36]: Yeah. Vibhu [01:32:36]: I think we’ve seen this across languages where Swyx [01:32:38]: Yeah, DeepSeek-OCR as well. Vibhu [01:32:40]: Yeah, DeepSeek-OCR. Swyx [01:32:41]: Like, you don’t have to tokenize text. Like, you can just throw them in as images. Vibhu [01:32:44]: There’s a lot that happens in that base pre-training. Like, there was an argument a long time ago of people saying, “Oh, humans have so many,

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nguages. Anthropic is in talks to lease up to 1GW of data center capacity from Stream Data Centers, potentially using AI chips from Google and Broadcom, The Information reported. YouTube added Gemini-powered editing in Shorts and YouTube Create, along with AI comment moderation and voice-and-face likeness detection for creators. China is reportedly probing DeepSeek and Moonshot over routing user data to Claude without users’ knowledge, following Anthropic’s Sept. 10 report on model distillation.

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otal traffic within the same power budget. The initial wait for the first token and overall response time remain separate considerations in the preceding latency analysis. For the DeepSeek V4 Pro agentic workload, Vera Rubin delivers substantially more total token throughput per megawatt than GB300 and MI355X at the matched targets compared below. The size of that advantage depends on the interactivity target and the serving engine used for comparison. At 100 TPS, Rubin delivers approximately 59

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into one all-gather, removing an extra collective whose latency can dominate the transfer time for such small arrays. The PR reports roughly 80 microseconds per layer saved in its DeepSeek-V3 measurements. While 80 µs may sound small, DeepSeek-V3/R1 has 58 layers that require this AllGather. Across those layers, the savings add up to around 4.64 ms per forward pass in isolation (80 µs × 58), reducing TPOT and increasing interactivity. Google also implemented the ReduceScatter collective on Spars

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nAI are discovering new vulnerabilities in industry-standard software, building POC exploits, and publishing the details in CVE descriptions; Open models such as Kimi K3, GLM-5.2, DeepSeek V4, Qwen 3.8, MiMo V2.5, MiniMax M3, Nemotron, Gemma, and Inkling are all rising in security benchmarks such as Cybench, NYU CTF Bench, AutoAdvExBench, and Cyberseceval 3. This makes it trivial for black hats to develop exploits from a CVE description as they can circumvent model guardrails Thus, when we began

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esults for themselves. All of the data is open source and the community has the opportunity to draw their own conclusions on the current state of real world inference performance. DeepSeek V4 Pro 0813 DeepSeek V4 Pro 0813 is an ultra popular frontier open weight model from China. It has 1.6 Trillion parameters with 49 Billion active parameters. As of August 21, the following graph shows the best performance per SKU for all submissions, normalized by total cost of ownership (TCO). The ISL/OSL dis

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rence above. Where does the accuracy come from? The by-difficulty breakdown tells two different stories: Figure 1. Post-memory Task Goal Completion by difficulty, ours vs. ACE. On DeepSeek-V3.2 (right) we win Easy, Hard, and Overall; ACE only edges Medium. On gpt-oss-120b (left) ACE leads easy and medium, but per-task selection wins the hard tasks — and the aggregate. Each system improves from its own no-memory baseline (see the by-difficulty reference tables under Method notes below). The two m

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oduce avoidable train-inference mismatch Why Blackwell-Native Recipes? Previous low-precision approaches were not designed around MXFP8 or NVFP4. The existing Miles path follows a DeepSeek-V3-style block-scaled FP8 recipe: weights use 128x128 block scaling, activations use 1x128 tile scaling, and scales are computed online for each tile or block. This is a strong Hopper-era recipe, but on Blackwell its FP32 scales are still applied in software around the Tensor Core path rather than through nati

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pool and we continue to the next all gather immediately. Other Quantization and Post Processes Not all necessary tensors are registered in SGLang's model.named parameters(). For example, with the DeepSeekV3 model lineage, the MLA contains local tensors w kc and w vc, which are generated after all weights are fully loaded in a post weight load() call. SGLang contains a huge number of custom quantization and hardware-specific logic that depends on device and can not be executed on our CPU replica.

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eneck in RL post-training — delivering up to 50% speedup without touching model outputs. Reinforcement learning has become the cornerstone of modern LLM post-training. Models like DeepSeek-R1 owe their reasoning capabilities to RL fine-tuning. But as models grow larger, a critical bottleneck has emerged: the rollout phase. In RL training, the model must generate complete responses to every prompt in a batch before the next training step can begin. The slowest generation determines total step tim

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er efficiency, scalability, and reasoning performance than traditional dense architectures. This paradigm shift defines today’s leading frontier models including GPT-5, Claude-4.5 DeepSeek-V3 and Kimi2 all of which leverage MoE designs to expand capability while optimizing compute utilization. ZAYA1 represents the first large-scale pretraining of an MoE model on an AMD platform, demonstrating that the AMD AI ecosystem is ready to power frontier-class AI development end-to-end. Zyphra co-designed

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agnosis We evaluated a comprehensive suite of frontier generative AI models against the 304 NEJM cases. The foundation models tested included GPT, Llama, Claude, Gemini, Grok, and DeepSeek. Beyond baseline benchmarking, we also developed the Microsoft AI Diagnostic Orchestrator (MAI-DxO), a system designed to emulate a virtual panel of physicians with diverse diagnostic approaches collaborating to solve diagnostic cases. We believe that orchestrating multiple language models will be critical to

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policy initializations for online reinforcement learning. We use this insight to train INTELLECT-MATH, a state-of-the-art model for mathematical reasoning (prior to the release of Deepseek-R1) in the 7B parameter regime that reduces the training time required to match the existing state-of-the-art by 10x. Additionally, we release NuminaMath-QwQ-CoT-5M, a dataset containing five million reasoning traces across 860k mathematics questions verified for correctness. All of our code as well as weights

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