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controllable agents better suited for real AI workflows. Consider a system of models performing a computer-use task measured by the Terminal-Bench Hard benchmark (Figure 1). While DeepSeek V4 has the highest overall accuracy in this example, it isn’t the best model for every task group. For instance, Kimi K2.6 is better suited to the ML and RL task groups, while Qwen3.5 397B A17B is preferable for math and science. The remaining six task groups should use DeepSeek V4. This approach can also be a

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tion the early knowledge distillation literature of 2015 to 2-3 key insights that got us to the multi-teacher on-policy distillation (MOPD) of models like Xiaomi MiMo-V2-Flash and DeepSeek V4. The second half of this book goes into a tour of over-optimization, regularization, evaluation, and character training, which is all about the many ways post-training can go wrong and what you need to stay on top of. This is what differentiates the book most from those that are just a list of code exercise

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en is available through the Hugging Face SDKs - huggingface hub ( = 1.26.1) for Python and @huggingface/inference for JavaScript. The following examples show how to use the latest DeepSeek V4 Flash through Baseten. Use a Hugging Face token to authenticate - the request will be routed to Baseten automatically. From your favorite Agent Harness Hugging Face Inference Providers are integrated in most Agent Harnesses - including Pi, OpenCode, Hermes Agents, OpenClaw, and more. This means you can plug

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cutives cited model cost as the primary obstacle to generative AI adoption. Chinese labs have shipped some of the most aggressively priced, yet competitive open models. The latest DeepSeek-V4-Flash costs just 1/105 of Anthropic’s Fable 5 on one task, according to Artificial Analysis. Even at that ratio, DeepSeek’s CEO said in a investor meeting that the company still runs a 60% profit margin and can recoup a model’s cost within 10 months. A growing trend today is developers now route models by c

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ghts the US-China gap and growing players in the open ecosystem. To date, our primary efforts on Interconnects have been release recaps for popular models like Kimi K3 , GLM 5.2 , DeepSeek R1 , etc. and monthly round-ups of the open models that matter, Artifacts Log . We’re expanding on these, building on the tools and internal data we’ve collected for other projects like The ATOM Project (and report ). This allows us to capture our ecosystem view of open models, develop methods for understandin

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runs as a minimal CLI with tree-structured sessions that allow branching and navigation without losing context; Broad provider support via BYOK: Pi connects to Anthropic, OpenAI, DeepSeek, Gemini, Groq, xAI, OpenRouter, and Kimi through your own API key or existing subscription; Small core: Pi keeps its built-in orchestration and tool surface minimal, which can make it more straightforward to inspect and modify than larger agent frameworks; No governance layer: Pi has no plan mode, sub-agents,

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idelines: a co-design checklist that helps model developers raise inference throughput and interactivity on NVIDIA GPUs. Stay tuned for a post covering sparse attention. Figure 1. DeepSeek-R1 prefill time breakdown at 4K, 32K, and 128K context lengths, where the attention share rises from 18% to 85% Every analysis is grounded in two sources: analytical formulas from GEMM-shape arithmetic, and measured data from prefill and decode kernels with FP8 for both attention compute and the KV cache. Pref

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55X GPU accelerator and made some vaguer claims about performance at the rack level compared to Vera-Rubin racks coming later this year. Here is the inference throughput using the DeepSeek V4 Flash model: As with other GPUs and XPUs, the increase in capacity across all fronts in the MI455X device yields higher token throughput, and particularly at the higher interactivity levels where users need more tokens processed. Here is how AMD reckons the cost per token on DeepSeek V4 Flash on the two dif

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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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s the number of tokens till 50% compute spend is on attention gets much larger than 25K. Interleaving linear attention alternatives with full attention as is done with gpt-oss and DeepSeekv4, results in massive flops reductions for the attention computation. For example with DeepSeekv4-Pro (1.6T A49B) it requires nearly 450 thousand tokens to reach the 50% tradeoff point. Grandcode uses Qwen-3.5-397B-A17B a model which hits 50% FLOPs for attention at 150 thousand tokens. ↩ This setting is techni

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mbership, an enterprise tier bundling its Allegretto plan benefits with corporate billing and support. (@Kimi Moonshot) A UK/US study found Kimi K3 significantly lags American models in offensive cyber “hacking power”—a data point that cuts against the panic narrative above (SCMP); Recode China AI published a full translation of DeepSeek founder Liang Wenfeng’s four-hour investor meeting—his AGI roadmap, the US-China compute gap, Huawei chips, and why DeepSeek stays open source (Recode China AI)

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models and is okay for working with office documents but lags badly in terms of its agentic abilities. And for the technically inclined, Chinese open weights models like Kimi K3, DeepSeek, and Qwen are surprisingly capable, but do require expertise to use as agents. And then there is Google. Google, which led on benchmarks not that long ago, has fallen behind where it now counts: it has no leading frontier model and it has nothing close to Codex and Code. That is why I don’t suggest Gemini as y

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be an integral part of the project. For instance, yesterday the Laguna S.1 model was released. It looks interesting on paper, however: will it really be good enough? Will the new DeepSeek v4 Flash checkpoints make it not really relevant for DwarfStar? It is too early to say. However, to collectively form an idea, publishing a branch with this model implementation is a good middle ground: people will try it, will refine it with their coding agents, and the community can collectively form an idea

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Timothy Prickett Morgan 发布于 2026年7月21日 星期二 // 21:32 UTC 最近几周，关于来自中国的开放权重 AI 模型——例如阿里巴巴 Qwen、DeepSeek R1、V3 和 V4，以及 Moonshot Kimi K3——在能力上正逐步逼近来自美国的闭源模型（如 OpenAI GPT、Anthropic Claude 和 Google Gemini）的讨论甚嚣尘上。 这场始于去年 1 月 DeepSeek R1 模型的讨论是有道理的，因为如果美洲和欧洲大陆的公司决定他们想要开放权重——在现阶段他们通常无法获得开源模型，因为模型制造成本太高——那么他们就会考虑中国模型，而不是成本高得多的闭源权重模型。这反过来会压低美国 AI 模型制造商所能产生的收入，而这或许不足以覆盖 OpenAI、Anthropic、Google 等公司正在承担的巨额 AI 硬件租赁成本以及如今的购置成本——他们正按照自己的愿景创造生成式 AI 的未来，而我们其他人将不得不应对这一未来。NEXTPLATFORM 广告 未来两年，账面上有数万亿美元的数据中心和硬件投资，以支撑

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to something shorter, say 3-5 months. From the release materials, it is clear that K3 is a true frontier model. It will be the closest open models have been to the frontier since DeepSeek R1 . DeepSeek R1 was a different story. This was a Chinese lab being extremely quick to pivot to reasoning models and release one faster than many American companies. Kimi K3 an example of a Chinese lab executing on scaling the known areas: data, algorithms, architecture, tools, environments, etc. Kimi K3 come

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t, Ryzen and data center GPUs with up to 2× faster with 70% less VRAM and no accuracy loss via our custom Triton kernels and math algorithms. We also support optimized ROCm builds for GGUF & Safetensors inference. Unsloth is an open-source local UI for faster LLM training and inference, with tool-call healing, code execution, secure web search, remote APIs, and HTTPS deployment. Connect local models to Claude Code, Codex agents and run the latest Kimi, GLM, DeepSeek, Qwen3.6, and Gemma 4 models.

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3 is the largest open LLM any Chinese company has ever released, assuming the open weights arrive on July 27 as promised. The AI community and much of X are calling it Moonshot’s “DeepSeek R1” moment, three weeks after Z.ai (Zhipu AI) sparked its own viral storm with GLM 5.2. The timing was deliberate: K3 landed just ahead of the World Artificial Intelligence Conference in Shanghai where Chinese President Xi Jinping called for greater open-source collaboration. What makes K3 an unusual release t

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ormance comparison between Day-0 and v0.5.15.post1 on 8 B300 Background GLM-5.2 keeps the same backbone as earlier GLM checkpoints: DSA with a sparse-attention indexer on top of a DeepSeek-V3-style MoE. It adds two major architectural changes: IndexShare for DSA, and MTP with IndexShare and KVShare. SGLang has supported the GLM-5.2-NVFP4 checkpoint since day 0 on (Grace) Blackwell hardware, using trtllm-gen kernels for both sparse attention and MoE. To turn that day-0 stack into a faster, more s

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核心的经济代言人来代表针对它们的行动可能带来的负面影响。从近期围绕Fable（以及随后的GPT-5.6）模型许可协议相关事件的报道来看，关于6月9日发生了什么，有更多说法： 据一位了解该会议的人士称，会上提到了该项目将如何处理开源AI模型的话题。该人士说，美国开源模型提供商Reflection AI的一位代表主张，开源模型应基于其能力获得该框架的豁免。目前，DeepSeek等中国开源模型相比其他可用的开放模型拥有相当大的领先优势，而Reflection尚未推出公开模型。 在这里实施任何形式的禁令，对AI的长期发展轨迹都将是一个重大错误。 最可能即将采取的行动是，禁止或无限期推迟任何能力明显高于GPT 5.5、Claude Opus 4.8或GLM-5.2区间的开放权重模型。鉴于持续存在的能力差距，这应该会在未来6个月内发生。 就目前情况而言，这些模型最有可能来自一家中国公司，正因如此，这场关于前沿开放模型能力的讨论不可避免地与其他问题（如蒸馏）联系在一起。政府“审查权”的能力门槛会随时间推移而变化，但一旦确立，对开放模型的推进很可能远慢于对闭源模型。部分原因是闭源模型更容易保障安全，但

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the drive to solve very difficult problems once reserved for proprietary models. Ollama’s cloud is the most delightful way to access the most powerful open models: GLM, Nemotron, DeepSeek, Kimi, MiniMax and more. On average, Ollama’s cloud has more than doubled in token volume every month, and we can’t wait to make it available to every team. Today, we’re announcing that Ollama has raised $88M from Peter Fenton at Benchmark, Tomasz Tunguz at Theory Ventures, Alex Kolicich at 8VC, along with Sol

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