搜索“DeepSeek”

a good idea. If the Tensordyne architecture pans out and can support all of the big inference engines, and Tensordyne can get enough HBM to make them in high volume, this may be a DeepSeek moment for AI hardware. RK Anand, one of the co-founders of Tensordyne, tells The Next Platform that Broadcom is the chip shepherd for the Napier compute engine and that with Broadcom being the third largest buyer of HBM memory on the planet and the third largest buyer of chip wafers from Taiwan Semiconductor

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no impact on model quality. However, many speculative decoding methods, like the EAGLE series and the native multi-token prediction (MTP) modules in recent models like Gemma 4 and DeepSeek-V4, still rely on sequential autoregression – but in the draft model instead of the target. The draft model generates draft tokens one-by-one,a poor fit for modern hardware and a limit on achievable speedup. That’s why Z Lab developed DFlash, which uses a lightweight block diffusion draft model to generate an

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e task is fundamentally broken. It serves as a first sanity check for task solvability when the synthesizer creates a new task instance. Task Evolution: Difficulty Tiers Following DeepSeek-V3.2's methodology (arxiv:2512.02556), we find that iteratively evolving a task from trivial to challenging is substantially easier than one-shotting a challenging task. Hence, the synthesizer first constructs a simple seed task following the spec outlined above and then iteratively increases its difficulty by

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new application domain was pioneered by proprietary models — models whose weights are not available under an Open Source Initiative-approved license, like MIT or Apache. But since DeepSeek’s epoch-making launch of DeepSeek-R1, open models have not been far behind. Now, GLM-5 matches the performance of proprietary models released in the last month and is available under the MIT license. For details on the above benchmarks and more, see Z.ai’s blog post. How do I run GLM-5? We’ve written in depth

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伯克利函数调用排行榜（该榜单按模型将自然语言请求转化为可执行代码和系统命令的能力进行排名），Anthropic 的 Claude 4 模型目前在工具调用方面表现最佳。 但较新的开源模型正在快速追赶。Goose 的文档重点介绍了几个具有强大工具调用支持的选项：Meta 的 Llama 系列、阿里巴巴的 Qwen 模型、Google 的 Gemma 变体，以及 DeepSeek 以推理为核心的架构。 该工具还与模型上下文协议（Model Context Protocol，简称 MCP）集成，这是一种将 AI 智能体连接到外部服务的新兴标准。通过 MCP，Goose 可以访问数据库、搜索引擎、文件系统和第三方 API——将其能力远远扩展到基础语言模型所能提供的范围之外。 对于有兴趣搭建完全免费、保护隐私的配置的开发者来说，这个过程涉及三个主要组件：Goose 本身、Ollama（一款用于在本地运行开源模型的工具）以及一个兼容的语言模型。 第 1 步：安装 Ollama Ollama 是一个开源项目，它极大地简化了在个人硬件上运行大型语言模型的过程。它通过简单的界面处理下载、优化和提供模型服务

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Lightning is an open-source project from Microsoft Research. We sincerely appreciate the support from MSR for this open-source exploration. Yuge Zhang is the primary contributor to this work. Related Posts DeepSeek-V4.1-Flash on vLLM: 5x Agentic Throughput Since Day 0 Within three weeks of release, vLLM made DeepSeek-V4.1-Flash 1.9x faster at low concurrency and lifted its throughput 5x on SemiAnalysis AgentX, with SWA bounded replay, CUDA graphs, DeepSeek's new kernels, and vLLM kernel fusions.

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ities and are able to have multi-turn interactions with the user and tools to get to a final result. qwen3; gpt-oss Recommended cloud models: qwen3:480b-cloud; gpt-oss:120b-cloud; deepseek-v3.1-cloud The web search and web fetch tools can return thousands of tokens. It is recommended to increase the context length of the model to 32000 tokens for reasonable performance. Search agents work best with full context length. Fetching page results To fetch individual pages (e.g. when a user provides a

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2.5: offers explicit thinking budgets, allowing enterprises to cap reasoning depth; Alibaba Qwen3: supports instruction-driven switching between reasoning and non-reasoning modes; DeepSeek v3.1: merges conversational and reasoning flows within a dual-mode single model The trend is clear: future inference systems will be defined by selectivity and intelligence, not just model size. Recent Research: vLLM Semantic Router Responding to this shift, the vLLM Semantic Router offers an open-source, inte

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ans designing for the real world: heterogeneous GPUs, public internet latency, and unreliable but abundant FLOPs. With the rise of reinforcement learning for reasoning models like DeepSeek R1, inference has moved to center stage, and is now a core component of the entire AI stack: Training: Generate rollouts during reinforcement learning (e.g. INTELLECT-2); Distillation: Creating synthetic data at scale (e.g. SYNTHETIC-1); Evaluation: Benchmarking model performance and safety That's why our next

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implementing the speculative decoding support; Kuntai Du is implementing the prefill disaggregation and KV Cache transfer support; Simon Mo and Zhuohan Li contributed to the V1 system design Related Posts DeepSeek-V4.1-Flash on vLLM: 5x Agentic Throughput Since Day 0 Within three weeks of release, vLLM made DeepSeek-V4.1-Flash 1.9x faster at low concurrency and lifted its throughput 5x on SemiAnalysis AgentX, with SWA bounded replay, CUDA graphs, DeepSeek's new kernels, and vLLM kernel fusions.

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has highest throughput on ShareGPT and Decode-heavy dataset (though the throughput is only marginally higher than TensorRT-LLM), but the throughput of vLLM is lower on Prefill-heavy dataset. Related Posts DeepSeek-V4.1-Flash on vLLM: 5x Agentic Throughput Since Day 0 Within three weeks of release, vLLM made DeepSeek-V4.1-Flash 1.9x faster at low concurrency and lifted its throughput 5x on SemiAnalysis AgentX, with SWA bounded replay, CUDA graphs, DeepSeek's new kernels, and vLLM kernel fusions.

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ire team — Siyuan Zhuang, Ying Sheng, Lianmin Zheng (UC Berkeley), Cody Yu (Independent Researcher), Joey Gonzalez (UC Berkeley), Hao Zhang (UC Berkeley & UCSD), and Ion Stoica (UC Berkeley). Related Posts DeepSeek-V4.1-Flash on vLLM: 5x Agentic Throughput Since Day 0 Within three weeks of release, vLLM made DeepSeek-V4.1-Flash 1.9x faster at low concurrency and lifted its throughput 5x on SemiAnalysis AgentX, with SWA bounded replay, CUDA graphs, DeepSeek's new kernels, and vLLM kernel fusions.

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