搜索“DeepSeek”

minute task timeout. Tasks took 6m 53s on average. That makes Fable slower per task than GPT 5.5, Opus 4.7, and BU 2.0, but faster than Gemini 3 Pro, Qwen 3.6 Plus, Kimi K2.6, and DeepSeek V4 Pro. The cost comparison makes the tradeoff clear. Fable completed many of the long research and browsing tasks that usually separate stronger web agents from weaker ones. These are tasks that require following many steps of reasoning, checking constraints, and returning a structured answer rather than extr

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came from treating inference as a full-stack problem: profiling end-to-end, identifying the most expensive operations, and eliminating them one by one. ThunderMLA. Kimi K2.5 uses DeepSeek's Multi-head Latent Attention (MLA) architecture. Standard implementations run two separate kernel launches per decode step. Our ThunderMLA — part of our ThunderKittens kernel library — fuses these into a single megakernel, eliminating launch overhead and the tail effects between them. On representative decode

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point that provides high quality multi-lingual transcription at an optimized speed. Then a large language model translates that transcript. Here we leverage the latest advances of Deepseek V4 Pro as default translator. We also enable the user's input of a predefined list of translation rules to maintain the faithfulness and accuracy. Finally, the TTS model generates translated speech, allowing users to specify their desired voice characteristics in plain text. Together-hosted Cartesia’s Sonic 3

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B (Feb 13, 2026); Qwen 3.5 (Feb 15, 2026); Ant Group’s Ling 2.5 1T & Ring 2.5 1T (Feb 16, 2026); Cohere’s Tiny Aya (Feb 17, 2026); Update 1: Sarvam 30B and 105B (Mar 6, 2026) (PS: DeepSeek V4 will be added once released.) Since there’s a lot of ground to cover, I will be referencing my previous The Big LLM Architecture Comparison article for certain technical topics (like Mixture-of-Experts, QK-Norm, Multi-head Latent Attention, etc.) throughout this article for background information to avoid r

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wise training-inference consistency a scalable and reusable feature. If you're interested or would like to contribute, please join these Slack channels: sig-post-training; sig-batch-invariant 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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From DeepSeek R1 to MiniMax-M2, the largest and most capable open-weight LLMs today remain autoregressive decoder-style transformers, which are built on flavors of the original multi-head attention mechanism. However, we have also seen alternatives to standard LLMs popping up in recent years, from text diffusion models to the most recent linear attention hybrid architectures. Some of them are geared towards better efficiency, and others, like code world models, aim to improve modeling performanc

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cial thanks to Vensen Mu, Jeff Aw, Jun Kang Chow, Tun Jian Tan, Pin Siang Tan, Amir Balwel, Ye Hur Cheong, Zhiyao Cen and Kaichao You for developing the Sleep Mode feature and this blog post. 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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is ongoing and there are an overwhelming number of improvements on the horizon. Looking ahead, we’re working to unlock significant throughput gains on cluster-scale inference for DeepSeek, Qwen, gpt-oss, and many more through speculative decoding and Data+Expert Parallel (DEP) configurations. With NVIDIA’s gpt-oss-120b-Eagle3-v2, which incorporates Eagle speculative decoding, we anticipate up to 2-3x improvement in throughput. Leveraging DEP, which takes advantage of the 1,800 GB/s low latency

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egorize PRMs as “step-level judges,” which are predominantly developed for training, not pure evaluation. (In practice, PRMs are difficult to train reliably at scale. For example, DeepSeek R1 did not adopt PRMs and instead combined verifiers for the reasoning training.) Judge-based evaluations offer advantages over preference-based leaderboards, including scalability and consistency, as they do not rely on large pools of human voters. (Technically, it is possible to outsource the preference-base

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ting, the open-weight 235B-Instruct variant is ranked 8 on the LMArena leaderboard, tied with the proprietary Claude Opus 4. The only 2 other open-weight LLMs that rank higher are DeepSeek 3.1 (3x larger) and Kimi K2 (4x larger). On September 5th, Qwen3 released a 1T parameter “max” variant on their platform that beats Kimi K2, DeepSeek 3.1, and Claude Opus 4 on all major benchmarks; however, this model is closed-source for now; There are many different model sizes available for different comput

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"Attention Is All You Need" https://arxiv.org/abs/1706.03762; "Efficient Memory Management for Large Language Model Serving with PagedAttention" https://arxiv.org/abs/2309.06180; "DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model" https://arxiv.org/abs/2405.04434; "Jenga: Effective Memory Management for Serving LLM with Heterogeneity" https://arxiv.org/abs/2503.18292; "Orca: A Distributed Serving System for Transformer-Based Generative Models" https://www.usenix.

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M, it’s a core part of the inference pipeline. Combined with caching, dynamic shape support, CUDA Graphs, and custom passes, it enables efficient, scalable LLM serving across any environment. 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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trends and developments point towards more, smaller models as being beneficial. This change, at a constant total parameter size, is nicely illustrated in Figure 15 below from the DeepSeekMoE paper. Notably, unlike DeepSeek's models, neither gpt-oss nor Qwen3 uses shared experts, though. To be fair, the small number of experts in gpt-oss could be a side effect of the 20B size. Looking at the 120B mode below, they indeed increased the number of experts (and transformer blocks) while keeping every

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oftware engineering, tool use, and long-context understanding. MiniMax-M1 leads among open-source models; Right: Theoretical inference FLOPs scaling with token length. Compared to DeepSeek R1, MiniMax-M1 uses only 25% of the FLOPs when generating sequences of 100k tokens Deploying MiniMax-M1 with vLLM We recommend deploying MiniMax-M1 using vLLM for optimal performance. Our tests demonstrate the following key benefits: Outstanding throughput; Efficient and intelligent memory management; Robust s

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idth interconnects between each GPU, like NVLink or InfiniBand, to minimize overhead from the increased communication costs. Pipeline Parallelism For extremely large models (e.g., DeepSeek R1, Llama 3.1 405B), a single node may not suffice. Pipeline parallelism shards models across nodes, each handling specific contiguous model layers. How It Works Each GPU loads and processes a distinct set of layers; Send/Receive Operations: Intermediate activations are transmitted between GPUs as computation

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o the same mechanism of "using a format for the model to structurally sampling outputs.” ↩; See this blog post from HuggingFace for using logit processors to control the generation process. ↩ 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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