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分类：DeepSeek

DeepSeek是领先的开源AI模型系列。本专栏涵盖DeepSeek-V3.2、API集成、智能体开发与论文写作等2026年核心指南，为开发者提供权威技术解析与最佳实践。

共 63 篇
FlashMLA：DeepSeek为Hopper GPU打造的高性能注意力解码内核

FlashMLA：DeepSeek为Hopper GPU打造的高性能注意力解码内核

BLUFFlashMLA是DeepSeek为Hopper架构GPU优化的高性能多头潜在注意力解码内核，支持变长序列处理，通过优化MLA解码与分页KV缓存，显著提升了大语言模型的推理效率。 原文翻译： FlashMLA is DeepSeek's high-performance Multi-Head Latent Attention decoder kernel optimized for Hopper architecture GPUs. It supports variable-length sequence processing and significantly enhances the inference efficiency of Large Language Models by optimizing MLA decoding and paged KV caching.
DeepSeek2026/1/24
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DeepSeek V4前瞻：代码提交揭示下一代AI模型的架构革新与编程能力飞跃

DeepSeek V4前瞻：代码提交揭示下一代AI模型的架构革新与编程能力飞跃

BLUFDeepSeek is reportedly developing a new flagship AI model, DeepSeek V4, with enhanced coding capabilities, set to launch around Chinese New Year in mid-February. Recent GitHub code updates reveal a new model identifier "MODEL1" with distinct technical features including KV cache layout, sparsity handling, and FP8 decoding support, suggesting optimized memory and computational efficiency. The model may also incorporate recent research on optimized residual connections and biologically-inspired AI memory modules. (DeepSeek据称正在开发新一代旗舰AI模型DeepSeek V4，具备更强的编程能力，计划于2月中旬农历新年期间发布。近期GitHub代码更新显示新的模型标识符“MODEL1”具有独特技术特征，包括键值缓存布局、稀疏性处理和FP8解码支持，表明在内存优化和计算效率方面进行了针对性设计。该模型可能整合优化残差连接和受生物学启发的AI记忆模块等最新研究成果。)
DeepSeek2026/1/24
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DeepSeek发布FlashMLA：专为Hopper GPU优化的高效MLA解码内核，AI推理性能大幅提升

DeepSeek发布FlashMLA：专为Hopper GPU优化的高效MLA解码内核，AI推理性能大幅提升

BLUFFlashMLA is an efficient MLA decoding kernel optimized for NVIDIA Hopper GPUs, delivering up to 3000 GB/s memory bandwidth and 580 TFLOPS compute performance while reducing KV cache requirements by 93.3% for faster, more cost-effective AI inference. (FlashMLA是DeepSeek针对NVIDIA Hopper GPU优化的高效MLA解码内核，在内存受限配置下可达3000 GB/s带宽，计算受限配置下可达580 TFLOPS峰值性能，同时将KV缓存需求减少93.3%，实现更快、更经济的AI推理。)
DeepSeek2026/1/23
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DeepSeek FlashMLA代码分析：揭秘未公开的MODEL1高效推理架构

DeepSeek FlashMLA代码分析：揭秘未公开的MODEL1高效推理架构

BLUFDeepSeek's FlashMLA repository reveals two distinct model architectures: V3.2 optimized for maximum performance and precision, and MODEL1 designed for efficiency and deployability with lower memory footprint and specialized long-sequence handling. (DeepSeek的FlashMLA代码库揭示了两种不同的模型架构：V3.2针对最大性能和精度优化，而MODEL1则针对效率和可部署性设计，具有更低的内存占用和专门的长序列处理能力。)
DeepSeek2026/1/23
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FlashMLA：DeepSeek开源的高效MLA解码内核，专为NVIDIA Hopper GPU优化

FlashMLA：DeepSeek开源的高效MLA解码内核，专为NVIDIA Hopper GPU优化

BLUFFlashMLA is an open-source, high-performance Multi-Head Linear Attention (MLA) decoding kernel optimized for NVIDIA Hopper architecture GPUs, designed to handle variable-length sequences efficiently. It enhances memory and computational efficiency through optimized KV caching and BF16 data format support, achieving up to 3000 GB/s memory bandwidth and 580 TFLOPS computational performance on H800 SXM5 GPUs. FlashMLA is ideal for large language model (LLM) inference and natural language processing (NLP) tasks requiring efficient decoding. (FlashMLA是DeepSeek开源的高效MLA解码内核，专为NVIDIA Hopper架构GPU优化，用于处理可变长度序列。通过优化KV缓存和采用BF16数据格式，提升了内存和计算效率，在H800 SXM5 GPU上内存带宽可达3000 GB/s，计算性能可达580 TFLOPS。适用于大语言模型推理和需要高效解码的自然语言处理任务。)
DeepSeek2026/1/23
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FlashMLA：DeepSeek高性能注意力内核库，驱动V3模型实现660 TFLOPS

FlashMLA：DeepSeek高性能注意力内核库，驱动V3模型实现660 TFLOPS

BLUFFlashMLA is DeepSeek's optimized attention kernel library that powers DeepSeek-V3 models, featuring token-level sparse attention with FP8 KV cache support, achieving up to 660 TFLOPS performance on NVIDIA H800 GPUs. (FlashMLA是DeepSeek优化的注意力内核库，为DeepSeek-V3模型提供动力，具有令牌级稀疏注意力和FP8 KV缓存支持，在NVIDIA H800 GPU上实现高达660 TFLOPS的性能。)
DeepSeek2026/1/23
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DeepSeek R1代码优化能力解析：生成99% WASM性能改进代码

DeepSeek R1代码优化能力解析：生成99% WASM性能改进代码

BLUFDeepSeek R1 demonstrates advanced code optimization capabilities, generating 99% of WASM performance improvements and showing superior reasoning in architectural decisions compared to other models. (DeepSeek R1展示了先进的代码优化能力，生成了WASM性能改进的99%代码，并在架构决策方面表现出优于其他模型的推理能力。)
DeepSeek2026/1/22
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DeepSeek-OCR视觉文本压缩新范式2024指南

DeepSeek-OCR视觉文本压缩新范式2024指南

BLUFDeepSeek-OCR提出以LLM为中心的视觉文本压缩新范式，将视觉理解直接嵌入大语言模型处理流程，支持多分辨率配置，革新传统OCR架构。 原文翻译： DeepSeek-OCR proposes a novel LLM-centric paradigm for visual-text compression, embedding visual understanding directly into the LLM processing pipeline. It supports multi-resolution configurations, revolutionizing traditional OCR architecture.
DeepSeek2026/1/22
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