fix: 添加 GET /api/categories/<id> 单个分类详情API

app.py

@@ -951,6 +951,17 @@ def api_categories():
     
     return jsonify(sorted(categories, key=lambda x: x.get('order', 0)))
 
+@app.route('/api/categories/<category_id>')
+def api_category_detail(category_id):
+    """获取单个分类详情"""
+    categories = load_data(CATEGORIES_FILE)
+    category = next((c for c in categories if c['id'] == category_id), None)
+    
+    if not category:
+        return jsonify({'error': 'Category not found'}), 404
+    
+    return jsonify(category)
+
 @app.route('/api/categories', methods=['POST'])
 def api_create_category():
     """创建新分类"""

data/config.json

@@ -6,9 +6,9 @@
   "copyright_year": "2026",
   "contact_email": "wlq@tphai.com",
   "github_url": "",
-  "updated_at": "2026-04-27 19:57:00",
-  "llm_base_url": "http://192.168.2.17:19007/v1",
-  "llm_api_key": "",
-  "llm_model": "auto",
-  "llm_vision_model": "gpt-4-vision-preview"
+  "llm_base_url": "https://open.bigmodel.cn/api/paas/v4",
+  "llm_api_key": "2259e33a1357460abe17919aaf81e73d.K44a8LPQTmFM5PKm",
+  "llm_model": "glm-4.5-air",
+  "llm_vision_model": "glm-4.6v",
+  "updated_at": "2026-04-27 23:58:26"
 }

data/cpus.json

@@ -1,10 +1,141 @@
 [
-  {"id": "epyc9654", "name": "AMD EPYC 9654", "manufacturer": "AMD", "architecture": "Zen 4", "cores": 96, "threads": 192, "base_clock_ghz": 2.4, "boost_clock_ghz": 3.7, "l3_cache_mb": 384, "tdp_watts": 360, "price_usd": 11000, "release_year": 2022, "description": "AMD顶级服务器CPU，96核心"},
-  {"id": "epyc9554", "name": "AMD EPYC 9554", "manufacturer": "AMD", "architecture": "Zen 4", "cores": 64, "threads": 128, "base_clock_ghz": 3.1, "boost_clock_ghz": 3.8, "l3_cache_mb": 256, "tdp_watts": 360, "price_usd": 6800, "release_year": 2022, "description": "64核心高性能服务器CPU"},
-  {"id": "epyc9454", "name": "AMD EPYC 9454", "manufacturer": "AMD", "architecture": "Zen 4", "cores": 48, "threads": 96, "base_clock_ghz": 2.75, "boost_clock_ghz": 3.8, "l3_cache_mb": 192, "tdp_watts": 290, "price_usd": 4100, "release_year": 2022, "description": "48核心服务器CPU"},
-  {"id": "xeonw9359x", "name": "Intel Xeon w9-3595X", "manufacturer": "Intel", "architecture": "Sapphire Rapids", "cores": 56, "threads": 112, "base_clock_ghz": 1.9, "boost_clock_ghz": 4.8, "l3_cache_mb": 105, "tdp_watts": 350, "price_usd": 6200, "release_year": 2023, "description": "Intel顶级工作站CPU"},
-  {"id": "xeonw5345", "name": "Intel Xeon w5-3435", "manufacturer": "Intel", "architecture": "Sapphire Rapids", "cores": 24, "threads": 48, "base_clock_ghz": 3.1, "boost_clock_ghz": 4.7, "l3_cache_mb": 45, "tdp_watts": 230, "price_usd": 950, "release_year": 2023, "description": "中端工作站CPU"},
-  {"id": "ryzen97950x", "name": "AMD Ryzen 9 7950X", "manufacturer": "AMD", "architecture": "Zen 4", "cores": 16, "threads": 32, "base_clock_ghz": 4.5, "boost_clock_ghz": 5.7, "l3_cache_mb": 64, "tdp_watts": 170, "price_usd": 550, "release_year": 2022, "description": "顶级消费级CPU，适合AI开发"},
-  {"id": "ryzen97950x3d", "name": "AMD Ryzen 9 7950X3D", "manufacturer": "AMD", "architecture": "Zen 4", "cores": 16, "threads": 32, "base_clock_ghz": 4.2, "boost_clock_ghz": 5.7, "l3_cache_mb": 144, "tdp_watts": 120, "price_usd": 700, "release_year": 2023, "description": "带3D V-Cache，游戏性能更强"},
-  {"id": "intel14900k", "name": "Intel Core i9-14900K", "manufacturer": "Intel", "architecture": "Raptor Lake Refresh", "cores": 24, "threads": 32, "base_clock_ghz": 3.2, "boost_clock_ghz": 6.0, "l3_cache_mb": 36, "tdp_watts": 125, "price_usd": 580, "release_year": 2023, "description": "Intel顶级消费级CPU"}
+  {
+    "id": "epyc9654",
+    "name": "AMD EPYC 9654",
+    "manufacturer": "AMD",
+    "architecture": "Zen 4",
+    "cores": 96,
+    "threads": 192,
+    "base_clock_ghz": 2.4,
+    "boost_clock_ghz": 3.7,
+    "l3_cache_mb": 384,
+    "tdp_watts": 360,
+    "price_usd": 11000,
+    "release_year": 2022,
+    "description": "AMD顶级服务器CPU，96核心"
+  },
+  {
+    "id": "epyc9554",
+    "name": "AMD EPYC 9554",
+    "manufacturer": "AMD",
+    "architecture": "Zen 4",
+    "cores": 64,
+    "threads": 128,
+    "base_clock_ghz": 3.1,
+    "boost_clock_ghz": 3.8,
+    "l3_cache_mb": 256,
+    "tdp_watts": 360,
+    "price_usd": 6800,
+    "release_year": 2022,
+    "description": "64核心高性能服务器CPU"
+  },
+  {
+    "id": "epyc9454",
+    "name": "AMD EPYC 9454",
+    "manufacturer": "AMD",
+    "architecture": "Zen 4",
+    "cores": 48,
+    "threads": 96,
+    "base_clock_ghz": 2.75,
+    "boost_clock_ghz": 3.8,
+    "l3_cache_mb": 192,
+    "tdp_watts": 290,
+    "price_usd": 4100,
+    "release_year": 2022,
+    "description": "48核心服务器CPU"
+  },
+  {
+    "id": "xeonw9359x",
+    "name": "Intel Xeon w9-3595X",
+    "manufacturer": "Intel",
+    "architecture": "Sapphire Rapids",
+    "cores": 56,
+    "threads": 112,
+    "base_clock_ghz": 1.9,
+    "boost_clock_ghz": 4.8,
+    "l3_cache_mb": 105,
+    "tdp_watts": 350,
+    "price_usd": 6200,
+    "release_year": 2023,
+    "description": "Intel顶级工作站CPU"
+  },
+  {
+    "id": "xeonw5345",
+    "name": "Intel Xeon w5-3435",
+    "manufacturer": "Intel",
+    "architecture": "Sapphire Rapids",
+    "cores": 24,
+    "threads": 48,
+    "base_clock_ghz": 3.1,
+    "boost_clock_ghz": 4.7,
+    "l3_cache_mb": 45,
+    "tdp_watts": 230,
+    "price_usd": 950,
+    "release_year": 2023,
+    "description": "中端工作站CPU"
+  },
+  {
+    "id": "ryzen97950x",
+    "name": "AMD Ryzen 9 7950X",
+    "manufacturer": "AMD",
+    "architecture": "Zen 4",
+    "cores": 16,
+    "threads": 32,
+    "base_clock_ghz": 4.5,
+    "boost_clock_ghz": 5.7,
+    "l3_cache_mb": 64,
+    "tdp_watts": 170,
+    "price_usd": 550,
+    "release_year": 2022,
+    "description": "顶级消费级CPU，适合AI开发"
+  },
+  {
+    "id": "ryzen97950x3d",
+    "name": "AMD Ryzen 9 7950X3D",
+    "manufacturer": "AMD",
+    "architecture": "Zen 4",
+    "cores": 16,
+    "threads": 32,
+    "base_clock_ghz": 4.2,
+    "boost_clock_ghz": 5.7,
+    "l3_cache_mb": 144,
+    "tdp_watts": 120,
+    "price_usd": 700,
+    "release_year": 2023,
+    "description": "带3D V-Cache，游戏性能更强"
+  },
+  {
+    "id": "intel14900k",
+    "name": "Intel Core i9-14900K",
+    "manufacturer": "Intel",
+    "architecture": "Raptor Lake Refresh",
+    "cores": 24,
+    "threads": 32,
+    "base_clock_ghz": 3.2,
+    "boost_clock_ghz": 6.0,
+    "l3_cache_mb": 36,
+    "tdp_watts": 125,
+    "price_usd": 580,
+    "release_year": 2023,
+    "description": "Intel顶级消费级CPU"
+  },
+  {
+    "name": "AMD 锐龙 AI 9 H 365",
+    "manufacturer": "AMD",
+    "architecture": "Zen 5, Zen 5c",
+    "cores": 10,
+    "threads": 20,
+    "base_clock_ghz": 2.0,
+    "boost_clock_ghz": 5.0,
+    "l3_cache_mb": 24,
+    "tdp_watts": 28,
+    "description": "AMD 锐龙 AI 处理器助力打造卓越 AI PC",
+    "id": "52af6cf2dc28",
+    "created_at": "2026-04-20 23:19:20",
+    "visible": true,
+    "raw_text": "AMD 锐龙 AI 9 H 365\nAMD 锐龙 AI 处理器助力打造卓越 AI PC\n\n \n全部折叠\n一般规格\n名称\nAMD 锐龙 AI 9 H 365\n产品系列\n锐龙\n系列\n锐龙 AI 300 系列\n外形规格\n笔记本电脑 , 台式机\nAMD PRO 技术\n否\n区域供货状况\n中国\n原代号\nStrix Point\n处理器架构\n4x Zen 5 , 6x Zen 5c\nCPU 核心数\n10\n多线程 (SMT)\n是\n线程数\n20\n最高加速时钟频率 \n最高可达 5 GHz\nMax Zen5c Clock \n最高可达 3.3 GHz\n基准时钟频率 \n2 GHz\nZen5 Base Clock\n2 GHz\nZen5c Base Clock\n2 GHz\nL2 高速缓存\n10 MB\nL3 高速缓存\n24 MB\n默认热设计功耗 (TDP)\n28W\nAMD 可配置热设计功耗 (cTDP)\n15-54W\nCPU 核心的处理器工艺\nTSMC 4nm FinFET\n封装芯片计数\n1\nAMD EXPO™ 内存超频技术\n是\n精准频率提升 (PBO)\n是\n曲线优化器电压偏移\n是\nCPU 平台\nFP8\n支持的扩展\nAES , AMD-V , AVX , AVX2 , AVX512 , FMA3 , MMX-plus , SHA , SSE , SSE2 , SSE3 , SSE4.1 , SSE4.2 , SSE4A , SSSE3 , x86-64\n最高工作温度 (Tjmax)\n100°C\n*支持的操作系统\nWindows 11 - 64-Bit Edition , RHEL x86 64-Bit , Ubuntu x86 64-Bit\n连接\nNative USB 4 (40Gbps)\n2\nNative USB 3.2 Gen 2 (10Gbps)\n2\nNative USB 2.0 (480Mbps)\n4\nPCI Express® Version\nPCIe® 4.0\n原生 PCIe® 通道 (总共/可用)\n16 , 16\nNVMe 支持\nBoot , RAID0 , RAID1\n系统内存类型\nDDR5 (FP8) , LPDDR5X (FP8)\n内存通道数\n2\n最大内存\n256 GB\n最高内存速度\n2x2R\tDDR5-5600, LPDDR5x-8000\n支持 ECC\n否\n显卡功能\n显卡型号\nAMD Radeon™ 880M\n显卡核心数\n12\n显卡频率\n2900 MHz\nDirectX® 版本\n12\nDisplayPort™ 版本\n2.1\nDisplayPort 扩展功能\nAdaptive-Sync , HDR Metadata , UHBR10\nDisplayPort 最高刷新率 (SDR)\n7680x4320 @ 60Hz , 3840x2160 @ 240Hz , 3440x1440 @ 360Hz , 2560x1440 @ 480Hz , 1920x1080 @ 600Hz\nDisplayPort 最高刷新率 (HDR)\n7680x4320 @ 60Hz , 3840x2160 @ 240Hz , 3440x1440 @ 360Hz , 2560x1440 @ 480Hz , 1920x1080 @ 600Hz\nHDMI® 版本\n2.1\n支持的 HDCP 版本\n2.3\nUSB Type-C® DisplayPort™ 备用模式\n是\n支持多个显示器\n是\n显示器个数上限\n4\nAMD FreeSync™\n是\n无线显示\nMiracast\n最大视频编码带宽 (SDR)\n1080p630 8bpc H.264, 1440p373 8bpc H.264, 2160p175 8bpc H.264, 1080p630 8bpc H.265, 1440p373 8bpc H.265, 2160p175 8bpc H.265, 4320p43 8bpc H.265, 1080p864 8/10bpc AV1, 1440p513 8/10bpc AV1, 2160p240 8/10bpc AV1, 4320p60 8/10bpc AV1\n\n最大视频解码带宽\n1080p60 8bpc MPEG2, 1080p60 8bpc VC1, 1080p786 8/10bpc VP9, 2160p196 8/10bpc VP9, 4320p49 8/10bpc VP9, 1080p1200 8bpc H.264, 2160p300 8bpc H.264, 4320p75 8bpc H.264, 1080p786 8/10bpc H.265, 2160p196 8/10bpc H.265, 4320p49 8/10bpc H.265, 1080p960 8/10bpc\n\nAMD SmartShift MAX\n是\nAMD 显存智取技术\n支持\nAI 引擎性能\nAMD Ryzen™ AI\n支持\nOverall TOPS\n最高可达 73 TOPS\nNPU TOPS\n最高可达 50 TOPS\n产品 ID\nTray 产品 ID\n100-000001530 (FP8)\n安全\nAMD 增强病毒防护 (NX bit)\n是",
+    "publish_date": "",
+    "views": 0,
+    "is_pinned": false
+  }
 ]

data/gpus.json

@@ -1,12 +1,191 @@
 [
-  {"id": "h100", "name": "NVIDIA H100", "manufacturer": "NVIDIA", "architecture": "Hopper", "cuda_cores": 16896, "tensor_cores": 528, "memory_gb": 80, "memory_bandwidth_gbs": 3352, "fp32_tflops": 67, "fp16_tflops": 1979, "int8_perf_tops": 3958, "price_usd": 30000, "release_year": 2022, "description": "数据中心顶级GPU，专为AI训练设计"},
-  {"id": "a100", "name": "NVIDIA A100", "manufacturer": "NVIDIA", "architecture": "Ampere", "cuda_cores": 6912, "tensor_cores": 432, "memory_gb": 80, "memory_bandwidth_gbs": 2039, "fp32_tflops": 19.5, "fp16_tflops": 312, "int8_perf_tops": 624, "price_usd": 10000, "release_year": 2020, "description": "数据中心主力GPU，AI训练推理通用"},
-  {"id": "a10040g", "name": "NVIDIA A100 40GB", "manufacturer": "NVIDIA", "architecture": "Ampere", "cuda_cores": 6912, "tensor_cores": 432, "memory_gb": 40, "memory_bandwidth_gbs": 1555, "fp32_tflops": 19.5, "fp16_tflops": 312, "int8_perf_tops": 624, "price_usd": 6000, "release_year": 2020, "description": "A100 40GB版本，性价比更高"},
-  {"id": "l40s", "name": "NVIDIA L40S", "manufacturer": "NVIDIA", "architecture": "Ada Lovelace", "cuda_cores": 18176, "tensor_cores": 568, "memory_gb": 48, "memory_bandwidth_gbs": 864, "fp32_tflops": 91.6, "fp16_tflops": 362, "int8_perf_tops": 724, "price_usd": 7000, "release_year": 2023, "description": "新一代数据中心GPU，推理优化"},
-  {"id": "rtx4090", "name": "NVIDIA RTX 4090", "manufacturer": "NVIDIA", "architecture": "Ada Lovelace", "cuda_cores": 16384, "tensor_cores": 512, "memory_gb": 24, "memory_bandwidth_gbs": 1008, "fp32_tflops": 82.6, "fp16_tflops": 330, "int8_perf_tops": 660, "price_usd": 1600, "release_year": 2022, "description": "消费级最强GPU，适合个人AI开发"},
-  {"id": "rtx4090d", "name": "NVIDIA RTX 4090D", "manufacturer": "NVIDIA", "architecture": "Ada Lovelace", "cuda_cores": 14592, "tensor_cores": 456, "memory_gb": 24, "memory_bandwidth_gbs": 1008, "fp32_tflops": 73.5, "fp16_tflops": 294, "int8_perf_tops": 588, "price_usd": 1400, "release_year": 2024, "description": "4090中国特供版，性能略降"},
-  {"id": "rtx3090", "name": "NVIDIA RTX 3090", "manufacturer": "NVIDIA", "architecture": "Ampere", "cuda_cores": 10496, "tensor_cores": 328, "memory_gb": 24, "memory_bandwidth_gbs": 936, "fp32_tflops": 35.6, "fp16_tflops": 142, "int8_perf_tops": 284, "price_usd": 1200, "release_year": 2020, "description": "上一代旗舰，性价比高"},
-  {"id": "rtx3080", "name": "NVIDIA RTX 3080", "manufacturer": "NVIDIA", "architecture": "Ampere", "cuda_cores": 8704, "tensor_cores": 272, "memory_gb": 10, "memory_bandwidth_gbs": 760, "fp32_tflops": 29.8, "fp16_tflops": 119, "int8_perf_tops": 238, "price_usd": 700, "release_year": 2020, "description": "中高端消费级GPU"},
-  {"id": "v100", "name": "NVIDIA V100", "manufacturer": "NVIDIA", "architecture": "Volta", "cuda_cores": 5120, "tensor_cores": 640, "memory_gb": 32, "memory_bandwidth_gbs": 900, "fp32_tflops": 14.8, "fp16_tflops": 118, "int8_perf_tops": 236, "price_usd": 4000, "release_year": 2017, "description": "上一代数据中心GPU，仍有价值"},
-  {"id": "mi300x", "name": "AMD MI300X", "manufacturer": "AMD", "architecture": "CDNA 3", "cuda_cores": 0, "tensor_cores": 304, "memory_gb": 192, "memory_bandwidth_gbs": 5300, "fp32_tflops": 81.7, "fp16_tflops": 1307, "int8_perf_tops": 2614, "price_usd": 15000, "release_year": 2023, "description": "AMD最强AI GPU，192GB显存"}
+  {
+    "id": "h100",
+    "name": "NVIDIA H100",
+    "manufacturer": "NVIDIA",
+    "architecture": "Hopper",
+    "cuda_cores": 16896,
+    "tensor_cores": 528,
+    "memory_gb": 80,
+    "memory_bandwidth_gbs": 3352,
+    "fp32_tflops": 67,
+    "fp16_tflops": 1979,
+    "int8_perf_tops": 3958,
+    "price_usd": 30000,
+    "release_year": 2022,
+    "description": "数据中心顶级GPU，专为AI训练设计"
+  },
+  {
+    "id": "a100",
+    "name": "NVIDIA A100",
+    "manufacturer": "NVIDIA",
+    "architecture": "Ampere",
+    "cuda_cores": 6912,
+    "tensor_cores": 432,
+    "memory_gb": 80,
+    "memory_bandwidth_gbs": 2039,
+    "fp32_tflops": 19.5,
+    "fp16_tflops": 312,
+    "int8_perf_tops": 624,
+    "price_usd": 10000,
+    "release_year": 2020,
+    "description": "数据中心主力GPU，AI训练推理通用"
+  },
+  {
+    "id": "a10040g",
+    "name": "NVIDIA A100 40GB",
+    "manufacturer": "NVIDIA",
+    "architecture": "Ampere",
+    "cuda_cores": 6912,
+    "tensor_cores": 432,
+    "memory_gb": 40,
+    "memory_bandwidth_gbs": 1555,
+    "fp32_tflops": 19.5,
+    "fp16_tflops": 312,
+    "int8_perf_tops": 624,
+    "price_usd": 6000,
+    "release_year": 2020,
+    "description": "A100 40GB版本，性价比更高"
+  },
+  {
+    "id": "l40s",
+    "name": "NVIDIA L40S",
+    "manufacturer": "NVIDIA",
+    "architecture": "Ada Lovelace",
+    "cuda_cores": 18176,
+    "tensor_cores": 568,
+    "memory_gb": 48,
+    "memory_bandwidth_gbs": 864,
+    "fp32_tflops": 91.6,
+    "fp16_tflops": 362,
+    "int8_perf_tops": 724,
+    "price_usd": 7000,
+    "release_year": 2023,
+    "description": "新一代数据中心GPU，推理优化"
+  },
+  {
+    "id": "rtx4090",
+    "name": "NVIDIA RTX 4090",
+    "manufacturer": "NVIDIA",
+    "architecture": "Ada Lovelace",
+    "cuda_cores": 16384,
+    "tensor_cores": 512,
+    "memory_gb": 24,
+    "memory_bandwidth_gbs": 1008,
+    "fp32_tflops": 82.6,
+    "fp16_tflops": 330,
+    "int8_perf_tops": 660,
+    "price_usd": 1600,
+    "release_year": 2022,
+    "description": "消费级最强GPU，适合个人AI开发"
+  },
+  {
+    "id": "rtx4090d",
+    "name": "NVIDIA RTX 4090D",
+    "manufacturer": "NVIDIA",
+    "architecture": "Ada Lovelace",
+    "cuda_cores": 14592,
+    "tensor_cores": 456,
+    "memory_gb": 24,
+    "memory_bandwidth_gbs": 1008,
+    "fp32_tflops": 73.5,
+    "fp16_tflops": 294,
+    "int8_perf_tops": 588,
+    "price_usd": 1400,
+    "release_year": 2024,
+    "description": "4090中国特供版，性能略降"
+  },
+  {
+    "id": "rtx3090",
+    "name": "NVIDIA RTX 3090",
+    "manufacturer": "NVIDIA",
+    "architecture": "Ampere",
+    "cuda_cores": 10496,
+    "tensor_cores": 328,
+    "memory_gb": 24,
+    "memory_bandwidth_gbs": 936,
+    "fp32_tflops": 35.6,
+    "fp16_tflops": 142,
+    "int8_perf_tops": 284,
+    "price_usd": 1200,
+    "release_year": 2020,
+    "description": "上一代旗舰，性价比高"
+  },
+  {
+    "id": "rtx3080",
+    "name": "NVIDIA RTX 3080",
+    "manufacturer": "NVIDIA",
+    "architecture": "Ampere",
+    "cuda_cores": 8704,
+    "tensor_cores": 272,
+    "memory_gb": 10,
+    "memory_bandwidth_gbs": 760,
+    "fp32_tflops": 29.8,
+    "fp16_tflops": 119,
+    "int8_perf_tops": 238,
+    "price_usd": 700,
+    "release_year": 2020,
+    "description": "中高端消费级GPU"
+  },
+  {
+    "id": "v100",
+    "name": "NVIDIA V100",
+    "manufacturer": "NVIDIA",
+    "architecture": "Volta",
+    "cuda_cores": 5120,
+    "tensor_cores": 640,
+    "memory_gb": 32,
+    "memory_bandwidth_gbs": 900,
+    "fp32_tflops": 14.8,
+    "fp16_tflops": 118,
+    "int8_perf_tops": 236,
+    "price_usd": 4000,
+    "release_year": 2017,
+    "description": "上一代数据中心GPU，仍有价值"
+  },
+  {
+    "id": "mi300x",
+    "name": "AMD MI300X",
+    "manufacturer": "AMD",
+    "architecture": "CDNA 3",
+    "cuda_cores": 0,
+    "tensor_cores": 304,
+    "memory_gb": 192,
+    "memory_bandwidth_gbs": 5300,
+    "fp32_tflops": 81.7,
+    "fp16_tflops": 1307,
+    "int8_perf_tops": 2614,
+    "price_usd": 15000,
+    "release_year": 2023,
+    "description": "AMD最强AI GPU，192GB显存"
+  },
+  {
+    "name": "RTX 6000D",
+    "manufacturer": "NVIDIA",
+    "memory_gb": 84,
+    "cuda_cores": 19968,
+    "description": "NVIDIA为中国市场定制的全新工作站显卡，搭载84GB GDDR7显存、19968个CUDA核心，采用被动散热设计，专为服务器机箱风道优化。显存总线为448位，核心频率为2430MHz，在Geekbench 6 OpenCL测试中获得390,656分。",
+    "id": "f56b2de6fac4",
+    "created_at": "2026-04-20 18:19:14",
+    "visible": true,
+    "raw_text": "据tweaktown报道，NVIDIA为中国市场定制的全新工作站显卡RTX 6000D近日迎来首度拆解。该卡搭载84GB GDDR7显存、19968个CUDA核心，采用被动散热设计，专为服务器机箱风道优化。\n\n\n相较于满血RTX PRO 6000（96GB GDDR7/512-bit），中国特供版RTX 6000D在规格上进行了多处调整。国内团队“技数犬”发布了拆解视频。\n\n据了解，RTX 6000D为无风扇被动散热设计，完全依靠机箱气流降温。\n\nRTX 6000D搭载28颗VRAM模块，总计84GB GDDR7显存，显存总线为448位（相比RTX PRO 6000的96GB/512位有所减少）。\n\nRTX 6000D GPU 核心为156 SM单元，19,968个CUDA核心，比RTX PRO 6000少约17%。\n\nRTX 6000D核心频率为2430MHz（RTX PRO 6000为2600MHz），TDP暂未公布。性能方面，RTX 6000D在Geekbench 6 OpenCL测试中获得390,656分，低于RTX PRO 6000的45–50万分。",
+    "currency": "CNY",
+    "price_usd": 45000,
+    "updated_at": "2026-04-20 18:28:10"
+  },
+  {
+    "name": "RTX PRO 6000",
+    "description": "这款专业显卡基于 GB202 GPU，拥有 24064 个 CUDA 核心（188 个 SM），运行频率达 2,617 MHz，并配备 96 GB 支持 ECC 校验的 GDDR7 显存。\n\n相比之下，面向游戏市场的旗舰显卡 RTX 5090 虽同样基于 GB202 ，但其 CUDA 核心数量缩减至 21,760 个，频率为 2,410 MHz，显存容量为 32 GB。\n\n96G超大显存RTX PRO 6000Blackwell初次跑分，略逊于RTX 5090\n其测试平台采用了华硕 Pro WS WRX80E-SAGE SE WIFI 主板、AMD 锐龙 Threadripper PRO 3975WX 处理器、512 GB 内存。\n\n在 Geekbench 6.4.0 上，其测试平台 OpenCL 得分仅 368219 分，略低于 RTX 5090 的 376,858 分，差距约 2.3%，外媒认为这主要是由于 RTX PRO 6000 缺乏正式版驱动导致，且显卡功耗可能受限。\n\nRTX PRO 6000 系列将提供两种版本，分别为适用于紧凑型机箱，规格相同的Max-Q 工作站版，但TDP 功耗限制在 300W；以及支持最高600W TDP的标准版，可满足高强度计算需求。",
+    "id": "d246301f2032",
+    "created_at": "2026-04-20 18:21:00",
+    "visible": true,
+    "raw_text": "这款专业显卡基于 GB202 GPU，拥有 24064 个 CUDA 核心（188 个 SM），运行频率达 2,617 MHz，并配备 96 GB 支持 ECC 校验的 GDDR7 显存。\n\n相比之下，面向游戏市场的旗舰显卡 RTX 5090 虽同样基于 GB202 ，但其 CUDA 核心数量缩减至 21,760 个，频率为 2,410 MHz，显存容量为 32 GB。\n\n96G超大显存RTX PRO 6000Blackwell初次跑分，略逊于RTX 5090\n其测试平台采用了华硕 Pro WS WRX80E-SAGE SE WIFI 主板、AMD 锐龙 Threadripper PRO 3975WX 处理器、512 GB 内存。\n\n在 Geekbench 6.4.0 上，其测试平台 OpenCL 得分仅 368219 分，略低于 RTX 5090 的 376,858 分，差距约 2.3%，外媒认为这主要是由于 RTX PRO 6000 缺乏正式版驱动导致，且显卡功耗可能受限。\n\nRTX PRO 6000 系列将提供两种版本，分别为适用于紧凑型机箱，规格相同的Max-Q 工作站版，但TDP 功耗限制在 300W；以及支持最高600W TDP的标准版，可满足高强度计算需求。",
+    "architecture": "GB202",
+    "memory_gb": 96,
+    "cuda_cores": 24064,
+    "currency": "CNY",
+    "price_usd": 65000,
+    "updated_at": "2026-04-20 18:28:23",
+    "manufacturer": "NVIDIA"
+  }
 ]

data/items_71fa2b4d818f.json

@@ -0,0 +1,77 @@
+[
+  {
+    "name": "Osmo Pocket 4",
+    "brand": "DJI",
+    "price": 2999,
+    "specs": {
+      "传感器类型": "1英寸CMOS",
+      "镜头": "20mm, f/2.0",
+      "ISO范围": "50-12800",
+      "视频分辨率": "4K 60fps",
+      "照片最大分辨率": "5472×3648",
+      "电池容量": "1545mAh",
+      "工作温度": "0°C至40°C"
+    },
+    "id": "597e29af5937",
+    "category_id": "71fa2b4d818f",
+    "created_at": "2026-04-28 00:07:01",
+    "visible": true,
+    "raw_text": "",
+    "images": [
+      "/static/uploads/1ad784e0b3c6_1777305525.png"
+    ],
+    "publish_date": "",
+    "views": 0,
+    "is_pinned": false
+  },
+  {
+    "name": "Osmo Pocket 3",
+    "brand": "DJI",
+    "price": 2799,
+    "specs": {
+      "传感器类型": "1英寸CMOS",
+      "镜头": "20mm, f/2.0",
+      "ISO范围": "50-6400",
+      "视频分辨率": "4K 60fps",
+      "照片最大分辨率": "5472×3648",
+      "电池容量": "1300mAh",
+      "工作温度": "0°C至40°C"
+    },
+    "id": "ad10ac80827b",
+    "category_id": "71fa2b4d818f",
+    "created_at": "2026-04-28 00:07:01",
+    "visible": true,
+    "raw_text": "",
+    "images": [
+      "/static/uploads/1ad784e0b3c6_1777305525.png"
+    ],
+    "publish_date": "",
+    "views": 0,
+    "is_pinned": false
+  },
+  {
+    "name": "DJI Pocket 2",
+    "brand": "DJI",
+    "price": 1999,
+    "specs": {
+      "传感器类型": "1/1.7英寸CMOS",
+      "镜头": "20mm, f/1.8",
+      "ISO范围": "100-3200",
+      "视频分辨率": "4K 60fps",
+      "照片最大分辨率": "6272×4680",
+      "电池容量": "875mAh",
+      "工作温度": "0°C至40°C"
+    },
+    "id": "0fde0f10ad96",
+    "category_id": "71fa2b4d818f",
+    "created_at": "2026-04-28 00:07:01",
+    "visible": true,
+    "raw_text": "",
+    "images": [
+      "/static/uploads/1ad784e0b3c6_1777305525.png"
+    ],
+    "publish_date": "",
+    "views": 0,
+    "is_pinned": false
+  }
+]

data/knowledge.json

@@ -1,9 +1,66 @@
 [
-  {"id": "k001", "title": "什么是参数量？", "category": "ai-models", "icon": "ri-calculator-line", "content": "参数量（Parameters）是衡量大模型规模的指标，表示模型中权重参数的数量。例如 GPT-3 有 175B 参数，即约1750亿个参数。", "detail": "参数量决定了模型的容量和表达能力。一般来说，参数量越大，模型能力越强，但也需要更多计算资源。\n\n常见规模分类：\n- 小模型：<1B (适合边缘设备)\n- 中模型：1B-10B (消费级GPU可运行)\n- 大模型：10B-100B (需要多GPU)\n- 超大模型：>100B (需要数据中心)", "order": 1},
-  {"id": "k002", "title": "什么是上下文长度？", "category": "ai-models", "icon": "ri-text-wrap", "content": "上下文长度（Context Length）是模型能处理的输入文本最大长度。更长的上下文意味着模型可以理解更长的文档或对话历史。", "detail": "常见长度：\n- 4K：传统长度，适合简单对话\n- 32K：中等长度，适合长文档\n- 128K：超长上下文，如GPT-4 Turbo\n- 200K：Claude 3的极限长度", "order": 2},
-  {"id": "k003", "title": "什么是量化？", "category": "ai-models", "icon": "ri-scales-3-line", "content": "量化（Quantization）是将模型参数从高精度转换为低精度，减少显存占用和计算量。如FP16→INT8→INT4，精度损失可控，资源节省显著。", "detail": "量化效果：\n- FP32→FP16: 显存减半，精度基本不变\n- FP16→INT8: 显存再减半，精度略降\n- INT8→INT4: 显存再减半，需特殊技术\n\n推荐工具：llama.cpp、GPTQ、AWQ等", "order": 3},
-  {"id": "k004", "title": "什么是MMLU？", "category": "ai-models", "icon": "ri-bar-chart-box-line", "content": "MMLU（Massive Multitask Language Understanding）是评估大模型综合能力的标准测试集，覆盖57个学科领域。", "detail": "分数参考：\n- 60-70%：入门级，如GPT-3\n- 70-80%：中等水平，如Llama 2 70B\n- 80-90%：优秀水平，如GPT-4、Claude 3", "order": 4},
-  {"id": "k005", "title": "如何计算显存需求？", "category": "gpus", "icon": "ri-memory-line", "content": "模型显存需求 ≈ 参数量 × 每参数字节数 × 1.3（含KV Cache开销）", "detail": "计算公式：\n- FP32: 参数量 × 4字节 × 1.3\n- FP16: 参数量 × 2字节 × 1.3\n- INT8: 参数量 × 1字节 × 1.3\n- INT4: 参数量 × 0.5字节 × 1.3\n\n例如：7B模型FP16加载需要约 7 × 2 × 1.3 ≈ 18GB显存", "order": 1},
-  {"id": "k006", "title": "GPU架构演进", "category": "gpus", "icon": "ri-history-line", "content": "NVIDIA GPU架构从Fermi到Hopper，每一代都有显著提升。了解架构有助于选择合适的GPU。", "detail": "主要架构：\n- Volta (2017): V100, 引入Tensor Core\n- Turing (2018): RTX 20系列, RT Core\n- Ampere (2020): A100, RTX 30系列\n- Hopper (2022): H100, FP8支持\n- Ada Lovelace (2022): RTX 40系列, L40S", "order": 2},
-  {"id": "k007", "title": "CPU核心数选择", "category": "cpus", "icon": "ri-database-2-line", "content": "CPU核心数的选择取决于应用场景。更多核心适合并行任务，但单核性能也很重要。", "detail": "场景推荐：\n- 办公/日常：4-6核足够\n- 开发/编译：8-16核\n- 服务器/虚拟化：16-64核\n- 高性能计算：64核以上\n\n注意：AI训练主要依赖GPU，CPU主要用于数据预处理", "order": 1}
+  {
+    "id": "k001",
+    "title": "什么是参数量？",
+    "category": "ai-models",
+    "icon": "ri-calculator-line",
+    "content": "参数量（Parameters）是衡量大模型规模的指标，表示模型中权重参数的数量。例如 GPT-3 有 175B 参数，即约1750亿个参数。",
+    "detail": "参数量决定了模型的容量和表达能力。一般来说，参数量越大，模型能力越强，但也需要更多计算资源。\n\n常见规模分类：\n- 小模型：<1B (适合边缘设备)\n- 中模型：1B-10B (消费级GPU可运行)\n- 大模型：10B-100B (需要多GPU)\n- 超大模型：>100B (需要数据中心)",
+    "order": 1
+  },
+  {
+    "id": "k002",
+    "title": "什么是上下文长度？",
+    "category": "ai-models",
+    "icon": "ri-text-wrap",
+    "content": "上下文长度（Context Length）是模型能处理的输入文本最大长度。更长的上下文意味着模型可以理解更长的文档或对话历史。",
+    "detail": "常见长度：\n- 4K：传统长度，适合简单对话\n- 32K：中等长度，适合长文档\n- 128K：超长上下文，如GPT-4 Turbo\n- 200K：Claude 3的极限长度",
+    "order": 2
+  },
+  {
+    "id": "k003",
+    "title": "什么是量化？",
+    "category": "ai-models",
+    "icon": "ri-scales-3-line",
+    "content": "量化（Quantization）是将模型参数从高精度转换为低精度，减少显存占用和计算量。如FP16→INT8→INT4，精度损失可控，资源节省显著。",
+    "detail": "量化效果：\n- FP32→FP16: 显存减半，精度基本不变\n- FP16→INT8: 显存再减半，精度略降\n- INT8→INT4: 显存再减半，需特殊技术\n\n推荐工具：llama.cpp、GPTQ、AWQ等",
+    "order": 3
+  },
+  {
+    "id": "k004",
+    "title": "什么是MMLU？",
+    "category": "ai-models",
+    "icon": "ri-bar-chart-box-line",
+    "content": "MMLU（Massive Multitask Language Understanding）是评估大模型综合能力的标准测试集，覆盖57个学科领域。",
+    "detail": "分数参考：\n- 60-70%：入门级，如GPT-3\n- 70-80%：中等水平，如Llama 2 70B\n- 80-90%：优秀水平，如GPT-4、Claude 3",
+    "order": 4,
+    "visible": false
+  },
+  {
+    "id": "k005",
+    "title": "如何计算显存需求？",
+    "category": "gpus",
+    "icon": "ri-memory-line",
+    "content": "模型显存需求 ≈ 参数量 × 每参数字节数 × 1.3（含KV Cache开销）",
+    "detail": "计算公式：\n- FP32: 参数量 × 4字节 × 1.3\n- FP16: 参数量 × 2字节 × 1.3\n- INT8: 参数量 × 1字节 × 1.3\n- INT4: 参数量 × 0.5字节 × 1.3\n\n例如：7B模型FP16加载需要约 7 × 2 × 1.3 ≈ 18GB显存",
+    "order": 1
+  },
+  {
+    "id": "k006",
+    "title": "GPU架构演进",
+    "category": "gpus",
+    "icon": "ri-history-line",
+    "content": "NVIDIA GPU架构从Fermi到Hopper，每一代都有显著提升。了解架构有助于选择合适的GPU。",
+    "detail": "主要架构：\n- Volta (2017): V100, 引入Tensor Core\n- Turing (2018): RTX 20系列, RT Core\n- Ampere (2020): A100, RTX 30系列\n- Hopper (2022): H100, FP8支持\n- Ada Lovelace (2022): RTX 40系列, L40S",
+    "order": 2
+  },
+  {
+    "id": "k007",
+    "title": "CPU核心数选择",
+    "category": "cpus",
+    "icon": "ri-database-2-line",
+    "content": "CPU核心数的选择取决于应用场景。更多核心适合并行任务，但单核性能也很重要。",
+    "detail": "场景推荐：\n- 办公/日常：4-6核足够\n- 开发/编译：8-16核\n- 服务器/虚拟化：16-64核\n- 高性能计算：64核以上\n\n注意：AI训练主要依赖GPU，CPU主要用于数据预处理",
+    "order": 1
+  }
 ]

data/models.json

@@ -190,6 +190,6 @@
     "license": "Proprietary",
     "description": "智谱AI大模型，中文能力强",
     "created_at": "2024-01-01",
-    "visible": true
+    "visible": false
   }
 ]