v1.8.0: 模块化重构 + 后台登录认证

2026-06-01 18:30:58 +08:00
parent cf0660f550
commit 692a5bfbe9
1 changed files with 210 additions and 0 deletions
--- a/data/gpus.json
+++ b/data/gpus.json
@@ -0,0 +1,210 @@
+[
+  {
+    "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,
+    "description": "数据中心顶级GPU，专为AI训练设计",
+    "subcategory_id": "datacenter",
+    "publish_date": "2022-01-01",
+    "visible": true
+  },
+  {
+    "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,
+    "description": "数据中心主力GPU，AI训练推理通用",
+    "subcategory_id": "datacenter",
+    "publish_date": "2020-01-01"
+  },
+  {
+    "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,
+    "description": "A100 40GB版本，性价比更高",
+    "subcategory_id": "datacenter",
+    "publish_date": "2020-01-01"
+  },
+  {
+    "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,
+    "description": "新一代数据中心GPU，推理优化",
+    "subcategory_id": "datacenter",
+    "publish_date": "2023-01-01"
+  },
+  {
+    "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,
+    "description": "消费级最强GPU，适合个人AI开发",
+    "subcategory_id": "gaming",
+    "publish_date": "2022-01-01"
+  },
+  {
+    "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,
+    "description": "4090中国特供版，性能略降",
+    "subcategory_id": "gaming",
+    "publish_date": "2024-01-01"
+  },
+  {
+    "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,
+    "description": "上一代旗舰，性价比高",
+    "subcategory_id": "gaming",
+    "publish_date": "2020-01-01"
+  },
+  {
+    "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,
+    "description": "中高端消费级GPU",
+    "subcategory_id": "gaming",
+    "publish_date": "2020-01-01"
+  },
+  {
+    "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,
+    "description": "上一代数据中心GPU，仍有价值",
+    "subcategory_id": "datacenter",
+    "publish_date": "2017-01-01"
+  },
+  {
+    "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,
+    "description": "AMD最强AI GPU，192GB显存",
+    "subcategory_id": "datacenter",
+    "publish_date": "2023-01-01"
+  },
+  {
+    "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": "2026-04-28 11:56:48",
+    "subcategory_id": "professional",
+    "views": 0,
+    "images": [],
+    "publish_date": "2024-01-01"
+  },
+  {
+    "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": "2026-04-28 11:56:38",
+    "manufacturer": "NVIDIA",
+    "subcategory_id": "professional",
+    "views": 0,
+    "images": [],
+    "publish_date": "2020-01-01"
+  }
+]