From 692a5bfbe90454ab5a0d89f4516eb4dda8580024 Mon Sep 17 00:00:00 2001 From: huangzhuang_3rd Date: Mon, 1 Jun 2026 18:30:58 +0800 Subject: [PATCH] =?UTF-8?q?v1.8.0:=20=E6=A8=A1=E5=9D=97=E5=8C=96=E9=87=8D?= =?UTF-8?q?=E6=9E=84=20+=20=E5=90=8E=E5=8F=B0=E7=99=BB=E5=BD=95=E8=AE=A4?= =?UTF-8?q?=E8=AF=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- data/gpus.json | 210 +++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 210 insertions(+) create mode 100644 data/gpus.json diff --git a/data/gpus.json b/data/gpus.json new file mode 100644 index 0000000..c15fd95 --- /dev/null +++ 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" + } +] \ No newline at end of file