210 lines
8.4 KiB
JSON
210 lines
8.4 KiB
JSON
[
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{
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"id": "h100",
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"name": "NVIDIA H100",
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"manufacturer": "NVIDIA",
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"architecture": "Hopper",
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"cuda_cores": 16896,
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"tensor_cores": 528,
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"memory_gb": 80,
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"memory_bandwidth_gbs": 3352,
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"fp32_tflops": 67,
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"fp16_tflops": 1979,
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"int8_perf_tops": 3958,
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"price_usd": 30000,
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"description": "数据中心顶级GPU，专为AI训练设计",
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"subcategory_id": "datacenter",
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"publish_date": "2022-01-01",
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"visible": true
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},
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{
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"id": "a100",
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"name": "NVIDIA A100",
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"manufacturer": "NVIDIA",
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"architecture": "Ampere",
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"cuda_cores": 6912,
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"tensor_cores": 432,
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"memory_gb": 80,
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"memory_bandwidth_gbs": 2039,
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"fp32_tflops": 19.5,
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"fp16_tflops": 312,
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"int8_perf_tops": 624,
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"price_usd": 10000,
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"description": "数据中心主力GPU，AI训练推理通用",
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"subcategory_id": "datacenter",
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"publish_date": "2020-01-01"
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},
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{
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"id": "a10040g",
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"name": "NVIDIA A100 40GB",
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"manufacturer": "NVIDIA",
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"architecture": "Ampere",
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"cuda_cores": 6912,
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"tensor_cores": 432,
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"memory_gb": 40,
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"memory_bandwidth_gbs": 1555,
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"fp32_tflops": 19.5,
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"fp16_tflops": 312,
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"int8_perf_tops": 624,
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"price_usd": 6000,
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"description": "A100 40GB版本，性价比更高",
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"subcategory_id": "datacenter",
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"publish_date": "2020-01-01"
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},
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{
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"id": "l40s",
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"name": "NVIDIA L40S",
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"manufacturer": "NVIDIA",
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"architecture": "Ada Lovelace",
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"cuda_cores": 18176,
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"tensor_cores": 568,
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"memory_gb": 48,
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"memory_bandwidth_gbs": 864,
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"fp32_tflops": 91.6,
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"fp16_tflops": 362,
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"int8_perf_tops": 724,
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"price_usd": 7000,
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"description": "新一代数据中心GPU，推理优化",
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"subcategory_id": "datacenter",
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"publish_date": "2023-01-01"
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},
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{
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"id": "rtx4090",
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"name": "NVIDIA RTX 4090",
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"manufacturer": "NVIDIA",
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"architecture": "Ada Lovelace",
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"cuda_cores": 16384,
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"tensor_cores": 512,
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"memory_gb": 24,
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"memory_bandwidth_gbs": 1008,
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"fp32_tflops": 82.6,
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"fp16_tflops": 330,
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"int8_perf_tops": 660,
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"price_usd": 1600,
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"description": "消费级最强GPU，适合个人AI开发",
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"subcategory_id": "gaming",
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"publish_date": "2022-01-01"
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},
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{
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"id": "rtx4090d",
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"name": "NVIDIA RTX 4090D",
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"manufacturer": "NVIDIA",
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"architecture": "Ada Lovelace",
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"cuda_cores": 14592,
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"tensor_cores": 456,
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"memory_gb": 24,
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"memory_bandwidth_gbs": 1008,
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"fp32_tflops": 73.5,
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"fp16_tflops": 294,
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"int8_perf_tops": 588,
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"price_usd": 1400,
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"description": "4090中国特供版，性能略降",
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"subcategory_id": "gaming",
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"publish_date": "2024-01-01"
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},
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{
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"id": "rtx3090",
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"name": "NVIDIA RTX 3090",
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"manufacturer": "NVIDIA",
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"architecture": "Ampere",
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"cuda_cores": 10496,
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"tensor_cores": 328,
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"memory_gb": 24,
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"memory_bandwidth_gbs": 936,
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"fp32_tflops": 35.6,
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"fp16_tflops": 142,
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"int8_perf_tops": 284,
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"price_usd": 1200,
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"description": "上一代旗舰，性价比高",
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"subcategory_id": "gaming",
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"publish_date": "2020-01-01"
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},
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{
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"id": "rtx3080",
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"name": "NVIDIA RTX 3080",
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"manufacturer": "NVIDIA",
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"architecture": "Ampere",
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"cuda_cores": 8704,
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"tensor_cores": 272,
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"memory_gb": 10,
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"memory_bandwidth_gbs": 760,
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"fp32_tflops": 29.8,
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"fp16_tflops": 119,
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"int8_perf_tops": 238,
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"price_usd": 700,
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"description": "中高端消费级GPU",
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"subcategory_id": "gaming",
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"publish_date": "2020-01-01"
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},
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{
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"id": "v100",
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"name": "NVIDIA V100",
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"manufacturer": "NVIDIA",
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"architecture": "Volta",
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"cuda_cores": 5120,
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"tensor_cores": 640,
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"memory_gb": 32,
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"memory_bandwidth_gbs": 900,
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"fp32_tflops": 14.8,
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"fp16_tflops": 118,
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"int8_perf_tops": 236,
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"price_usd": 4000,
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"description": "上一代数据中心GPU，仍有价值",
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"subcategory_id": "datacenter",
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"publish_date": "2017-01-01"
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},
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{
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"id": "mi300x",
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"name": "AMD MI300X",
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"manufacturer": "AMD",
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"architecture": "CDNA 3",
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"cuda_cores": 0,
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"tensor_cores": 304,
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"memory_gb": 192,
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"memory_bandwidth_gbs": 5300,
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"fp32_tflops": 81.7,
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"fp16_tflops": 1307,
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"int8_perf_tops": 2614,
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"price_usd": 15000,
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"description": "AMD最强AI GPU，192GB显存",
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"subcategory_id": "datacenter",
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"publish_date": "2023-01-01"
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},
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{
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"name": "RTX 6000D",
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"manufacturer": "NVIDIA",
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"memory_gb": 84,
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"cuda_cores": 19968,
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"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。",
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"id": "f56b2de6fac4",
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"created_at": "2026-04-20 18:19:14",
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"visible": true,
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"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。",
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"currency": "CNY",
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"price_usd": 45000,
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"updated_at": "2026-04-28 11:56:48",
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"subcategory_id": "professional",
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"views": 0,
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"images": [],
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"publish_date": "2024-01-01"
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},
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{
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"name": "RTX PRO 6000",
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"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。",
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"id": "d246301f2032",
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"created_at": "2026-04-20 18:21:00",
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"visible": true,
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"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。",
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"architecture": "GB202",
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"memory_gb": 96,
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"cuda_cores": 24064,
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"currency": "CNY",
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"price_usd": 65000,
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"updated_at": "2026-04-28 11:56:38",
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"manufacturer": "NVIDIA",
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"subcategory_id": "professional",
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"views": 0,
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"images": [],
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"publish_date": "2020-01-01"
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}
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] |