\u7a81\u7834\u6027\u7684 NVLink \u4e92\u8fde\u6027\u80fd<\/b><\/strong><\/h2>\n\u672c\u4ea7\u54c1\u901a\u8fc7\u521b\u65b0\u7684\u8bbe\u8ba1\uff0c\u7ed5\u8fc7\u4e86\u4f20\u7edf PCIe \u901a\u9053\u7684\u9650\u5236\uff1a<\/p>\n
\n- \u5229\u7528 PEX8749 \u4ea4\u6362\u82af\u7247\uff0c\u5c06\u4e3b\u673a\u7684\u4e00\u6761 PCIe 3.0 x16 \u63d2\u69fd \u903b\u8f91\u62c6\u5206\u4e3a\u4e24\u6761\u72ec\u7acb\u7684 PCIe 3.0 x16 \u901a\u9053\uff0c\u5206\u522b\u8fde\u63a5\u81f3\u4e24\u5757 V100 \u663e\u5361\u3002<\/li>\n
- \u4e24\u5757 V100 \u663e\u5361\u4e4b\u95f4\u901a\u8fc7\u4e13\u7528\u7684 6 \u901a\u9053 NVLink 2.0 \u6865\u63a5\u5668\u8fdb\u884c\u76f4\u63a5\u9ad8\u901f\u4e92\u8fde\uff0c\u63d0\u4f9b\u9ad8\u8fbe 300 GB\/s \u7684\u53cc\u5411\u603b\u5e26\u5bbd\u3002<\/li>\n
- \u8fd9\u79cd\u8bbe\u8ba1\u786e\u4fdd\u4e86\u53cc\u5361\u4e4b\u95f4\u7684\u6570\u636e\u4ea4\u6362\u901f\u5ea6\u8fdc\u8d85 PCIe 3.0 x16 (\u7ea6 16 GB\/s \u5355\u5411\u5e26\u5bbd)\uff0c\u5145\u5206\u53d1\u6325\u4e86 V100 NVLink \u4e92\u8fde\u7684\u6781\u81f4\u6027\u80fd\uff0c\u663e\u8457\u63d0\u5347\u591a\u5361\u534f\u540c\u8ba1\u7b97\u6548\u7387\u3002<\/li>\n<\/ol>\n
\u4fbf\u6377\u90e8\u7f72\u4e0e\u7075\u6d3b\u6269\u5c55<\/b><\/strong><\/h2>\n* \u5373\u63d2\u5373\u7528\uff1a \u53ea\u9700\u5c06\u4ea7\u54c1\u9644\u5e26\u7684\u8f6c\u63a5\u5361\u63d2\u5165\u4e3b\u673a\u4efb\u610f\u4e00\u4e2a PCIe x16 \u63d2\u69fd\uff0c\u64cd\u4f5c\u7cfb\u7edf\u5373\u53ef\u81ea\u52a8\u8bc6\u522b\u4e24\u5757\u72ec\u7acb\u7684 V100 \u663e\u5361\uff0c\u65e0\u9700\u989d\u5916\u5b89\u88c5\u8f6c\u63a5\u5361\u9a71\u52a8\uff0c\u90e8\u7f72\u6781\u5176\u7b80\u4fbf\u3002<\/p>\n
* \u5f3a\u5927\u6269\u5c55\u6027\uff1a \u5f97\u76ca\u4e8e\u72ec\u7acb\u7684\u4e3b\u673a\u63a5\u53e3\u8bbe\u8ba1\uff0c\u672c\u4ea7\u54c1\u53ef\u8f7b\u677e\u63a5\u5165\u5177\u5907\u591a\u4e2a PCIe x16 \u63d2\u69fd\uff08\u9700\u6ee1\u8db3\u7269\u7406\u7a7a\u95f4\u548c\u4f9b\u7535\u8981\u6c42\uff09\u7684\u670d\u52a1\u5668\u6216\u5de5\u4f5c\u7ad9\u3002\u7528\u6237\u53ef\u7075\u6d3b\u90e8\u7f72\u591a\u7ec4\u53cc\u5361\u76d2\u5b50\uff0c\u5b9e\u73b0 4\u5361\u30018\u5361 \u751a\u81f3\u66f4\u5927\u89c4\u6a21\u7684\u4e13\u4e1a\u7b97\u529b\u6c60\u6784\u5efa\uff0c\u6ee1\u8db3\u4e0d\u65ad\u589e\u957f\u7684\u8ba1\u7b97\u9700\u6c42\u3002<\/p>\n
\u6574\u4f53\u6027\u80fd<\/b><\/strong><\/h2>\n\n- \u53cc\u5361\u805a\u5408\u7b97\u529b\uff1a \u63d0\u4f9b\u9876\u7ea7\u7684 FP32\/FP64 \u6d6e\u70b9\u6027\u80fd\u548c INT8 \u63a8\u7406\u6027\u80fd\uff0c\u7279\u522b\u9002\u5408 HPC\u3001AI \u8bad\u7ec3\/\u63a8\u7406\u3001CAE \u4eff\u771f\u3001\u79d1\u5b66\u8ba1\u7b97\u7b49\u4e13\u4e1a\u8d1f\u8f7d\u3002<\/li>\n
- \u6781\u81f4\u901a\u4fe1\u5e26\u5bbd\uff1a \u53cc\u5361\u95f4 300 GB\/s \u7684 NVLink \u5e26\u5bbd\uff0c\u662f\u6784\u5efa\u9ad8\u6548\u80fd\u591a GPU \u7cfb\u7edf\u7684\u5173\u952e\uff0c\u663e\u8457\u4f18\u4e8e\u4f9d\u8d56 PCIe \u4e92\u8054\u7684\u65b9\u6848\u3002<\/li>\n
- \u7a33\u5b9a\u53ef\u9760\u8fd0\u884c\uff1a \u4e13\u4e1a\u7ea7\u6c34\u51b7\u6563\u70ed\u548c\u4f18\u8d28\u7535\u6e90\u4fdd\u969c\u53cc\u5361\u957f\u65f6\u95f4\u9ad8\u8d1f\u8f7d\u7a33\u5b9a\u8fd0\u884c\u3002<\/li>\n
- \u9ad8\u5bc6\u5ea6\u7b97\u529b\u90e8\u7f72\uff1a \u6a21\u5757\u5316\u8bbe\u8ba1\u6781\u5927\u8282\u7701\u7a7a\u95f4\uff0c\u7b80\u5316\u5927\u89c4\u6a21\u96c6\u7fa4\u90e8\u7f72\u548c\u7ba1\u7406\u3002<\/li>\n<\/ul>\n
\u4ea7\u54c1\u89c4\u683c<\/b><\/strong><\/h2>\n\n- GPU: 2 x NVIDIA Tesla V100-SXM2-32GB<\/li>\n
- CUDA \u6838\u5fc3: 10240 (2 x 5120)<\/li>\n
- \u663e\u5b58: 64GB HBM2 (2 x 32GB)<\/li>\n
- \u663e\u5b58\u5e26\u5bbd: ~1800 GB\/s \u805a\u5408 (2 x ~900 GB\/s)<\/li>\n
- \u8ba1\u7b97\u6027\u80fd (\u5cf0\u503c):\n
\n- FP32: ~28.2 TFLOPS<\/li>\n
- FP64: ~14.1 TFLOPS<\/li>\n
- INT8: ~224 TOPS<\/li>\n<\/ul>\n<\/li>\n
- \u4e92\u8054\u6280\u672f: NVIDIA NVLink (Gen2), 6 \u901a\u9053<\/li>\n
- \u5361\u95f4\u5e26\u5bbd: 300 GB\/s (\u53cc\u5411)<\/li>\n
- \u4e3b\u673a\u63a5\u53e3: PCIe 3.0 x16 (\u901a\u8fc7 PEX8749 \u62c6\u5206\u4e3a 2x PCIe 3.0 x16 \u8fde\u63a5 GPU)<\/li>\n
- \u6563\u70ed\u65b9\u5f0f: \u5b9a\u5236\u4e00\u4f53\u5f0f\u6c34\u51b7<\/li>\n
- \u7535\u6e90: \u96c6\u6210\u9ad8\u529f\u7387\u9ad8\u6548850W\u7535\u6e90\u6a21\u5757 (\u6ee1\u8db3\u53cc\u5361 TDP 600W+ \u53ca\u7cfb\u7edf\u9700\u6c42)<\/li>\n
- \u5916\u5f62\u5c3a\u5bf8:311mm x 356mm x 180mm<\/li>\n
- \u91cd\u91cf: ~5kg<\/li>\n<\/ul>\n
\u9002\u7528\u573a\u666f<\/b><\/strong><\/h2>\n\n- \u4eba\u5de5\u667a\u80fd\u4e0e\u6df1\u5ea6\u5b66\u4e60\uff1a\n
\n- \u5927\u89c4\u6a21\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u8bad\u7ec3 (\u5c24\u5176\u53d7\u76ca\u4e8e NVLink \u9ad8\u901f\u4e92\u8054\u548c FP16\/FP32 \u6027\u80fd)\u3002<\/li>\n
- \u9ad8\u6027\u80fd AI \u63a8\u7406 (INT8)\u3002<\/li>\n
- \u81ea\u7136\u8bed\u8a00\u5904\u7406 (NLP)\u3001\u8ba1\u7b97\u673a\u89c6\u89c9 (CV) \u7814\u7a76\u4e0e\u5e94\u7528\u3002<\/li>\n<\/ul>\n<\/li>\n
- \u9ad8\u6027\u80fd\u8ba1\u7b97 (HPC)\uff1a\n
\n- \u79d1\u5b66\u8ba1\u7b97\u4e0e\u4eff\u771f (CAE, CFD, FEA – \u5c24\u5176\u53d7\u76ca\u4e8e\u9ad8 FP64 \u6027\u80fd)\u3002<\/li>\n
- \u5206\u5b50\u52a8\u529b\u5b66\u6a21\u62df\u3001\u8ba1\u7b97\u5316\u5b66\u3001\u7269\u7406\u6a21\u62df\u3002<\/li>\n
- \u91d1\u878d\u5efa\u6a21\u4e0e\u98ce\u9669\u5206\u6790\u3002<\/li>\n<\/ul>\n<\/li>\n
- \u4e13\u4e1a\u6e32\u67d3\u4e0e\u865a\u62df\u5316\uff1a\n
\n- GPU \u52a0\u901f\u6e32\u67d3 (V-Ray, Redshift, Octane \u7b49)\u3002<\/li>\n
- Virtual GPU (vGPU) \u5e94\u7528\uff0c\u4e3a\u591a\u4e2a\u7528\u6237\u63d0\u4f9b\u5f3a\u5927\u865a\u62df\u5de5\u4f5c\u7ad9\u3002<\/li>\n<\/ul>\n<\/li>\n
- \u6570\u636e\u4e2d\u5fc3\u4e0e\u4e91\u8ba1\u7b97\uff1a\n
\n- \u6784\u5efa\u9ad8\u5bc6\u5ea6\u3001\u9ad8\u80fd\u6548\u7684 GPU \u8ba1\u7b97\u8282\u70b9\u3002<\/li>\n
- \u63d0\u4f9b\u6309\u9700\u7684\u4e91\u7aef AI\/HPC \u7b97\u529b\u670d\u52a1\u3002<\/li>\n<\/ul>\n<\/li>\n
- \u9700\u8981\u8d85\u8d8a\u6d88\u8d39\u7ea7\u663e\u5361 (\u5982 3090, 4090) \u7684\u4e13\u4e1a\u8ba1\u7b97\u80fd\u529b\u3001\u53cc\u7cbe\u5ea6\u6027\u80fd\u3001\u5927\u663e\u5b58\u5bb9\u91cf\u4ee5\u53ca\u591a\u5361\u9ad8\u901f\u4e92\u8054\u7684\u573a\u666f\u3002<\/li>\n<\/ul>\n
\u786c\u4ef6\u89c4\u683c<\/b><\/strong><\/h2>\nGPU\uff1a 2 \u5757 Tesla V100 16G\/32G<\/p>\n
GPU\u663e\u5b58\uff1a 32G\/64 GB \u7cfb\u7edf\u663e\u5b58\u603b\u5bb9\u91cf<\/p>\n
Nvlink\u901f\u7387\uff1a300G\/s<\/p>\n
\u566a\u97f3: < 40\u00a0dB<\/p>\n
\u91cd\u91cf:\u00a02 Kg<\/p>\n
\u7cfb\u7edf\u5c3a\u5bf8 311mm x 356mm x 180mm<\/p>\n
\u6700\u5927\u529f\u7387\uff1a600 W<\/p>\n
V100\u6838\u5fc3\u8fd0\u884c\u6e29\u5ea6\u8303\u56f4\uff1a37\u201380\u00a0\u00b0C<\/p>\n
\u9002\u7528\u64cd\u4f5c\u7cfb\u7edf \uff1aUbuntu\/Windows<\/p>\n
![\"\"](\"http:\/\/www.ai-cooling.com\/wp-content\/uploads\/2025\/07\/ScreenShot_2026-05-13_140359_107.png\")
<\/p>\n
![\"\"](\"http:\/\/www.ai-cooling.com\/wp-content\/uploads\/2025\/07\/\u5fae\u4fe1\u56fe\u7247_20250604153848.png\")
<\/p>\n
![\"\"](\"http:\/\/www.ai-cooling.com\/wp-content\/uploads\/2025\/07\/\u5fae\u4fe1\u622a\u56fe_20250617100336.jpg\")
<\/p>\n
Tesla V100 Dual Card Water Cooling Graphics Card Dock<\/strong><\/p>\nExternal graphics card with 32G memory and NVLink, meeting all your requirements for V100 dual card NVLink. Lightweight, compact, quiet, and powerful.<\/p>\n
Comparable to 3090-24G and 4090-24G, with the same 10,000 CUDA core configuration, our 32G memory outperforms them.<\/p>\n
Can run various 32B-Q4 large models directly.<\/p>\n
With an additional 16G or higher NVIDIA graphics card in the host, you can run any 70B-Q4 quantization model.<\/p>\n
In stock, ships immediately via SF Express.<\/p>\n
Using the PEX8749 chip, one PCIe 3.0 x16 channel is split into two PCIe 3.0 x16 channels, and then connected to the motherboard via NVLink 6-channel dual-way 25G full-speed connection, with a data transfer rate of 300G between the two V100 cards.<\/p>\n
Dual 240mm radiator water cooling configuration, silent operation, cooling two 300W V100 cards, with a daily operating temperature not exceeding 47\u00b0C and a full-load V100 core temperature not exceeding 60\u00b0C.<\/p>\n
Body size: 40 x 20.5 x 41 cm<\/p>\n
Internal: 2 x 240mm radiators<\/p>\n
Water cooling head: Aluminum alloy heat sink + copper core + acrylic body<\/p>\n
Water pump: High-speed submerged pump<\/p>\n
V100 combination scheme:<\/p>\n
\n- 16G + 16G = 32G<\/li>\n
- 16G + 32G = 48G<\/li>\n
- 32G + 32G = 64G<\/li>\n<\/ol>\n
Introduction:<\/strong><\/p>\nThis is a high-performance computing and AI training platform, built with the latest domestic SXM2 dual-card NVLink expansion scheme. It integrates two NVIDIA Tesla V100-SXM2 16GB professional graphics cards, with a water cooling system, optimized power supply, and structural design, easily handling large-scale AI training, scientific simulations, and other heavy-duty tasks.<\/p>\n
Why choose this product?<\/strong><\/p>\nUltra-powerful computing, professional calculation tool<\/p>\n
Equipped with two Tesla V100 graphics cards, with a total of 10,240 CUDA cores and 640 Tensor cores, designed for scientific computing, deep learning, and high-performance simulations.<\/p>\n
Total memory capacity reaches 32GB high-speed HBM2 memory, with a bandwidth of up to 900GB\/s (single card), far surpassing ordinary graphics cards, easily handling large models and big data.<\/p>\n
High-speed interconnection, double efficiency<\/p>\n
Two graphics cards connected via NVLink 2.0, with a data transfer bandwidth of up to 300 GB\/s (dual-way), far faster than traditional PCIe interfaces, effectively improving multi-card collaboration efficiency.<\/p>\n
Using the advanced PEX8749 chip, one PCIe slot can connect two independent graphics cards, breaking hardware limitations.<\/p>\n
Plug-and-play, easy deployment<\/p>\n
System automatically recognizes the card after insertion, no need for additional drivers, quick deployment.<\/p>\n
Can be flexibly expanded, supporting multiple devices to form 4-card, 8-card, or more GPU computing clusters, adapting to growing business needs.<\/p>\n
Powerful water cooling system, stable operation<\/p>\n
Customized water cooling scheme, combined with efficient power supply, ensuring stable operation of the dual cards under high load.<\/p>\n
Modular design saves space, suitable for data centers or laboratory environments.<\/p>\n
Note:<\/strong><\/p>\n\n- V100 power consumption is 300W, HBM heat generation is huge, and air cooling is difficult to suppress. Water cooling is recommended to avoid damaging the core and HBM memory.<\/li>\n
- Original driver installation guidance is provided, with no driver drop, and a guarantee of lighting up.<\/li>\n
- Water cooling fan, cold cover, NVLink conversion card (non-human damage) are warranted for 6 months.<\/li>\n
- Formal invoice: general invoice + 6%<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"
\u8fd9\u662f\u4e00\u6b3e\u4e13\u4e3a\u9ad8\u6027\u80fd\u8ba1\u7b97\u548cAI\u8bad\u7ec3\u6253\u9020\u7684\u53cc\u5361\u6c34\u51b7\u8ba1\u7b97\u5e73\u53f0\uff0c\u57fa\u4e8e\u56fd\u5185\u6700\u65b0\u7684SXM2\u53cc\u5361NVLink\u6269\u5c55\u65b9\u6848\u3002\u5b83\u96c6\u6210\u4e86\u4e24\u5f20NVIDIA Tesla V100-SXM2 16GB\u4e13\u4e1a\u663e\u5361\uff0c\u914d\u5907\u6c34\u51b7\u7cfb\u7edf\u3001\u4f18\u5316\u7684\u7535\u6e90\u548c\u7ed3\u6784\u8bbe\u8ba1\uff0c\u8f7b\u677e\u5e94\u5bf9\u5927\u89c4\u6a21AI\u8bad\u7ec3\u3001\u79d1\u5b66\u4eff\u771f\u7b49\u91cd\u8f7d\u4efb\u52a1\u3002<\/p>\n","protected":false},"featured_media":222,"template":"","meta":[],"product_brand":[],"product_cat":[21],"product_tag":[],"class_list":["post-132","product","type-product","status-publish","has-post-thumbnail","product_cat-ai","first","instock","shipping-taxable","purchasable","product-type-simple"],"_links":{"self":[{"href":"https:\/\/www.ai-cooling.com\/index.php?rest_route=\/wp\/v2\/product\/132","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.ai-cooling.com\/index.php?rest_route=\/wp\/v2\/product"}],"about":[{"href":"https:\/\/www.ai-cooling.com\/index.php?rest_route=\/wp\/v2\/types\/product"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.ai-cooling.com\/index.php?rest_route=\/wp\/v2\/media\/222"}],"wp:attachment":[{"href":"https:\/\/www.ai-cooling.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=132"}],"wp:term":[{"taxonomy":"product_brand","embeddable":true,"href":"https:\/\/www.ai-cooling.com\/index.php?rest_route=%2Fwp%2Fv2%2Fproduct_brand&post=132"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/www.ai-cooling.com\/index.php?rest_route=%2Fwp%2Fv2%2Fproduct_cat&post=132"},{"taxonomy":"product_tag","embeddable":true,"href":"https:\/\/www.ai-cooling.com\/index.php?rest_route=%2Fwp%2Fv2%2Fproduct_tag&post=132"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\u4fbf\u6377\u90e8\u7f72\u4e0e\u7075\u6d3b\u6269\u5c55<\/b><\/strong><\/h2>\n* \u5373\u63d2\u5373\u7528\uff1a \u53ea\u9700\u5c06\u4ea7\u54c1\u9644\u5e26\u7684\u8f6c\u63a5\u5361\u63d2\u5165\u4e3b\u673a\u4efb\u610f\u4e00\u4e2a PCIe x16 \u63d2\u69fd\uff0c\u64cd\u4f5c\u7cfb\u7edf\u5373\u53ef\u81ea\u52a8\u8bc6\u522b\u4e24\u5757\u72ec\u7acb\u7684 V100 \u663e\u5361\uff0c\u65e0\u9700\u989d\u5916\u5b89\u88c5\u8f6c\u63a5\u5361\u9a71\u52a8\uff0c\u90e8\u7f72\u6781\u5176\u7b80\u4fbf\u3002<\/p>\n
* \u5f3a\u5927\u6269\u5c55\u6027\uff1a \u5f97\u76ca\u4e8e\u72ec\u7acb\u7684\u4e3b\u673a\u63a5\u53e3\u8bbe\u8ba1\uff0c\u672c\u4ea7\u54c1\u53ef\u8f7b\u677e\u63a5\u5165\u5177\u5907\u591a\u4e2a PCIe x16 \u63d2\u69fd\uff08\u9700\u6ee1\u8db3\u7269\u7406\u7a7a\u95f4\u548c\u4f9b\u7535\u8981\u6c42\uff09\u7684\u670d\u52a1\u5668\u6216\u5de5\u4f5c\u7ad9\u3002\u7528\u6237\u53ef\u7075\u6d3b\u90e8\u7f72\u591a\u7ec4\u53cc\u5361\u76d2\u5b50\uff0c\u5b9e\u73b0 4\u5361\u30018\u5361 \u751a\u81f3\u66f4\u5927\u89c4\u6a21\u7684\u4e13\u4e1a\u7b97\u529b\u6c60\u6784\u5efa\uff0c\u6ee1\u8db3\u4e0d\u65ad\u589e\u957f\u7684\u8ba1\u7b97\u9700\u6c42\u3002<\/p>\n
\u6574\u4f53\u6027\u80fd<\/b><\/strong><\/h2>\n\n- \u53cc\u5361\u805a\u5408\u7b97\u529b\uff1a \u63d0\u4f9b\u9876\u7ea7\u7684 FP32\/FP64 \u6d6e\u70b9\u6027\u80fd\u548c INT8 \u63a8\u7406\u6027\u80fd\uff0c\u7279\u522b\u9002\u5408 HPC\u3001AI \u8bad\u7ec3\/\u63a8\u7406\u3001CAE \u4eff\u771f\u3001\u79d1\u5b66\u8ba1\u7b97\u7b49\u4e13\u4e1a\u8d1f\u8f7d\u3002<\/li>\n
- \u6781\u81f4\u901a\u4fe1\u5e26\u5bbd\uff1a \u53cc\u5361\u95f4 300 GB\/s \u7684 NVLink \u5e26\u5bbd\uff0c\u662f\u6784\u5efa\u9ad8\u6548\u80fd\u591a GPU \u7cfb\u7edf\u7684\u5173\u952e\uff0c\u663e\u8457\u4f18\u4e8e\u4f9d\u8d56 PCIe \u4e92\u8054\u7684\u65b9\u6848\u3002<\/li>\n
- \u7a33\u5b9a\u53ef\u9760\u8fd0\u884c\uff1a \u4e13\u4e1a\u7ea7\u6c34\u51b7\u6563\u70ed\u548c\u4f18\u8d28\u7535\u6e90\u4fdd\u969c\u53cc\u5361\u957f\u65f6\u95f4\u9ad8\u8d1f\u8f7d\u7a33\u5b9a\u8fd0\u884c\u3002<\/li>\n
- \u9ad8\u5bc6\u5ea6\u7b97\u529b\u90e8\u7f72\uff1a \u6a21\u5757\u5316\u8bbe\u8ba1\u6781\u5927\u8282\u7701\u7a7a\u95f4\uff0c\u7b80\u5316\u5927\u89c4\u6a21\u96c6\u7fa4\u90e8\u7f72\u548c\u7ba1\u7406\u3002<\/li>\n<\/ul>\n
\u4ea7\u54c1\u89c4\u683c<\/b><\/strong><\/h2>\n\n- GPU: 2 x NVIDIA Tesla V100-SXM2-32GB<\/li>\n
- CUDA \u6838\u5fc3: 10240 (2 x 5120)<\/li>\n
- \u663e\u5b58: 64GB HBM2 (2 x 32GB)<\/li>\n
- \u663e\u5b58\u5e26\u5bbd: ~1800 GB\/s \u805a\u5408 (2 x ~900 GB\/s)<\/li>\n
- \u8ba1\u7b97\u6027\u80fd (\u5cf0\u503c):\n
\n- FP32: ~28.2 TFLOPS<\/li>\n
- FP64: ~14.1 TFLOPS<\/li>\n
- INT8: ~224 TOPS<\/li>\n<\/ul>\n<\/li>\n
- \u4e92\u8054\u6280\u672f: NVIDIA NVLink (Gen2), 6 \u901a\u9053<\/li>\n
- \u5361\u95f4\u5e26\u5bbd: 300 GB\/s (\u53cc\u5411)<\/li>\n
- \u4e3b\u673a\u63a5\u53e3: PCIe 3.0 x16 (\u901a\u8fc7 PEX8749 \u62c6\u5206\u4e3a 2x PCIe 3.0 x16 \u8fde\u63a5 GPU)<\/li>\n
- \u6563\u70ed\u65b9\u5f0f: \u5b9a\u5236\u4e00\u4f53\u5f0f\u6c34\u51b7<\/li>\n
- \u7535\u6e90: \u96c6\u6210\u9ad8\u529f\u7387\u9ad8\u6548850W\u7535\u6e90\u6a21\u5757 (\u6ee1\u8db3\u53cc\u5361 TDP 600W+ \u53ca\u7cfb\u7edf\u9700\u6c42)<\/li>\n
- \u5916\u5f62\u5c3a\u5bf8:311mm x 356mm x 180mm<\/li>\n
- \u91cd\u91cf: ~5kg<\/li>\n<\/ul>\n
\u9002\u7528\u573a\u666f<\/b><\/strong><\/h2>\n\n- \u4eba\u5de5\u667a\u80fd\u4e0e\u6df1\u5ea6\u5b66\u4e60\uff1a\n
\n- \u5927\u89c4\u6a21\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u8bad\u7ec3 (\u5c24\u5176\u53d7\u76ca\u4e8e NVLink \u9ad8\u901f\u4e92\u8054\u548c FP16\/FP32 \u6027\u80fd)\u3002<\/li>\n
- \u9ad8\u6027\u80fd AI \u63a8\u7406 (INT8)\u3002<\/li>\n
- \u81ea\u7136\u8bed\u8a00\u5904\u7406 (NLP)\u3001\u8ba1\u7b97\u673a\u89c6\u89c9 (CV) \u7814\u7a76\u4e0e\u5e94\u7528\u3002<\/li>\n<\/ul>\n<\/li>\n
- \u9ad8\u6027\u80fd\u8ba1\u7b97 (HPC)\uff1a\n
\n- \u79d1\u5b66\u8ba1\u7b97\u4e0e\u4eff\u771f (CAE, CFD, FEA – \u5c24\u5176\u53d7\u76ca\u4e8e\u9ad8 FP64 \u6027\u80fd)\u3002<\/li>\n
- \u5206\u5b50\u52a8\u529b\u5b66\u6a21\u62df\u3001\u8ba1\u7b97\u5316\u5b66\u3001\u7269\u7406\u6a21\u62df\u3002<\/li>\n
- \u91d1\u878d\u5efa\u6a21\u4e0e\u98ce\u9669\u5206\u6790\u3002<\/li>\n<\/ul>\n<\/li>\n
- \u4e13\u4e1a\u6e32\u67d3\u4e0e\u865a\u62df\u5316\uff1a\n
\n- GPU \u52a0\u901f\u6e32\u67d3 (V-Ray, Redshift, Octane \u7b49)\u3002<\/li>\n
- Virtual GPU (vGPU) \u5e94\u7528\uff0c\u4e3a\u591a\u4e2a\u7528\u6237\u63d0\u4f9b\u5f3a\u5927\u865a\u62df\u5de5\u4f5c\u7ad9\u3002<\/li>\n<\/ul>\n<\/li>\n
- \u6570\u636e\u4e2d\u5fc3\u4e0e\u4e91\u8ba1\u7b97\uff1a\n
\n- \u6784\u5efa\u9ad8\u5bc6\u5ea6\u3001\u9ad8\u80fd\u6548\u7684 GPU \u8ba1\u7b97\u8282\u70b9\u3002<\/li>\n
- \u63d0\u4f9b\u6309\u9700\u7684\u4e91\u7aef AI\/HPC \u7b97\u529b\u670d\u52a1\u3002<\/li>\n<\/ul>\n<\/li>\n
- \u9700\u8981\u8d85\u8d8a\u6d88\u8d39\u7ea7\u663e\u5361 (\u5982 3090, 4090) \u7684\u4e13\u4e1a\u8ba1\u7b97\u80fd\u529b\u3001\u53cc\u7cbe\u5ea6\u6027\u80fd\u3001\u5927\u663e\u5b58\u5bb9\u91cf\u4ee5\u53ca\u591a\u5361\u9ad8\u901f\u4e92\u8054\u7684\u573a\u666f\u3002<\/li>\n<\/ul>\n
\u786c\u4ef6\u89c4\u683c<\/b><\/strong><\/h2>\nGPU\uff1a 2 \u5757 Tesla V100 16G\/32G<\/p>\n
GPU\u663e\u5b58\uff1a 32G\/64 GB \u7cfb\u7edf\u663e\u5b58\u603b\u5bb9\u91cf<\/p>\n
Nvlink\u901f\u7387\uff1a300G\/s<\/p>\n
\u566a\u97f3: < 40\u00a0dB<\/p>\n
\u91cd\u91cf:\u00a02 Kg<\/p>\n
\u7cfb\u7edf\u5c3a\u5bf8 311mm x 356mm x 180mm<\/p>\n
\u6700\u5927\u529f\u7387\uff1a600 W<\/p>\n
V100\u6838\u5fc3\u8fd0\u884c\u6e29\u5ea6\u8303\u56f4\uff1a37\u201380\u00a0\u00b0C<\/p>\n
\u9002\u7528\u64cd\u4f5c\u7cfb\u7edf \uff1aUbuntu\/Windows<\/p>\n
<\/p>\n
<\/p>\n
<\/p>\n
Tesla V100 Dual Card Water Cooling Graphics Card Dock<\/strong><\/p>\nExternal graphics card with 32G memory and NVLink, meeting all your requirements for V100 dual card NVLink. Lightweight, compact, quiet, and powerful.<\/p>\n
Comparable to 3090-24G and 4090-24G, with the same 10,000 CUDA core configuration, our 32G memory outperforms them.<\/p>\n
Can run various 32B-Q4 large models directly.<\/p>\n
With an additional 16G or higher NVIDIA graphics card in the host, you can run any 70B-Q4 quantization model.<\/p>\n
In stock, ships immediately via SF Express.<\/p>\n
Using the PEX8749 chip, one PCIe 3.0 x16 channel is split into two PCIe 3.0 x16 channels, and then connected to the motherboard via NVLink 6-channel dual-way 25G full-speed connection, with a data transfer rate of 300G between the two V100 cards.<\/p>\n
Dual 240mm radiator water cooling configuration, silent operation, cooling two 300W V100 cards, with a daily operating temperature not exceeding 47\u00b0C and a full-load V100 core temperature not exceeding 60\u00b0C.<\/p>\n
Body size: 40 x 20.5 x 41 cm<\/p>\n
Internal: 2 x 240mm radiators<\/p>\n
Water cooling head: Aluminum alloy heat sink + copper core + acrylic body<\/p>\n
Water pump: High-speed submerged pump<\/p>\n
V100 combination scheme:<\/p>\n
\n- 16G + 16G = 32G<\/li>\n
- 16G + 32G = 48G<\/li>\n
- 32G + 32G = 64G<\/li>\n<\/ol>\n
Introduction:<\/strong><\/p>\nThis is a high-performance computing and AI training platform, built with the latest domestic SXM2 dual-card NVLink expansion scheme. It integrates two NVIDIA Tesla V100-SXM2 16GB professional graphics cards, with a water cooling system, optimized power supply, and structural design, easily handling large-scale AI training, scientific simulations, and other heavy-duty tasks.<\/p>\n
Why choose this product?<\/strong><\/p>\nUltra-powerful computing, professional calculation tool<\/p>\n
Equipped with two Tesla V100 graphics cards, with a total of 10,240 CUDA cores and 640 Tensor cores, designed for scientific computing, deep learning, and high-performance simulations.<\/p>\n
Total memory capacity reaches 32GB high-speed HBM2 memory, with a bandwidth of up to 900GB\/s (single card), far surpassing ordinary graphics cards, easily handling large models and big data.<\/p>\n
High-speed interconnection, double efficiency<\/p>\n
Two graphics cards connected via NVLink 2.0, with a data transfer bandwidth of up to 300 GB\/s (dual-way), far faster than traditional PCIe interfaces, effectively improving multi-card collaboration efficiency.<\/p>\n
Using the advanced PEX8749 chip, one PCIe slot can connect two independent graphics cards, breaking hardware limitations.<\/p>\n
Plug-and-play, easy deployment<\/p>\n
System automatically recognizes the card after insertion, no need for additional drivers, quick deployment.<\/p>\n
Can be flexibly expanded, supporting multiple devices to form 4-card, 8-card, or more GPU computing clusters, adapting to growing business needs.<\/p>\n
Powerful water cooling system, stable operation<\/p>\n
Customized water cooling scheme, combined with efficient power supply, ensuring stable operation of the dual cards under high load.<\/p>\n
Modular design saves space, suitable for data centers or laboratory environments.<\/p>\n
Note:<\/strong><\/p>\n\n- V100 power consumption is 300W, HBM heat generation is huge, and air cooling is difficult to suppress. Water cooling is recommended to avoid damaging the core and HBM memory.<\/li>\n
- Original driver installation guidance is provided, with no driver drop, and a guarantee of lighting up.<\/li>\n
- Water cooling fan, cold cover, NVLink conversion card (non-human damage) are warranted for 6 months.<\/li>\n
- Formal invoice: general invoice + 6%<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"
\u8fd9\u662f\u4e00\u6b3e\u4e13\u4e3a\u9ad8\u6027\u80fd\u8ba1\u7b97\u548cAI\u8bad\u7ec3\u6253\u9020\u7684\u53cc\u5361\u6c34\u51b7\u8ba1\u7b97\u5e73\u53f0\uff0c\u57fa\u4e8e\u56fd\u5185\u6700\u65b0\u7684SXM2\u53cc\u5361NVLink\u6269\u5c55\u65b9\u6848\u3002\u5b83\u96c6\u6210\u4e86\u4e24\u5f20NVIDIA Tesla V100-SXM2 16GB\u4e13\u4e1a\u663e\u5361\uff0c\u914d\u5907\u6c34\u51b7\u7cfb\u7edf\u3001\u4f18\u5316\u7684\u7535\u6e90\u548c\u7ed3\u6784\u8bbe\u8ba1\uff0c\u8f7b\u677e\u5e94\u5bf9\u5927\u89c4\u6a21AI\u8bad\u7ec3\u3001\u79d1\u5b66\u4eff\u771f\u7b49\u91cd\u8f7d\u4efb\u52a1\u3002<\/p>\n","protected":false},"featured_media":222,"template":"","meta":[],"product_brand":[],"product_cat":[21],"product_tag":[],"class_list":["post-132","product","type-product","status-publish","has-post-thumbnail","product_cat-ai","first","instock","shipping-taxable","purchasable","product-type-simple"],"_links":{"self":[{"href":"https:\/\/www.ai-cooling.com\/index.php?rest_route=\/wp\/v2\/product\/132","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.ai-cooling.com\/index.php?rest_route=\/wp\/v2\/product"}],"about":[{"href":"https:\/\/www.ai-cooling.com\/index.php?rest_route=\/wp\/v2\/types\/product"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.ai-cooling.com\/index.php?rest_route=\/wp\/v2\/media\/222"}],"wp:attachment":[{"href":"https:\/\/www.ai-cooling.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=132"}],"wp:term":[{"taxonomy":"product_brand","embeddable":true,"href":"https:\/\/www.ai-cooling.com\/index.php?rest_route=%2Fwp%2Fv2%2Fproduct_brand&post=132"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/www.ai-cooling.com\/index.php?rest_route=%2Fwp%2Fv2%2Fproduct_cat&post=132"},{"taxonomy":"product_tag","embeddable":true,"href":"https:\/\/www.ai-cooling.com\/index.php?rest_route=%2Fwp%2Fv2%2Fproduct_tag&post=132"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
- \n
- \u53cc\u5361\u805a\u5408\u7b97\u529b\uff1a \u63d0\u4f9b\u9876\u7ea7\u7684 FP32\/FP64 \u6d6e\u70b9\u6027\u80fd\u548c INT8 \u63a8\u7406\u6027\u80fd\uff0c\u7279\u522b\u9002\u5408 HPC\u3001AI \u8bad\u7ec3\/\u63a8\u7406\u3001CAE \u4eff\u771f\u3001\u79d1\u5b66\u8ba1\u7b97\u7b49\u4e13\u4e1a\u8d1f\u8f7d\u3002<\/li>\n
- \u6781\u81f4\u901a\u4fe1\u5e26\u5bbd\uff1a \u53cc\u5361\u95f4 300 GB\/s \u7684 NVLink \u5e26\u5bbd\uff0c\u662f\u6784\u5efa\u9ad8\u6548\u80fd\u591a GPU \u7cfb\u7edf\u7684\u5173\u952e\uff0c\u663e\u8457\u4f18\u4e8e\u4f9d\u8d56 PCIe \u4e92\u8054\u7684\u65b9\u6848\u3002<\/li>\n
- \u7a33\u5b9a\u53ef\u9760\u8fd0\u884c\uff1a \u4e13\u4e1a\u7ea7\u6c34\u51b7\u6563\u70ed\u548c\u4f18\u8d28\u7535\u6e90\u4fdd\u969c\u53cc\u5361\u957f\u65f6\u95f4\u9ad8\u8d1f\u8f7d\u7a33\u5b9a\u8fd0\u884c\u3002<\/li>\n
- \u9ad8\u5bc6\u5ea6\u7b97\u529b\u90e8\u7f72\uff1a \u6a21\u5757\u5316\u8bbe\u8ba1\u6781\u5927\u8282\u7701\u7a7a\u95f4\uff0c\u7b80\u5316\u5927\u89c4\u6a21\u96c6\u7fa4\u90e8\u7f72\u548c\u7ba1\u7406\u3002<\/li>\n<\/ul>\n
\u4ea7\u54c1\u89c4\u683c<\/b><\/strong><\/h2>\n
- \n
- GPU: 2 x NVIDIA Tesla V100-SXM2-32GB<\/li>\n
- CUDA \u6838\u5fc3: 10240 (2 x 5120)<\/li>\n
- \u663e\u5b58: 64GB HBM2 (2 x 32GB)<\/li>\n
- \u663e\u5b58\u5e26\u5bbd: ~1800 GB\/s \u805a\u5408 (2 x ~900 GB\/s)<\/li>\n
- \u8ba1\u7b97\u6027\u80fd (\u5cf0\u503c):\n
- \n
- FP32: ~28.2 TFLOPS<\/li>\n
- FP64: ~14.1 TFLOPS<\/li>\n
- INT8: ~224 TOPS<\/li>\n<\/ul>\n<\/li>\n
- \u4e92\u8054\u6280\u672f: NVIDIA NVLink (Gen2), 6 \u901a\u9053<\/li>\n
- \u5361\u95f4\u5e26\u5bbd: 300 GB\/s (\u53cc\u5411)<\/li>\n
- \u4e3b\u673a\u63a5\u53e3: PCIe 3.0 x16 (\u901a\u8fc7 PEX8749 \u62c6\u5206\u4e3a 2x PCIe 3.0 x16 \u8fde\u63a5 GPU)<\/li>\n
- \u6563\u70ed\u65b9\u5f0f: \u5b9a\u5236\u4e00\u4f53\u5f0f\u6c34\u51b7<\/li>\n
- \u7535\u6e90: \u96c6\u6210\u9ad8\u529f\u7387\u9ad8\u6548850W\u7535\u6e90\u6a21\u5757 (\u6ee1\u8db3\u53cc\u5361 TDP 600W+ \u53ca\u7cfb\u7edf\u9700\u6c42)<\/li>\n
- \u5916\u5f62\u5c3a\u5bf8:311mm x 356mm x 180mm<\/li>\n
- \u91cd\u91cf: ~5kg<\/li>\n<\/ul>\n
\u9002\u7528\u573a\u666f<\/b><\/strong><\/h2>\n
- \n
- \u4eba\u5de5\u667a\u80fd\u4e0e\u6df1\u5ea6\u5b66\u4e60\uff1a\n
- \n
- \u5927\u89c4\u6a21\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u8bad\u7ec3 (\u5c24\u5176\u53d7\u76ca\u4e8e NVLink \u9ad8\u901f\u4e92\u8054\u548c FP16\/FP32 \u6027\u80fd)\u3002<\/li>\n
- \u9ad8\u6027\u80fd AI \u63a8\u7406 (INT8)\u3002<\/li>\n
- \u81ea\u7136\u8bed\u8a00\u5904\u7406 (NLP)\u3001\u8ba1\u7b97\u673a\u89c6\u89c9 (CV) \u7814\u7a76\u4e0e\u5e94\u7528\u3002<\/li>\n<\/ul>\n<\/li>\n
- \u9ad8\u6027\u80fd\u8ba1\u7b97 (HPC)\uff1a\n
- \n
- \u79d1\u5b66\u8ba1\u7b97\u4e0e\u4eff\u771f (CAE, CFD, FEA – \u5c24\u5176\u53d7\u76ca\u4e8e\u9ad8 FP64 \u6027\u80fd)\u3002<\/li>\n
- \u5206\u5b50\u52a8\u529b\u5b66\u6a21\u62df\u3001\u8ba1\u7b97\u5316\u5b66\u3001\u7269\u7406\u6a21\u62df\u3002<\/li>\n
- \u91d1\u878d\u5efa\u6a21\u4e0e\u98ce\u9669\u5206\u6790\u3002<\/li>\n<\/ul>\n<\/li>\n
- \u4e13\u4e1a\u6e32\u67d3\u4e0e\u865a\u62df\u5316\uff1a\n
- \n
- GPU \u52a0\u901f\u6e32\u67d3 (V-Ray, Redshift, Octane \u7b49)\u3002<\/li>\n
- Virtual GPU (vGPU) \u5e94\u7528\uff0c\u4e3a\u591a\u4e2a\u7528\u6237\u63d0\u4f9b\u5f3a\u5927\u865a\u62df\u5de5\u4f5c\u7ad9\u3002<\/li>\n<\/ul>\n<\/li>\n
- \u6570\u636e\u4e2d\u5fc3\u4e0e\u4e91\u8ba1\u7b97\uff1a\n
- \n
- \u6784\u5efa\u9ad8\u5bc6\u5ea6\u3001\u9ad8\u80fd\u6548\u7684 GPU \u8ba1\u7b97\u8282\u70b9\u3002<\/li>\n
- \u63d0\u4f9b\u6309\u9700\u7684\u4e91\u7aef AI\/HPC \u7b97\u529b\u670d\u52a1\u3002<\/li>\n<\/ul>\n<\/li>\n
- \u9700\u8981\u8d85\u8d8a\u6d88\u8d39\u7ea7\u663e\u5361 (\u5982 3090, 4090) \u7684\u4e13\u4e1a\u8ba1\u7b97\u80fd\u529b\u3001\u53cc\u7cbe\u5ea6\u6027\u80fd\u3001\u5927\u663e\u5b58\u5bb9\u91cf\u4ee5\u53ca\u591a\u5361\u9ad8\u901f\u4e92\u8054\u7684\u573a\u666f\u3002<\/li>\n<\/ul>\n
\u786c\u4ef6\u89c4\u683c<\/b><\/strong><\/h2>\n
GPU\uff1a 2 \u5757 Tesla V100 16G\/32G<\/p>\n
GPU\u663e\u5b58\uff1a 32G\/64 GB \u7cfb\u7edf\u663e\u5b58\u603b\u5bb9\u91cf<\/p>\n
Nvlink\u901f\u7387\uff1a300G\/s<\/p>\n
\u566a\u97f3: < 40\u00a0dB<\/p>\n
\u91cd\u91cf:\u00a02 Kg<\/p>\n
\u7cfb\u7edf\u5c3a\u5bf8 311mm x 356mm x 180mm<\/p>\n
\u6700\u5927\u529f\u7387\uff1a600 W<\/p>\n
V100\u6838\u5fc3\u8fd0\u884c\u6e29\u5ea6\u8303\u56f4\uff1a37\u201380\u00a0\u00b0C<\/p>\n
\u9002\u7528\u64cd\u4f5c\u7cfb\u7edf \uff1aUbuntu\/Windows<\/p>\n
<\/p>\n<\/p>\n<\/p>\nTesla V100 Dual Card Water Cooling Graphics Card Dock<\/strong><\/p>\n
External graphics card with 32G memory and NVLink, meeting all your requirements for V100 dual card NVLink. Lightweight, compact, quiet, and powerful.<\/p>\n
Comparable to 3090-24G and 4090-24G, with the same 10,000 CUDA core configuration, our 32G memory outperforms them.<\/p>\n
Can run various 32B-Q4 large models directly.<\/p>\n
With an additional 16G or higher NVIDIA graphics card in the host, you can run any 70B-Q4 quantization model.<\/p>\n
In stock, ships immediately via SF Express.<\/p>\n
Using the PEX8749 chip, one PCIe 3.0 x16 channel is split into two PCIe 3.0 x16 channels, and then connected to the motherboard via NVLink 6-channel dual-way 25G full-speed connection, with a data transfer rate of 300G between the two V100 cards.<\/p>\n
Dual 240mm radiator water cooling configuration, silent operation, cooling two 300W V100 cards, with a daily operating temperature not exceeding 47\u00b0C and a full-load V100 core temperature not exceeding 60\u00b0C.<\/p>\n
Body size: 40 x 20.5 x 41 cm<\/p>\n
Internal: 2 x 240mm radiators<\/p>\n
Water cooling head: Aluminum alloy heat sink + copper core + acrylic body<\/p>\n
Water pump: High-speed submerged pump<\/p>\n
V100 combination scheme:<\/p>\n
- \n
- 16G + 16G = 32G<\/li>\n
- 16G + 32G = 48G<\/li>\n
- 32G + 32G = 64G<\/li>\n<\/ol>\n
Introduction:<\/strong><\/p>\n
This is a high-performance computing and AI training platform, built with the latest domestic SXM2 dual-card NVLink expansion scheme. It integrates two NVIDIA Tesla V100-SXM2 16GB professional graphics cards, with a water cooling system, optimized power supply, and structural design, easily handling large-scale AI training, scientific simulations, and other heavy-duty tasks.<\/p>\n
Why choose this product?<\/strong><\/p>\n
Ultra-powerful computing, professional calculation tool<\/p>\n
Equipped with two Tesla V100 graphics cards, with a total of 10,240 CUDA cores and 640 Tensor cores, designed for scientific computing, deep learning, and high-performance simulations.<\/p>\n
Total memory capacity reaches 32GB high-speed HBM2 memory, with a bandwidth of up to 900GB\/s (single card), far surpassing ordinary graphics cards, easily handling large models and big data.<\/p>\n
High-speed interconnection, double efficiency<\/p>\n
Two graphics cards connected via NVLink 2.0, with a data transfer bandwidth of up to 300 GB\/s (dual-way), far faster than traditional PCIe interfaces, effectively improving multi-card collaboration efficiency.<\/p>\n
Using the advanced PEX8749 chip, one PCIe slot can connect two independent graphics cards, breaking hardware limitations.<\/p>\n
Plug-and-play, easy deployment<\/p>\n
System automatically recognizes the card after insertion, no need for additional drivers, quick deployment.<\/p>\n
Can be flexibly expanded, supporting multiple devices to form 4-card, 8-card, or more GPU computing clusters, adapting to growing business needs.<\/p>\n
Powerful water cooling system, stable operation<\/p>\n
Customized water cooling scheme, combined with efficient power supply, ensuring stable operation of the dual cards under high load.<\/p>\n
Modular design saves space, suitable for data centers or laboratory environments.<\/p>\n
Note:<\/strong><\/p>\n
- \n
- V100 power consumption is 300W, HBM heat generation is huge, and air cooling is difficult to suppress. Water cooling is recommended to avoid damaging the core and HBM memory.<\/li>\n
- Original driver installation guidance is provided, with no driver drop, and a guarantee of lighting up.<\/li>\n
- Water cooling fan, cold cover, NVLink conversion card (non-human damage) are warranted for 6 months.<\/li>\n
- Formal invoice: general invoice + 6%<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"
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