4卡96GB显存暴力输出！锐炫Pro B60和长城世恒X-AIGC工作站评测：可满足千人同时在线聊天

Ofweek光电信息网02-12

一、前言：当前最具性价比的96GB/192GB AI推理卡凭借深耕多年的CUDA护城河，NVIDIA在AI领域一度拥有“定价权”，这也让这家公司的GPU及相关产品的售价逐渐脱离普通的消费者。然而，随着硬件巨头Intel向“全栈AI公司”快速转型，这种绝对垄断正在被打破。早在2019年，Intel就发布了oneAPI 跨架构编程模型，旨在让代码在 CPU、GPU、NPU 之间通用。这意味着开发者用...

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href=http://gu.qq.com/resources/shy/news/detail-v2/index.html#/?id=nesSN20260212121445a49aba54&s=b><strong>Ofweek光电信息网</strong></a>\n\n\n</h4>\n\n</header>\n<article>\n<div>\n<p>一、前言：当前最具性价比的96GB/192GB AI推理卡凭借深耕多年的CUDA护城河，NVIDIA在AI领域一度拥有“定价权”，这也让这家公司的GPU及相关产品的售价逐渐脱离普通的消费者。然而，随着硬件巨头Intel向“全栈AI公司”快速转型，这种绝对垄断正在被打破。早在2019年，Intel就发布了oneAPI 跨架构编程模型，旨在让代码在 CPU、GPU、NPU 之间通用。这意味着开发者用...</p>\n\n<a href=\"http://gu.qq.com/resources/shy/news/detail-v2/index.html#/?id=nesSN20260212121445a49aba54&s=b\">网页链接</a>\n\n</div>\n\n\n</article>\n</div>\n</body>\n</html>\n","isBrief":false,"type":0,"news_type":1,"symbol":"LU0708994859.HKD","symbol_name":"TEMPLETON GLOBAL \"A\" (HKD) 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Ruixuan Pro B60 and Great Wall Shiheng X-AIGC workstation review: can satisfy thousands of people chatting online at the same time","themeId":null,"isJumpTheme":false,"ttsUrl":null,"symbols_score_info":{"NVDA":1.87,"NVD3.UK":0.6,"AI":0.78,"NVDS.UK":0.6,"NVD":0.6,"NVDY":0.6,"NVDD":0.6,"INTC":1.95,"NVDS":0.6,"NVDU":0.6,"2NVD.UK":0.6,"04335":1.95,"NVIW.SI":0.6,"SNVD.UK":0.6,"NVDX":0.6,"NVDG":0.6,"NVDL":0.6,"RTX":0.7,"NVDW":0.6,"NVDQ":0.6,"NVD2.UK":0.6,"3NVD.UK":0.6,"DIPS":0.6,"NVII":0.6},"content_text":"一、前言：当前最具性价比的96GB/192GB AI推理卡凭借深耕多年的CUDA护城河，NVIDIA在AI领域一度拥有“定价权”，这也让这家公司的GPU及相关产品的售价逐渐脱离普通的消费者。然而，随着硬件巨头Intel向“全栈AI公司”快速转型，这种绝对垄断正在被打破。早在2019年，Intel就发布了oneAPI 跨架构编程模型，旨在让代码在 CPU、GPU、NPU 之间通用。这意味着开发者用一套代码即可调用 Intel 的所有算力，降低了迁移成本。oneAPI还允许开发者将原本仅能NVIDIA CUDA环境下运行的代码，通过其迁移工具（SYCLomatic）快速转换到Intel硬件上，为Arc系列显卡运行主流大模型打下了坚实的软件基础。去年，Intel发布了基于第二代Xe2架构（Battlemage）的专业级显卡—Intel Arc Pro B60。随后，以Maxsun（铭瑄）、SPARKLE（撼与）、GUNNIR（蓝戟）为代表的核心伙伴正式将其推向全球市场，直指高性能AI推理领域。Intel Arc Pro B60与此前发布的消费级Intel Arc B580一样，都采用了完整的BMG-G21 GPU 核心， 拥有20个Xe2核心，2560个FP32单元（也可以说是2560个流处理器），20个光追单元和160个XXM AI引擎。每颗BMG-G21 GPU可提供12.28 TFLOPS的FP32浮点性能以及197 TOPS的INT8 AI性能。在显存方面，Intel Arc Pro B60设计了192bit位宽、19Gbps GDDR6显存，显存带宽高达456GB/s，显存容量则从Intel Arc B580的12GB直接翻倍到了24GB。与更贵的NVIDIA RTX Pro 2000相比，Intel Arc Pro B60不论是显存容量还是显存带宽都比对手高出了50%。而在大模型推理中，显存容量决定了模型的参数上限，带宽则决定了吐字速度。相比之下，NVIDIA同样显存规格的AI加速卡，售价往往是Arc Pro B60的3至4倍。随着DeepSeek等大规模 MoE 模型爆发的，Intel Arc Pro B60成为了目前市面上构建 96GB（4卡） 到 192GB（8卡）超大显存池最具性价比的方案。此次我们收到了来自于长城的世恒X-AIGC工作站，这台主机搭载了Intel Xeon w5-3435X处理器、256GB（4x64GB）DDR5 ECC 5600MHz内存、2600W金牌电源以及4张Intel Arc Pro B60 24GB显卡，共计96GB显存。二、英特尔锐炫Pro B60和长城世恒X-AIGC工作站图赏长城世恒X图形工作站包含4张Arc Pro B60显卡，不过为了运输安全，发货时，显卡会单独包装，并不会直接安装在主机里面。Arc Pro B60 24GB公版显卡正面照，双槽厚度，配备一个涡轮风扇。显卡背面有全尺寸的金属背板，大量的片式聚合物电容也放在背面。3个DP 2.1和一个HDMI 2.1接口。2个8Pin供电接口放在了显卡尾端。长城世恒X图形工作站。超大的水冷头与水泵采用了分离设计，水泵在其中一根水冷管上。将4张Arc Pro B60安装进去。长城世恒X图形工作站与4张Arc Pro B60合体。三、GPT-OSS-120B MXFP4多并发性能测试：能满足千人同时在线聊天GPT-OSS-120B是OpenAI首个开源的千亿级参数模型，总参数 1170 亿 (117B)，被认为是测试多卡并联（Multi-GPU Parallelism）和分布式计算性能的标杆。我们将GPT-OSS-120B模型下载至容器的llmmodels目录下，并开启vLLM OpenAI API服务，具体参数如下：由于120b占用显卡超过66GB，只能4卡并联进行测试，推理精度bfloat16，单次批处理的最大Token总数为 8192，最大上下文长度（Token 数）为 3000，GPU 显存利用率上限为 90%（预留10%给系统），使用MXFP4（混合精度 FP4）进行量化压缩。vLLM OpenAI API 服务已经成功启动，下面单开一个窗口进行测试。vLLM版本是最新的0.5.0，无法使用过去的benchmark_serving.py脚本进行测试，因此我们直接使用vllm bench serve命令，分别测试并发数1,10,20,30,40,50,60,70,80,90,100时的AI性能。这是并发数为1的测试成绩日志，请求成功率 100%，在处理 120B 这种超大规模模型时，100 个请求全部成功且无一报错，说明4卡 Arc Pro B60 + MXFP4运行测试时非常稳定。TTFT (首字延迟)仅为91.37ms，说明Arc Pro B60预填充（Prefill）阶段的爆发力极强。平均 ITL (逐词延迟) 为 32.01 ms，输出吞吐量则为184tok/s。下面是并发数从1,10,20,30,40,50,60,70,80,90,100的性能变化。当并发数从1~10时： 系统的吞吐量呈现指数级增长，从 184 飙升至 613 tok/s。不过并发数达到60之后，吞吐量为701 tok/s，基本上已经达到了这套系统的极限，即便请求数增加到100之后，总吞吐量也就增加了1%左右。整个测试期间，ITL (逐词延迟)稳定得出奇，在达到并发 30 后，ITL 甚至随着并发增加而轻微下降。也就是说在高负载下，计算核心被填充得更满，单步推理的效率反而由于批处理效应而略微提升。TTFT (首字延迟)震动比较剧烈，并发数为1时仅有91ms，并发数20时为241ns，并发数100时已经到了1344ms。对于大多数用户而言，10 tok/s即可拥有丝滑的访问体验，根据长城世恒X图形工作站700tok/s的极限性能计算，它可以承受70个用户同时请求回答。再按1:15的活跃比计算，这台工作站可以支持1000人同时在线聊天。四、Llama-3.1-8B测试：比同价位RTX Pro 2000 16GB要快50%1、Llama-3.1-8BLlama-3.1-8B的显存需求只有7GB左右，因此不仅可以对Arc Pro B60 24GB进行单卡、双卡、4卡测试，我们还能测试桌面版RTX 5060 Ti 16GB的推理性能并与之进行对比！vLLM 0.5.0 正式建立了对 Intel Arc GPU的原生支持，不再是以往那种简单的代码迁移，而是针对 Intel 的计算单元架构做了适配：在并发数下，RTX 5060 Ti 16GB的性能略胜于单卡Arc Pro B60 24GB，但随着并发数的提升，Arc Pro B60 24GB随着并发数的提升，Arc Pro B60 24GB凭借大显存的优势开始逆袭，并发数90时，可以领先RTX 5060 Ti 16GB约10%左右。4张Arc Pro B60 24GB在低并发数时性能优势并不明显，但随着并发数的提升，特别是达到100并发后，4张Arc Pro B60 24GB的性能几乎是单卡的4倍。下面将精度降为FP8，重复上面的测试，但将N卡换成RTX Pro 2000。与同价位的NVIDIA RTX Pro 2000 16GB相比，Arc Pro B60 24GB几乎展现出了碾压性优势。同样是4卡并行进行运算，4xArc Pro B60 24GB比起4xRTX Pro 2000 16GB要强了50%左右，在并发数为100的情况下，凭借96GB大显存，Intel的领先幅度甚至达到了65%。五、小结：用入门级N卡的价钱 买了接近旗舰级N卡的显存容量和推理性能凭借CUDA生态的支持，NVIDIA的GPU在特定的生产力与AI方面的确有无可比拟的性能优势。但是在大模型时代，显存即正义。96GB显存意味着你可以本地运行参数量更大的模型，比如千亿级参数GPT-OSS-120B、LLaMA-3-130B对于4卡Arc Pro B60 24GB完全不是问题。同样价位的NVIDIA RTX Pro 2000 16GB，4卡合计64GB显存，这是一个相对尴尬的显存容量，只能运行70B模型。在面对千亿级模型时必须极致量化压缩显存，且仅支持短上下文低负载推理，完全无法支持训练和微调。而在性能方面，Arc Pro B60 24GB几乎展现出了碾压性优势。同样是4卡并行运算（Llama-3.1-8B-Instruct FP8），4xArc Pro B60 24GB比起4xRTX Pro 2000 16GB要强了50%左右，在并发数为100的情况下，凭借96GB大显存，高负载（Batch 100）下，Intel 方案达到了 2110 Tokens/s，而同样价位的NVIDIA方案仅为 1279 Tokens/s。Intel的领先幅度超过了65%。在运行1200亿参数的GPT-OSS-120B时，Arc Pro B60 24GB在预填充（Prefill）阶段展现出了极强的爆发力，并发数为1的时候，Mean TFT (首字延迟)仅为91.37ms。当并发数达到60之后，4张Arc Pro B60 24GB的吞吐量超过了701 tok/s，能满足千人同时在线聊天。对于想要组建高性能本地 LLM 推理站的企业而言，5000元的Arc Pro B60 24GB显然是更具性价比的选择。要知道NVIDIA类型算力的24GB专业卡，其售价几乎是Arc Pro B60 24GB的4倍左右。很简单的结论：Arc Pro B60 24GB能让用户以入门级N卡的价钱，买了接近旗舰级N卡的显存容量和推理性能。","kind":"news","is_publish_news":true,"is_publish_highlight":false,"is_publish_live":false,"is_publish_wemedia":null,"editions":null,"column":"","sentiment":"0","news_tag":"","news_rank":0,"symbols":[],"gpt_button":1,"need_auth":false,"code":"91000000","status":"200"},"commentList":[],"isCommentEnd":true,"newsSizeData":{"likeSize":0,"commentSize":0,"repostSize":0,"favoriteSize":0,"likeStatus":false,"favoriteStatus":false},"APP":{"userAgent":"Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; 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