{"id":8607,"date":"2026-04-24T11:48:07","date_gmt":"2026-04-24T08:48:07","guid":{"rendered":"https:\/\/unihost.com\/blog\/?p=8607"},"modified":"2026-04-24T11:58:17","modified_gmt":"2026-04-24T08:58:17","slug":"best-gpu-servers-for-machine-learning-in-2026","status":"publish","type":"post","link":"https:\/\/unihost.com\/blog\/uk\/best-gpu-servers-for-machine-learning-in-2026\/","title":{"rendered":"\u041d\u0430\u0439\u043a\u0440\u0430\u0449\u0456 GPU-\u0441\u0435\u0440\u0432\u0435\u0440\u0438 \u0434\u043b\u044f \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043d\u0430\u0432\u0447\u0430\u043d\u043d\u044f"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">\u042f\u043a\u0449\u043e \u0432\u0438 \u0432\u0436\u0435 \u0437\u043d\u0430\u0454\u0442\u0435, \u0449\u043e \u0432\u0430\u043c \u043f\u043e\u0442\u0440\u0456\u0431\u0435\u043d GPU-\u0441\u0435\u0440\u0432\u0435\u0440 \u0434\u043b\u044f ML &#8211; \u043f\u043e\u0447\u0438\u043d\u0430\u0439\u0442\u0435 \u0437 \u0442\u0430\u0431\u043b\u0438\u0446\u0456 \u043d\u0438\u0436\u0447\u0435. \u042f\u043a\u0449\u043e \u0449\u0435 \u0441\u0443\u043c\u043d\u0456\u0432\u0430\u0454\u0442\u0435\u0441\u044f \u043c\u0456\u0436 CPU \u0456 GPU \u0430\u0431\u043e \u043d\u0435 \u0437\u043d\u0430\u0454\u0442\u0435, \u044f\u043a\u0430 \u043a\u043e\u043d\u0444\u0456\u0433\u0443\u0440\u0430\u0446\u0456\u044f \u043f\u0456\u0434\u0456\u0439\u0434\u0435 &#8211; \u0447\u0438\u0442\u0430\u0439\u0442\u0435 \u0434\u0430\u043b\u0456.<\/span><\/p>\n<p>&nbsp;<\/p>\n<h2><b>\u0428\u0432\u0438\u0434\u043a\u0438\u0439 \u0432\u0438\u0431\u0456\u0440: \u044f\u043a\u0430 \u043a\u043e\u043d\u0444\u0456\u0433\u0443\u0440\u0430\u0446\u0456\u044f \u0432\u0430\u043c \u043f\u043e\u0442\u0440\u0456\u0431\u043d\u0430<\/b><\/h2>\n<table>\n<tbody>\n<tr>\n<td><b>\u0412\u0430\u0448\u0435 \u0437\u0430\u0432\u0434\u0430\u043d\u043d\u044f<\/b><\/td>\n<td><b>\u041c\u0456\u043d\u0456\u043c\u0430\u043b\u044c\u043d\u0430 \u043a\u043e\u043d\u0444\u0456\u0433\u0443\u0440\u0430\u0446\u0456\u044f<\/b><\/td>\n<td><b>\u041e\u043f\u0442\u0438\u043c\u0430\u043b\u044c\u043d\u0430 \u043a\u043e\u043d\u0444\u0456\u0433\u0443\u0440\u0430\u0446\u0456\u044f<\/b><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">\u041f\u0440\u043e\u0442\u043e\u0442\u0438\u043f, \u043d\u0430\u0432\u0447\u0430\u043d\u043d\u044f \u043d\u0430 \u043c\u0430\u043b\u0438\u0445 \u0434\u0430\u0442\u0430\u0441\u0435\u0442\u0430\u0445<\/span><\/td>\n<td><span style=\"font-weight: 400;\">1x RTX 4090 (24 \u0413\u0411)<\/span><\/td>\n<td><span style=\"font-weight: 400;\">2x RTX 4090 (48 \u0413\u0411)<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">\u0424\u0430\u0439\u043d-\u0442\u044e\u043d\u0456\u043d\u0433 7B-13B \u043c\u043e\u0434\u0435\u043b\u0435\u0439 (LoRA\/QLoRA)<\/span><\/td>\n<td><span style=\"font-weight: 400;\">1x A100 40GB<\/span><\/td>\n<td><span style=\"font-weight: 400;\">2x A100 80GB<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">\u0424\u0430\u0439\u043d-\u0442\u044e\u043d\u0456\u043d\u0433 30B-70B \u043c\u043e\u0434\u0435\u043b\u0435\u0439<\/span><\/td>\n<td><span style=\"font-weight: 400;\">4x A100 80GB<\/span><\/td>\n<td><span style=\"font-weight: 400;\">4x H100 80GB<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">\u0422\u0440\u0435\u043d\u0443\u0432\u0430\u043d\u043d\u044f 7B-30B \u0437 \u043d\u0443\u043b\u044f<\/span><\/td>\n<td><span style=\"font-weight: 400;\">4x A100 80GB + NVLink<\/span><\/td>\n<td><span style=\"font-weight: 400;\">8x A100 80GB + NVLink<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">\u0422\u0440\u0435\u043d\u0443\u0432\u0430\u043d\u043d\u044f 70B+ \/ foundation models<\/span><\/td>\n<td><span style=\"font-weight: 400;\">8x H100 80GB + InfiniBand<\/span><\/td>\n<td><span style=\"font-weight: 400;\">8x H200 141GB + InfiniBand<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">\u041f\u0440\u043e\u0434\u0430\u043a\u0448\u043d LLM-\u0456\u043d\u0444\u0435\u0440\u0435\u043d\u0441<\/span><\/td>\n<td><span style=\"font-weight: 400;\">2x A100 40GB<\/span><\/td>\n<td><span style=\"font-weight: 400;\">4x A100 80GB \u0430\u0431\u043e 2x H100<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">\u041a\u043e\u043c\u043f&#8217;\u044e\u0442\u0435\u0440\u043d\u0438\u0439 \u0437\u0456\u0440 (real-time)<\/span><\/td>\n<td><span style=\"font-weight: 400;\">1x RTX 4090<\/span><\/td>\n<td><span style=\"font-weight: 400;\">2-4x A100 40GB<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Embedding-\u0433\u0435\u043d\u0435\u0440\u0430\u0446\u0456\u044f (\u0432\u0435\u043b\u0438\u043a\u0438\u0439 \u043e\u0431\u0441\u044f\u0433)<\/span><\/td>\n<td><span style=\"font-weight: 400;\">1x A100 40GB<\/span><\/td>\n<td><span style=\"font-weight: 400;\">2x A100 80GB<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><span style=\"font-weight: 400;\">\u042f\u043a\u0449\u043e \u0432\u0430\u0448\u0435 \u0437\u0430\u0432\u0434\u0430\u043d\u043d\u044f \u0454 \u0432 \u0442\u0430\u0431\u043b\u0438\u0446\u0456 &#8211; \u043a\u043e\u043d\u0444\u0456\u0433\u0443\u0440\u0430\u0446\u0456\u044f \u0432\u0438\u0437\u043d\u0430\u0447\u0435\u043d\u0430. \u042f\u043a\u0449\u043e \u043d\u0456 &#8211; \u0447\u0438\u0442\u0430\u0439\u0442\u0435 \u0441\u0446\u0435\u043d\u0430\u0440\u0456\u0457 \u043d\u0438\u0436\u0447\u0435, \u0432\u043e\u043d\u0438 \u043e\u0445\u043e\u043f\u043b\u044e\u044e\u0442\u044c \u043d\u0435\u0442\u0438\u043f\u043e\u0432\u0456 \u043a\u0435\u0439\u0441\u0438.<\/span><\/p>\n<p>&nbsp;<\/p>\n<h2><b>\u0427\u043e\u043c\u0443 GPU, \u0430 \u043d\u0435 CPU \u0434\u043b\u044f ML<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">\u0422\u0440\u0435\u043d\u0443\u0432\u0430\u043d\u043d\u044f \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u043e\u0457 \u043c\u0435\u0440\u0435\u0436\u0456 &#8211; \u0446\u0435 \u043c\u0456\u043b\u044c\u044f\u0440\u0434\u0438 \u043e\u043f\u0435\u0440\u0430\u0446\u0456\u0439 \u043c\u043d\u043e\u0436\u0435\u043d\u043d\u044f \u043c\u0430\u0442\u0440\u0438\u0446\u044c, \u0449\u043e \u0432\u0438\u043a\u043e\u043d\u0443\u044e\u0442\u044c\u0441\u044f \u043f\u043e\u0441\u043b\u0456\u0434\u043e\u0432\u043d\u043e \u0437\u0430 \u0435\u043f\u043e\u0445\u0430\u043c\u0438. CPU \u043c\u0430\u0454 8-128 \u043f\u043e\u0442\u0443\u0436\u043d\u0438\u0445 \u044f\u0434\u0435\u0440 \u0434\u043b\u044f \u043f\u043e\u0441\u043b\u0456\u0434\u043e\u0432\u043d\u0438\u0445 \u0437\u0430\u0434\u0430\u0447. GPU \u043c\u0430\u0454 6 000-18 000+ \u043f\u0440\u043e\u0441\u0442\u0438\u0445 \u044f\u0434\u0435\u0440 CUDA, \u044f\u043a\u0456 \u0432\u0438\u043a\u043e\u043d\u0443\u044e\u0442\u044c \u0446\u0456 \u043e\u043f\u0435\u0440\u0430\u0446\u0456\u0457 \u043f\u0430\u0440\u0430\u043b\u0435\u043b\u044c\u043d\u043e. \u0420\u0456\u0437\u043d\u0438\u0446\u044f \u0434\u043b\u044f ML-\u0437\u0430\u0434\u0430\u0447 &#8211; \u0432\u0456\u0434 10x \u0434\u043e 100x \u043d\u0430 \u043a\u043e\u0440\u0438\u0441\u0442\u044c GPU.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u041a\u043e\u043d\u043a\u0440\u0435\u0442\u043d\u043e: \u0442\u0440\u0435\u043d\u0443\u0432\u0430\u043d\u043d\u044f BERT-large (340M \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u0456\u0432) \u043d\u0430 \u043e\u0434\u043d\u043e\u043c\u0443 CPU (32 \u044f\u0434\u0440\u0430, Xeon) \u0437\u0430\u0439\u043c\u0430\u0454 ~72 \u0433\u043e\u0434\u0438\u043d\u0438. \u041d\u0430 \u043e\u0434\u043d\u043e\u043c\u0443 A100 80GB &#8211; ~4 \u0433\u043e\u0434\u0438\u043d\u0438. \u041d\u0430 4x A100 &#8211; \u043c\u0435\u043d\u0448\u0435 \u0433\u043e\u0434\u0438\u043d\u0438. CPU \u043d\u0435 \u043f\u0440\u043e\u0441\u0442\u043e \u043f\u043e\u0432\u0456\u043b\u044c\u043d\u0456\u0448\u0438\u0439 &#8211; \u0432\u0456\u043d \u0440\u043e\u0431\u0438\u0442\u044c \u0442\u0440\u0435\u043d\u0443\u0432\u0430\u043d\u043d\u044f \u0432\u0435\u043b\u0438\u043a\u0438\u0445 \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u043f\u0440\u0430\u043a\u0442\u0438\u0447\u043d\u043e \u043d\u0435\u0440\u0435\u0430\u043b\u044c\u043d\u0438\u043c.<\/span><\/p>\n<p>&nbsp;<\/p>\n<table>\n<tbody>\n<tr>\n<td><b>\u0417\u0430\u0434\u0430\u0447\u0430<\/b><\/td>\n<td><b>CPU<\/b><\/td>\n<td><b>GPU (A100)<\/b><\/td>\n<td><b>\u041f\u0440\u0438\u0441\u043a\u043e\u0440\u0435\u043d\u043d\u044f<\/b><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">BERT-large \u0442\u0440\u0435\u043d\u0443\u0432\u0430\u043d\u043d\u044f (1 \u0435\u043f\u043e\u0445\u0430)<\/span><\/td>\n<td><span style=\"font-weight: 400;\">~72 \u0433\u043e\u0434<\/span><\/td>\n<td><span style=\"font-weight: 400;\">~4 \u0433\u043e\u0434<\/span><\/td>\n<td><span style=\"font-weight: 400;\">~18x<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">GPT-2 (1.5B) inference, 1 \u0437\u0430\u043f\u0438\u0442<\/span><\/td>\n<td><span style=\"font-weight: 400;\">~8 \u0441\u0435\u043a<\/span><\/td>\n<td><span style=\"font-weight: 400;\">~0.1 \u0441\u0435\u043a<\/span><\/td>\n<td><span style=\"font-weight: 400;\">~80x<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">ResNet-50 training (ImageNet)<\/span><\/td>\n<td><span style=\"font-weight: 400;\">~10 \u0434\u043d\u0456\u0432<\/span><\/td>\n<td><span style=\"font-weight: 400;\">~12 \u0433\u043e\u0434<\/span><\/td>\n<td><span style=\"font-weight: 400;\">~20x<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Embedding-\u0433\u0435\u043d\u0435\u0440\u0430\u0446\u0456\u044f (1M \u0432\u0435\u043a\u0442\u043e\u0440\u0456\u0432)<\/span><\/td>\n<td><span style=\"font-weight: 400;\">~2 \u0433\u043e\u0434<\/span><\/td>\n<td><span style=\"font-weight: 400;\">~3 \u0445\u0432<\/span><\/td>\n<td><span style=\"font-weight: 400;\">~40x<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<h2><b>\u0429\u043e \u0442\u0430\u043a\u0435 GPU-\u0441\u0435\u0440\u0432\u0435\u0440 \u0434\u043b\u044f ML<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">GPU-\u0441\u0435\u0440\u0432\u0435\u0440 \u0434\u043b\u044f machine learning &#8211; \u0446\u0435 \u0432\u0438\u0434\u0456\u043b\u0435\u043d\u0438\u0439 bare-metal \u0441\u0435\u0440\u0432\u0435\u0440 \u0456\u0437 \u043e\u0434\u043d\u0438\u043c \u0430\u0431\u043e \u043a\u0456\u043b\u044c\u043a\u043e\u043c\u0430 GPU, \u043e\u043f\u0442\u0438\u043c\u0456\u0437\u043e\u0432\u0430\u043d\u0438\u0439 \u0434\u043b\u044f \u043e\u0431\u0447\u0438\u0441\u043b\u044e\u0432\u0430\u043b\u044c\u043d\u043e \u0456\u043d\u0442\u0435\u043d\u0441\u0438\u0432\u043d\u0438\u0445 ML-\u043d\u0430\u0432\u0430\u043d\u0442\u0430\u0436\u0435\u043d\u044c. \u0412\u0456\u0434 \u0437\u0432\u0438\u0447\u0430\u0439\u043d\u043e\u0433\u043e GPU-\u0441\u0435\u0440\u0432\u0435\u0440\u0430 \u0432\u0456\u043d \u0432\u0456\u0434\u0440\u0456\u0437\u043d\u044f\u0454\u0442\u044c\u0441\u044f \u0441\u043f\u0435\u0446\u0438\u0444\u0456\u0447\u043d\u0438\u043c \u0441\u0442\u0435\u043a\u043e\u043c: \u0434\u043e\u0441\u0442\u0430\u0442\u043d\u0456\u0439 VRAM \u0434\u043b\u044f \u043c\u043e\u0434\u0435\u043b\u0456, NVLink \u0430\u0431\u043e NVSwitch \u0434\u043b\u044f \u043c\u0456\u0436\u0433\u0440\u0443\u043f\u043e\u0432\u043e\u0457 \u043a\u043e\u043c\u0443\u043d\u0456\u043a\u0430\u0446\u0456\u0457, \u0448\u0432\u0438\u0434\u043a\u0435 NVMe-\u0441\u0445\u043e\u0432\u0438\u0449\u0435 \u0434\u043b\u044f \u0441\u0442\u0440\u0438\u043c\u0456\u043d\u0433\u0443 \u0434\u0430\u0442\u0430\u0441\u0435\u0442\u0456\u0432, \u0456 \u0434\u043e\u0441\u0442\u0430\u0442\u043d\u044c\u043e \u0441\u0438\u0441\u0442\u0435\u043c\u043d\u043e\u0457 RAM \u0434\u043b\u044f \u043f\u0440\u0435\u043f\u0440\u043e\u0446\u0435\u0441\u0438\u043d\u0433\u0443.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u041a\u043b\u044e\u0447\u043e\u0432\u0456 \u043a\u043e\u043c\u043f\u043e\u043d\u0435\u043d\u0442\u0438, \u044f\u043a\u0456 \u0432\u0438\u0437\u043d\u0430\u0447\u0430\u044e\u0442\u044c \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u0438\u0432\u043d\u0456\u0441\u0442\u044c:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">VRAM (GPU-\u043f\u0430\u043c&#8217;\u044f\u0442\u044c) &#8211; \u043d\u0430\u0439\u0447\u0430\u0441\u0442\u0456\u0448\u0435 \u0432\u0443\u0437\u044c\u043a\u0435 \u043c\u0456\u0441\u0446\u0435. 70B \u043c\u043e\u0434\u0435\u043b\u044c \u0443 FP16 \u043f\u043e\u0442\u0440\u0435\u0431\u0443\u0454 ~140 \u0413\u0411. \u042f\u043a\u0449\u043e \u043c\u043e\u0434\u0435\u043b\u044c \u043d\u0435 \u0432\u043b\u0430\u0437\u0438\u0442\u044c \u0443 VRAM &#8211; \u0430\u0431\u043e \u043a\u0432\u0430\u043d\u0442\u0438\u0437\u0430\u0446\u0456\u044f (INT8\/INT4), \u0430\u0431\u043e \u0431\u0456\u043b\u044c\u0448\u0435 GPU.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">GPU-\u0456\u043d\u0442\u0435\u0440\u043a\u043e\u043d\u0435\u043a\u0442 &#8211; NVLink \u0434\u043e\u0437\u0432\u043e\u043b\u044f\u0454 GPU \u043d\u0430 \u043e\u0434\u043d\u043e\u043c\u0443 \u0432\u0443\u0437\u043b\u0456 \u0434\u0456\u043b\u0438\u0442\u0438\u0441\u044f \u043f\u0430\u043c&#8217;\u044f\u0442\u0442\u044e \u0442\u0430 \u0441\u043f\u0456\u043b\u043a\u0443\u0432\u0430\u0442\u0438\u0441\u044f \u0437 \u043f\u0440\u043e\u043f\u0443\u0441\u043a\u043d\u043e\u044e \u0437\u0434\u0430\u0442\u043d\u0456\u0441\u0442\u044e 600 \u0413\u0411\/\u0441 (H100). \u0411\u0435\u0437 NVLink &#8211; \u043a\u043e\u043c\u0443\u043d\u0456\u043a\u0430\u0446\u0456\u044f \u0447\u0435\u0440\u0435\u0437 PCIe, \u0449\u043e \u0443 5-10 \u0440\u0430\u0437\u0456\u0432 \u043f\u043e\u0432\u0456\u043b\u044c\u043d\u0456\u0448\u0435 \u0434\u043b\u044f \u0440\u043e\u0437\u043f\u043e\u0434\u0456\u043b\u0435\u043d\u043e\u0433\u043e \u0442\u0440\u0435\u043d\u0443\u0432\u0430\u043d\u043d\u044f.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">NVMe-\u0441\u0445\u043e\u0432\u0438\u0449\u0435 &#8211; \u043f\u0456\u0434 \u0447\u0430\u0441 \u0442\u0440\u0435\u043d\u0443\u0432\u0430\u043d\u043d\u044f \u0441\u0435\u0440\u0432\u0435\u0440 \u0431\u0435\u0437\u043f\u0435\u0440\u0435\u0440\u0432\u043d\u043e \u0441\u0442\u0440\u0438\u043c\u0438\u0442\u044c \u0431\u0430\u0442\u0447\u0456. \u041e\u0434\u0438\u043d NVMe 3.5 \u0413\u0411\/\u0441 \u043d\u0435 \u0432\u0441\u0442\u0438\u0433\u0430\u0454 \u0437\u0430 8xA100. \u041c\u0456\u043d\u0456\u043c\u0443\u043c &#8211; RAID \u0456\u0437 \u043a\u0456\u043b\u044c\u043a\u043e\u0445 NVMe \u0430\u0431\u043e \u043e\u043a\u0440\u0435\u043c\u0438\u0439 storage-\u0432\u0443\u0437\u043e\u043b.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">\u0421\u0438\u0441\u0442\u0435\u043c\u043d\u0430 RAM &#8211; \u043c\u0430\u0454 \u0431\u0443\u0442\u0438 \u043d\u0435 \u043c\u0435\u043d\u0448\u0435 \u0441\u0443\u043c\u0430\u0440\u043d\u043e\u0433\u043e VRAM. \u041d\u0430 8xH100 (640 \u0413\u0411 VRAM) &#8211; \u043c\u0456\u043d\u0456\u043c\u0443\u043c 512 \u0413\u0411 RAM \u0434\u043b\u044f \u043d\u043e\u0440\u043c\u0430\u043b\u044c\u043d\u043e\u0433\u043e \u043f\u0440\u0435\u043f\u0440\u043e\u0446\u0435\u0441\u0438\u043d\u0433\u0443.<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h2><b>\u0421\u0446\u0435\u043d\u0430\u0440\u0456\u0457: \u0445\u0442\u043e \u0456 \u044f\u043a\u0443 \u043a\u043e\u043d\u0444\u0456\u0433\u0443\u0440\u0430\u0446\u0456\u044e \u043e\u0431\u0438\u0440\u0430\u0454<\/b><\/h2>\n<h3><b>\u0421\u0446\u0435\u043d\u0430\u0440\u0456\u0439 1 &#8211; ML-\u0456\u043d\u0436\u0435\u043d\u0435\u0440 \u0443 \u0441\u0442\u0430\u0440\u0442\u0430\u043f\u0456, \u043f\u0435\u0440\u0448\u0456 \u0435\u043a\u0441\u043f\u0435\u0440\u0438\u043c\u0435\u043d\u0442\u0438<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">\u0421\u0438\u0442\u0443\u0430\u0446\u0456\u044f: \u043a\u043e\u043c\u0430\u043d\u0434\u0430 \u0437 2-3 ML-\u0456\u043d\u0436\u0435\u043d\u0435\u0440\u0456\u0432, \u0454 \u0456\u0434\u0435\u044f \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u0443, \u043f\u043e\u0442\u0440\u0456\u0431\u043d\u043e \u043f\u0435\u0440\u0435\u0432\u0456\u0440\u0438\u0442\u0438 \u0433\u0456\u043f\u043e\u0442\u0435\u0437\u0438 \u043d\u0430 \u043d\u0435\u0432\u0435\u043b\u0438\u043a\u0438\u0445 \u0434\u0430\u0442\u0430\u0441\u0435\u0442\u0430\u0445. \u0411\u044e\u0434\u0436\u0435\u0442 \u043e\u0431\u043c\u0435\u0436\u0435\u043d\u0438\u0439, \u043a\u043e\u043d\u0444\u0456\u0433\u0443\u0440\u0430\u0446\u0456\u044f \u043c\u043e\u0436\u0435 \u0437\u043c\u0456\u043d\u044e\u0432\u0430\u0442\u0438\u0441\u044f \u0449\u043e\u043c\u0456\u0441\u044f\u0446\u044f.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u0429\u043e \u0432\u0456\u0434\u0431\u0443\u0432\u0430\u0454\u0442\u044c\u0441\u044f \u0431\u0435\u0437 GPU: \u0442\u0440\u0435\u043d\u0443\u0432\u0430\u043d\u043d\u044f \u043f\u0440\u043e\u0441\u0442\u043e\u0433\u043e \u043a\u043b\u0430\u0441\u0438\u0444\u0456\u043a\u0430\u0442\u043e\u0440\u0430 \u043d\u0430 100k \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0456\u0432 \u0437\u0430\u0439\u043c\u0430\u0454 \u0433\u043e\u0434\u0438\u043d\u0443 \u0437\u0430\u043c\u0456\u0441\u0442\u044c \u0445\u0432\u0438\u043b\u0438\u043d\u0438. \u0406\u0442\u0435\u0440\u0430\u0446\u0456\u0457 \u0441\u043f\u043e\u0432\u0456\u043b\u044c\u043d\u044e\u044e\u0442\u044c\u0441\u044f \u0432 20-50 \u0440\u0430\u0437\u0456\u0432. \u041a\u043e\u043c\u0430\u043d\u0434\u0430 \u0432\u0438\u0442\u0440\u0430\u0447\u0430\u0454 \u0447\u0430\u0441 \u043d\u0430 \u043e\u0447\u0456\u043a\u0443\u0432\u0430\u043d\u043d\u044f, \u0430 \u043d\u0435 \u043d\u0430 \u0440\u043e\u0437\u0440\u043e\u0431\u043a\u0443.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u0420\u0456\u0448\u0435\u043d\u043d\u044f: 1-2x RTX 4090 (24 \u0413\u0411 \u043a\u043e\u0436\u043d\u0430). \u0414\u043b\u044f \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u0434\u043e 13B (\u0437 \u043a\u0432\u0430\u043d\u0442\u0438\u0437\u0430\u0446\u0456\u0454\u044e) &#8211; \u0434\u043e\u0441\u0442\u0430\u0442\u043d\u044c\u043e. \u0412\u0430\u0440\u0442\u0456\u0441\u0442\u044c &#8211; $300-700\/\u043c\u0456\u0441. \u042f\u043a\u0449\u043e \u043f\u043e\u0442\u0440\u0456\u0431\u043d\u0430 \u0433\u043d\u0443\u0447\u043a\u0456\u0441\u0442\u044c &#8211; \u0445\u043c\u0430\u0440\u043d\u0438\u0439 GPU-\u0456\u043d\u0441\u0442\u0430\u043d\u0441 \u0437 \u043f\u043e\u0447\u0430\u0441\u043e\u0432\u043e\u044e \u043e\u043f\u043b\u0430\u0442\u043e\u044e \u043d\u0430 \u043f\u043e\u0447\u0430\u0442\u043a\u0443, \u0432\u0438\u0434\u0456\u043b\u0435\u043d\u0438\u0439 \u0441\u0435\u0440\u0432\u0435\u0440 \u043f\u0440\u0438 \u0440\u0435\u0433\u0443\u043b\u044f\u0440\u043d\u043e\u043c\u0443 \u043d\u0430\u0432\u0430\u043d\u0442\u0430\u0436\u0435\u043d\u043d\u0456 \u0432\u0456\u0434 60% \u0447\u0430\u0441\u0443.<\/span><\/p>\n<p>&nbsp;<\/p>\n<h3><b>\u0421\u0446\u0435\u043d\u0430\u0440\u0456\u0439 2 &#8211; \u0424\u0430\u0439\u043d-\u0442\u044e\u043d\u0456\u043d\u0433 LLM \u0434\u043b\u044f \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u0443<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">\u0421\u0438\u0442\u0443\u0430\u0446\u0456\u044f: \u0454 \u0431\u0430\u0437\u043e\u0432\u0430 \u043c\u043e\u0434\u0435\u043b\u044c (Llama 3, Mistral, Gemma), \u043f\u043e\u0442\u0440\u0456\u0431\u043d\u043e \u0430\u0434\u0430\u043f\u0442\u0443\u0432\u0430\u0442\u0438 \u043f\u0456\u0434 \u043a\u043e\u043d\u043a\u0440\u0435\u0442\u043d\u0438\u0439 \u0434\u043e\u043c\u0435\u043d (\u044e\u0440\u0438\u0434\u0438\u0447\u043d\u0456 \u0442\u0435\u043a\u0441\u0442\u0438, \u043c\u0435\u0434\u0438\u0447\u043d\u0430 \u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u0430\u0446\u0456\u044f, \u043a\u043e\u0434). \u0414\u0430\u0442\u0430\u0441\u0435\u0442 &#8211; 10k-500k \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0456\u0432. \u0422\u0440\u0435\u043d\u0443\u0432\u0430\u043d\u043d\u044f &#8211; \u0440\u0435\u0433\u0443\u043b\u044f\u0440\u043d\u0435 (\u0440\u0430\u0437 \u043d\u0430 \u0442\u0438\u0436\u0434\u0435\u043d\u044c \u0430\u0431\u043e \u043c\u0456\u0441\u044f\u0446\u044c).<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u0424\u0430\u0439\u043d-\u0442\u044e\u043d\u0456\u043d\u0433 7B \u0447\u0435\u0440\u0435\u0437 LoRA \u043d\u0430 \u043e\u0434\u043d\u043e\u043c\u0443 A100 40GB \u0437\u0430\u0439\u043c\u0430\u0454 2-8 \u0433\u043e\u0434\u0438\u043d \u0437\u0430\u043b\u0435\u0436\u043d\u043e \u0432\u0456\u0434 \u0434\u0430\u0442\u0430\u0441\u0435\u0442\u0443. \u0414\u043b\u044f 70B \u0447\u0435\u0440\u0435\u0437 QLoRA \u043d\u0430 4x A100 80GB &#8211; 12-24 \u0433\u043e\u0434\u0438\u043d\u0438. \u0426\u0435 \u0432\u0436\u0435 \u0440\u0435\u0430\u043b\u044c\u043d\u0435 \u043f\u0440\u043e\u0434\u0430\u043a\u0448\u043d-\u0440\u043e\u0437\u043a\u043b\u0430\u0434\u0430\u043d\u043d\u044f.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u0420\u0456\u0448\u0435\u043d\u043d\u044f: \u0434\u043b\u044f 7B-13B &#8211; 1-2x A100 40GB \u0430\u0431\u043e RTX 4090. \u0414\u043b\u044f 30B-70B &#8211; 4x A100 80GB \u0437 NVLink. \u0412\u0438\u0434\u0456\u043b\u0435\u043d\u0438\u0439 bare-metal \u0432\u0438\u043f\u0440\u0430\u0432\u0434\u0430\u043d\u0438\u0439 \u043f\u0440\u0438 \u0440\u0435\u0433\u0443\u043b\u044f\u0440\u043d\u0438\u0445 \u0442\u0440\u0435\u043d\u0443\u0432\u0430\u043d\u043d\u044f\u0445 &#8211; \u0434\u0435\u0448\u0435\u0432\u0448\u0435 \u0445\u043c\u0430\u0440\u0438 \u0432\u0456\u0434 ~3 \u0437\u0430\u043f\u0443\u0441\u043a\u0456\u0432 \u043d\u0430 \u043c\u0456\u0441\u044f\u0446\u044c.<\/span><\/p>\n<p>&nbsp;<\/p>\n<h3><b>\u0421\u0446\u0435\u043d\u0430\u0440\u0456\u0439 3 &#8211; \u041f\u0440\u043e\u0434\u0430\u043a\u0448\u043d LLM-\u0456\u043d\u0444\u0435\u0440\u0435\u043d\u0441<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">\u0421\u0438\u0442\u0443\u0430\u0446\u0456\u044f: \u043c\u043e\u0434\u0435\u043b\u044c \u0432\u0436\u0435 \u043d\u0430\u0432\u0447\u0435\u043d\u0430, \u043f\u043e\u0442\u0440\u0456\u0431\u043d\u043e \u0437\u0430\u043f\u0443\u0441\u0442\u0438\u0442\u0438 API \u0434\u043b\u044f 1000+ \u043a\u043e\u0440\u0438\u0441\u0442\u0443\u0432\u0430\u0447\u0456\u0432. \u0412\u0438\u043c\u043e\u0433\u0438: latency &lt; 200 \u043c\u0441 \u043d\u0430 \u043f\u0435\u0440\u0448\u0438\u0439 \u0442\u043e\u043a\u0435\u043d, throughput 50+ \u0437\u0430\u043f\u0438\u0442\u0456\u0432\/\u0441\u0435\u043a.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u0422\u0443\u0442 \u0432\u0430\u0436\u043b\u0438\u0432\u0438\u0439 \u043d\u0435 \u043b\u0438\u0448\u0435 VRAM, \u0430 \u0439 throughput GPU. H100 \u0433\u0435\u043d\u0435\u0440\u0443\u0454 \u0442\u043e\u043a\u0435\u043d\u0438 ~3x \u0448\u0432\u0438\u0434\u0448\u0435 \u0437\u0430 A100 \u043f\u0440\u0438 \u043e\u0434\u043d\u0430\u043a\u043e\u0432\u043e\u043c\u0443 VRAM \u0437\u0430\u0432\u0434\u044f\u043a\u0438 \u0442\u0440\u0430\u043d\u0441\u0444\u043e\u0440\u043c\u0435\u0440\u0443 FlashAttention 2 \u0456 \u0432\u0438\u0449\u0456\u0439 \u043f\u0430\u043c&#8217;\u044f\u0442\u043d\u0456\u0439 \u043f\u0440\u043e\u043f\u0443\u0441\u043a\u043d\u0456\u0439 \u0437\u0434\u0430\u0442\u043d\u043e\u0441\u0442\u0456 (3.35 \u0422\u0411\/\u0441 vs 2 \u0422\u0411\/\u0441). \u0414\u043b\u044f \u043c\u043e\u0434\u0435\u043b\u0456 13B &#8211; \u0434\u043e\u0441\u0442\u0430\u0442\u043d\u044c\u043e 1x A100 40GB. \u0414\u043b\u044f 70B &#8211; 2x H100 \u0430\u0431\u043e 4x A100 80GB.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u0420\u0456\u0448\u0435\u043d\u043d\u044f: \u0432\u0438\u0434\u0456\u043b\u0435\u043d\u0438\u0439 \u0441\u0435\u0440\u0432\u0435\u0440 \u0437\u0430\u043c\u0456\u0441\u0442\u044c \u0445\u043c\u0430\u0440\u0438 \u043e\u043a\u0443\u043f\u0430\u0454\u0442\u044c\u0441\u044f \u043f\u0440\u0438 \u043f\u043e\u0441\u0442\u0456\u0439\u043d\u043e\u043c\u0443 \u043d\u0430\u0432\u0430\u043d\u0442\u0430\u0436\u0435\u043d\u043d\u0456. 2x H100 \u0434\u043b\u044f 70B-\u0456\u043d\u0444\u0435\u0440\u0435\u043d\u0441\u0443 \u0443 \u043f\u0440\u043e\u0434\u0430\u043a\u0448\u043d\u0456 &#8211; \u0441\u0442\u0430\u043d\u0434\u0430\u0440\u0442\u043d\u0430 \u043a\u043e\u043d\u0444\u0456\u0433\u0443\u0440\u0430\u0446\u0456\u044f \u0434\u043b\u044f LLM API.<\/span><\/p>\n<p>&nbsp;<\/p>\n<h3><b>\u0421\u0446\u0435\u043d\u0430\u0440\u0456\u0439 4 &#8211; \u0414\u043e\u0441\u043b\u0456\u0434\u043d\u0438\u0446\u044c\u043a\u0430 \u043a\u043e\u043c\u0430\u043d\u0434\u0430, \u0442\u0440\u0435\u043d\u0443\u0432\u0430\u043d\u043d\u044f \u0437 \u043d\u0443\u043b\u044f<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">\u0421\u0438\u0442\u0443\u0430\u0446\u0456\u044f: \u0430\u043a\u0430\u0434\u0435\u043c\u0456\u0447\u043d\u0430 \u0430\u0431\u043e R&amp;D-\u043a\u043e\u043c\u0430\u043d\u0434\u0430, \u0442\u0440\u0435\u043d\u0443\u0432\u0430\u043d\u043d\u044f \u0432\u043b\u0430\u0441\u043d\u043e\u0457 \u0430\u0440\u0445\u0456\u0442\u0435\u043a\u0442\u0443\u0440\u0438 \u0430\u0431\u043e foundation model. \u0414\u0430\u0442\u0430\u0441\u0435\u0442\u0438 &#8211; \u0441\u043e\u0442\u043d\u0456 \u0413\u0411 \u0430\u0431\u043e \u0442\u0435\u0440\u0430\u0431\u0430\u0439\u0442\u0438. \u0427\u0430\u0441 \u0442\u0440\u0435\u043d\u0443\u0432\u0430\u043d\u043d\u044f &#8211; \u0434\u043d\u0456 \u0430\u0431\u043e \u0442\u0438\u0436\u043d\u0456.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u0422\u0443\u0442 \u043a\u0440\u0438\u0442\u0438\u0447\u043d\u0438\u0439 InfiniBand \u043c\u0456\u0436 \u0432\u0443\u0437\u043b\u0430\u043c\u0438: \u043f\u0440\u0438 \u0442\u0440\u0435\u043d\u0443\u0432\u0430\u043d\u043d\u0456 \u043d\u0430 32 GPU \u0440\u0456\u0437\u043d\u0438\u0445 \u0441\u0435\u0440\u0432\u0435\u0440\u0456\u0432 \u0433\u0440\u0430\u0434\u0456\u0454\u043d\u0442\u0438 \u0441\u0438\u043d\u0445\u0440\u043e\u043d\u0456\u0437\u0443\u044e\u0442\u044c\u0441\u044f \u0447\u0435\u0440\u0435\u0437 \u043c\u0435\u0440\u0435\u0436\u0443. InfiniBand 400 \u0413\u0431\u0456\u0442\/\u0441 vs 100 GbE Ethernet \u0434\u0430\u0454 \u0440\u0456\u0437\u043d\u0438\u0446\u044e \u0432 \u0435\u0444\u0435\u043a\u0442\u0438\u0432\u043d\u043e\u0441\u0442\u0456 multi-node \u0442\u0440\u0435\u043d\u0443\u0432\u0430\u043d\u043d\u044f \u0434\u043e 2-3x.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u0420\u0456\u0448\u0435\u043d\u043d\u044f: 8x H100 \u0430\u0431\u043e H200 \u044f\u043a \u043c\u0456\u043d\u0456\u043c\u0430\u043b\u044c\u043d\u0438\u0439 \u0432\u0443\u0437\u043e\u043b \u0434\u043b\u044f \u0441\u0435\u0440\u0439\u043e\u0437\u043d\u0438\u0445 \u0437\u0430\u0434\u0430\u0447. NVLink \u0432\u0441\u0435\u0440\u0435\u0434\u0438\u043d\u0456 \u0432\u0443\u0437\u043b\u0430, InfiniBand \u043c\u0456\u0436 \u0432\u0443\u0437\u043b\u0430\u043c\u0438. NVMe RAID \u0434\u043b\u044f \u0441\u0442\u0440\u0438\u043c\u0456\u043d\u0433\u0443 \u0434\u0430\u0442\u0430\u0441\u0435\u0442\u0456\u0432.<\/span><\/p>\n<p>&nbsp;<\/p>\n<h2><b>\u041a\u0440\u0430\u0449\u0456 GPU-\u043a\u043e\u043d\u0444\u0456\u0433\u0443\u0440\u0430\u0446\u0456\u0457 \u0434\u043b\u044f ML<\/b><\/h2>\n<table>\n<tbody>\n<tr>\n<td><b>GPU<\/b><\/td>\n<td><b>VRAM<\/b><\/td>\n<td><b>HBM bandwidth<\/b><\/td>\n<td><b>NVLink<\/b><\/td>\n<td><b>\u0426\u0456\u043d\u0430\/\u043c\u0456\u0441 (\u043e\u0440\u0456\u0454\u043d\u0442.)<\/b><\/td>\n<td><b>\u041e\u043f\u0442\u0438\u043c\u0430\u043b\u044c\u043d\u043e \u0434\u043b\u044f<\/b><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">RTX 4090<\/span><\/td>\n<td><span style=\"font-weight: 400;\">24 \u0413\u0411<\/span><\/td>\n<td><span style=\"font-weight: 400;\">1 \u0422\u0411\/\u0441<\/span><\/td>\n<td><span style=\"font-weight: 400;\">\u041d\u0456<\/span><\/td>\n<td><span style=\"font-weight: 400;\">$300-450<\/span><\/td>\n<td><span style=\"font-weight: 400;\">\u041f\u0440\u043e\u0442\u043e\u0442\u0438\u043f\u0438, \u0434\u0440\u0456\u0431\u043d\u0456 \u043c\u043e\u0434\u0435\u043b\u0456, inference \u0434\u043e 13B<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">A100 40GB<\/span><\/td>\n<td><span style=\"font-weight: 400;\">40 \u0413\u0411<\/span><\/td>\n<td><span style=\"font-weight: 400;\">2 \u0422\u0411\/\u0441<\/span><\/td>\n<td><span style=\"font-weight: 400;\">\u0422\u0430\u043a<\/span><\/td>\n<td><span style=\"font-weight: 400;\">$600-900<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Fine-tuning 7B-30B, inference 30B+<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">A100 80GB<\/span><\/td>\n<td><span style=\"font-weight: 400;\">80 \u0413\u0411<\/span><\/td>\n<td><span style=\"font-weight: 400;\">2 \u0422\u0411\/\u0441<\/span><\/td>\n<td><span style=\"font-weight: 400;\">\u0422\u0430\u043a<\/span><\/td>\n<td><span style=\"font-weight: 400;\">$900-1400<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Fine-tuning 70B, training 7B-30B<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">H100 80GB<\/span><\/td>\n<td><span style=\"font-weight: 400;\">80 \u0413\u0411<\/span><\/td>\n<td><span style=\"font-weight: 400;\">3.35 \u0422\u0411\/\u0441<\/span><\/td>\n<td><span style=\"font-weight: 400;\">\u0422\u0430\u043a (NVLink 4)<\/span><\/td>\n<td><span style=\"font-weight: 400;\">$2000-3500<\/span><\/td>\n<td><span style=\"font-weight: 400;\">\u041f\u0440\u043e\u0434\u0430\u043a\u0448\u043d inference, training 30B+<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">H200 141GB<\/span><\/td>\n<td><span style=\"font-weight: 400;\">141 \u0413\u0411<\/span><\/td>\n<td><span style=\"font-weight: 400;\">4.8 \u0422\u0411\/\u0441<\/span><\/td>\n<td><span style=\"font-weight: 400;\">\u0422\u0430\u043a (NVLink 4)<\/span><\/td>\n<td><span style=\"font-weight: 400;\">$3500-6000<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Foundation models, 70B+ training<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p><span style=\"font-weight: 400;\">\u0426\u0456\u043d\u0438 &#8211; \u0437\u0430 \u043e\u0434\u0438\u043d GPU \u0432 \u043a\u043e\u043d\u0444\u0456\u0433\u0443\u0440\u0430\u0446\u0456\u0457 \u0432\u0438\u0434\u0456\u043b\u0435\u043d\u043e\u0433\u043e bare-metal \u0441\u0435\u0440\u0432\u0435\u0440\u0430. \u0425\u043c\u0430\u0440\u043d\u0456 on-demand \u0446\u0456\u043d\u0438 \u0432 2-4 \u0440\u0430\u0437\u0438 \u0432\u0438\u0449\u0456 \u043f\u0440\u0438 \u043f\u043e\u0441\u0442\u0456\u0439\u043d\u043e\u043c\u0443 \u043d\u0430\u0432\u0430\u043d\u0442\u0430\u0436\u0435\u043d\u043d\u0456.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u041f\u0435\u0440\u0435\u0433\u043b\u044f\u043d\u0443\u0442\u0438 \u0430\u043a\u0442\u0443\u0430\u043b\u044c\u043d\u0456 GPU-\u0441\u0435\u0440\u0432\u0435\u0440\u0438: <\/span><a href=\"https:\/\/unihost.com\/dedicated\/gpu\/\"><span style=\"font-weight: 400;\">GPU servers Unihost<\/span><\/a><span style=\"font-weight: 400;\">. \u0414\u043b\u044f AI\/ML \u0445\u043e\u0441\u0442\u0438\u043d\u0433\u0443 \u0437 managed-\u0456\u043d\u0444\u0440\u0430\u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u043e\u044e:\u00a0<\/span><\/p>\n<p><a href=\"https:\/\/unihost.com\/dedicated\/ai-servers\/\"><span style=\"font-weight: 400;\">AI hosting Unihost<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<p>&nbsp;<\/p>\n<h2><b>\u0421\u0446\u0435\u043d\u0430\u0440\u0456\u0457 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u043d\u043d\u044f ML<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Computer Vision. \u0414\u0435\u0442\u0435\u043a\u0446\u0456\u044f \u043e\u0431&#8217;\u0454\u043a\u0442\u0456\u0432 (YOLO, DETR), \u0441\u0435\u0433\u043c\u0435\u043d\u0442\u0430\u0446\u0456\u044f, \u043a\u043b\u0430\u0441\u0438\u0444\u0456\u043a\u0430\u0446\u0456\u044f \u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u044c. \u0412\u0438\u043c\u043e\u0433\u0438 \u0434\u043e VRAM \u043d\u0438\u0436\u0447\u0456, \u043d\u0456\u0436 \u0443 LLM &#8211; \u0431\u0430\u0442\u0447 \u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u044c \u0437\u0430\u0439\u043c\u0430\u0454 4-16 \u0413\u0411 \u0434\u043b\u044f \u0431\u0456\u043b\u044c\u0448\u043e\u0441\u0442\u0456 \u0430\u0440\u0445\u0456\u0442\u0435\u043a\u0442\u0443\u0440. 1-2x RTX 4090 \u0430\u0431\u043e A100 40GB \u0437\u0430\u043a\u0440\u0438\u0432\u0430\u0454 90% CV-\u0437\u0430\u0434\u0430\u0447.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">NLP \u0442\u0430 \u043e\u0431\u0440\u043e\u0431\u043a\u0430 \u0442\u0435\u043a\u0441\u0442\u0443. BERT, RoBERTa, T5 \u0434\u043b\u044f \u043a\u043b\u0430\u0441\u0438\u0444\u0456\u043a\u0430\u0446\u0456\u0457, NER, sentiment. \u041c\u043e\u0434\u0435\u043b\u0456 \u0434\u043e 1B \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u0456\u0432 &#8211; RTX 4090 \u0431\u0456\u043b\u044c\u0448 \u043d\u0456\u0436 \u0434\u043e\u0441\u0442\u0430\u0442\u043d\u044c\u043e. \u0412\u0435\u043b\u0438\u043a\u0456 \u0442\u0440\u0430\u043d\u0441\u0444\u043e\u0440\u043c\u0435\u0440\u0438 (3B-7B) &#8211; A100 40GB.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u0420\u0435\u043a\u043e\u043c\u0435\u043d\u0434\u0430\u0446\u0456\u0439\u043d\u0456 \u0441\u0438\u0441\u0442\u0435\u043c\u0438. Embedding-\u043c\u043e\u0434\u0435\u043b\u0456, \u0434\u0432\u043e\u0432\u0435\u0436\u0435\u0432\u0456 \u0430\u0440\u0445\u0456\u0442\u0435\u043a\u0442\u0443\u0440\u0438, \u0440\u0430\u043d\u0436\u0443\u0432\u0430\u043d\u043d\u044f. \u041e\u0431\u0441\u044f\u0433 VRAM \u0432\u0456\u0434\u043d\u043e\u0441\u043d\u043e \u043d\u0435\u0432\u0435\u043b\u0438\u043a\u0438\u0439, \u0430\u043b\u0435 \u0432\u0430\u0436\u043b\u0438\u0432\u0430 \u0448\u0432\u0438\u0434\u043a\u0456\u0441\u0442\u044c inference \u0434\u043b\u044f real-time \u0440\u0435\u043a\u043e\u043c\u0435\u043d\u0434\u0430\u0446\u0456\u0439. 1-2x A100 40GB \u0434\u043b\u044f \u043f\u0440\u043e\u0434\u0430\u043a\u0448\u043d-\u0440\u0435\u043a\u043e\u043c\u0435\u043d\u0434\u0435\u0440\u0456\u0432.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u0413\u0435\u043d\u0435\u0440\u0430\u0446\u0456\u044f \u0430\u0443\u0434\u0456\u043e \u0442\u0430 \u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u044c. Stable Diffusion, Whisper, MusicGen. SD XL \u043f\u043e\u0442\u0440\u0435\u0431\u0443\u0454 8-12 \u0413\u0411 VRAM \u0434\u043b\u044f \u0431\u0430\u0437\u043e\u0432\u043e\u0433\u043e inference. \u0414\u043b\u044f fine-tuning \u0456 batch-\u0433\u0435\u043d\u0435\u0440\u0430\u0446\u0456\u0457 &#8211; 24+ \u0413\u0411. RTX 4090 \u0430\u0431\u043e A100 40GB.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Reinforcement Learning. RLHF \u0434\u043b\u044f LLM, \u0456\u0433\u0440\u043e\u0432\u0456 \u0430\u0433\u0435\u043d\u0442\u0438. \u041a\u043e\u043c\u0431\u0456\u043d\u0430\u0446\u0456\u044f \u043e\u0431\u0447\u0438\u0441\u043b\u0435\u043d\u044c \u043d\u0430 GPU \u0456 CPU. \u0421\u043f\u0435\u0446\u0456\u0444\u0456\u0447\u043d\u0456 \u0432\u0438\u043c\u043e\u0433\u0438 \u0437\u0430\u043b\u0435\u0436\u0430\u0442\u044c \u0432\u0456\u0434 \u0441\u0435\u0440\u0435\u0434\u043e\u0432\u0438\u0449\u0430 &#8211; \u0432\u0456\u0434 RTX 4090 \u0434\u043e multi-GPU \u043a\u043b\u0430\u0441\u0442\u0435\u0440\u0430 \u0434\u043b\u044f \u0441\u043a\u043b\u0430\u0434\u043d\u0438\u0445 \u0437\u0430\u0434\u0430\u0447.<\/span><\/p>\n<p>&nbsp;<\/p>\n<h2><b>\u0427\u0430\u0441\u0442\u0456 \u0437\u0430\u043f\u0438\u0442\u0430\u043d\u043d\u044f<\/b><\/h2>\n<h3><b>\u042f\u043a\u0438\u0439 GPU \u043d\u0430\u0439\u043a\u0440\u0430\u0449\u0438\u0439 \u0434\u043b\u044f \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043d\u0430\u0432\u0447\u0430\u043d\u043d\u044f?<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">\u0417\u0430\u043b\u0435\u0436\u0438\u0442\u044c \u0432\u0456\u0434 \u0437\u0430\u0434\u0430\u0447\u0456 \u0456 \u0431\u044e\u0434\u0436\u0435\u0442\u0443. H100 80GB &#8211; \u043d\u0430\u0439\u043a\u0440\u0430\u0449\u0435 \u0437\u0430\u043b\u0456\u0437\u043e \u0434\u043b\u044f \u0441\u0435\u0440\u0439\u043e\u0437\u043d\u043e\u0433\u043e ML \u0443 2026 \u0440\u043e\u0446\u0456, \u0430\u043b\u0435 \u0437\u0430 \u0446\u0456\u043d\u043e\u044e. A100 80GB &#8211; \u043e\u043f\u0442\u0438\u043c\u0430\u043b\u044c\u043d\u0438\u0439 \u0431\u0430\u043b\u0430\u043d\u0441 \u0434\u043b\u044f \u0431\u0456\u043b\u044c\u0448\u043e\u0441\u0442\u0456 \u043f\u0440\u043e\u0434\u0430\u043a\u0448\u043d-\u0437\u0430\u0434\u0430\u0447. RTX 4090 &#8211; \u043d\u0430\u0439\u043a\u0440\u0430\u0449\u0438\u0439 \u0432\u0438\u0431\u0456\u0440 \u0434\u043b\u044f \u0431\u044e\u0434\u0436\u0435\u0442\u043d\u043e\u0433\u043e \u0441\u0442\u0430\u0440\u0442\u0443 \u0456 \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u0434\u043e 13B. \u042f\u043a\u0449\u043e \u043e\u0431\u043c\u0435\u0436\u0435\u043d\u0456 \u0440\u0435\u0441\u0443\u0440\u0441\u0438 &#8211; A100 40GB \u0437\u0430\u043a\u0440\u0438\u0432\u0430\u0454 70% \u0440\u0435\u0430\u043b\u044c\u043d\u0438\u0445 ML-\u0437\u0430\u0434\u0430\u0447.<\/span><\/p>\n<h3><b>\u0427\u0438 \u043f\u043e\u0442\u0440\u0456\u0431\u0435\u043d GPU \u0434\u043b\u044f AI-\u0442\u0440\u0435\u043d\u0443\u0432\u0430\u043d\u043d\u044f?<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">\u0414\u043b\u044f \u0431\u0443\u0434\u044c-\u044f\u043a\u043e\u0433\u043e \u0441\u0435\u0440\u0439\u043e\u0437\u043d\u043e\u0433\u043e ML &#8211; \u0442\u0430\u043a. CPU-\u0442\u0440\u0435\u043d\u0443\u0432\u0430\u043d\u043d\u044f \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u0438\u0445 \u043c\u0435\u0440\u0435\u0436 \u0443 10-100 \u0440\u0430\u0437\u0456\u0432 \u043f\u043e\u0432\u0456\u043b\u044c\u043d\u0456\u0448\u0435. \u0412\u0438\u043d\u044f\u0442\u043e\u043a: \u043d\u0435\u0432\u0435\u043b\u0438\u043a\u0456 \u043a\u043b\u0430\u0441\u0438\u0447\u043d\u0456 ML-\u043c\u043e\u0434\u0435\u043b\u0456 (Random Forest, XGBoost, \u043b\u0456\u043d\u0456\u0439\u043d\u0456 \u043c\u043e\u0434\u0435\u043b\u0456) \u0446\u0456\u043b\u043a\u043e\u043c \u0442\u0440\u0435\u043d\u0443\u044e\u0442\u044c\u0441\u044f \u043d\u0430 CPU. \u0410\u043b\u0435 \u044f\u043a\u0449\u043e \u0432\u0438 \u043f\u0440\u0430\u0446\u044e\u0454\u0442\u0435 \u0437 \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u0438\u043c\u0438 \u043c\u0435\u0440\u0435\u0436\u0430\u043c\u0438 \u0432\u0456\u0434 \u043a\u0456\u043b\u044c\u043a\u043e\u0445 \u043c\u0456\u043b\u044c\u0439\u043e\u043d\u0456\u0432 \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u0456\u0432 &#8211; GPU \u043e\u0431\u043e\u0432&#8217;\u044f\u0437\u043a\u043e\u0432\u0438\u0439.<\/span><\/p>\n<h3><b>\u0421\u043a\u0456\u043b\u044c\u043a\u0438 VRAM \u043f\u043e\u0442\u0440\u0456\u0431\u043d\u043e \u0434\u043b\u044f ML?<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">\u041f\u0440\u0430\u0432\u0438\u043b\u043e: \u0440\u043e\u0437\u043c\u0456\u0440 \u043c\u043e\u0434\u0435\u043b\u0456 (\u0432 \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u0430\u0445) \u00d7 2 \u0431\u0430\u0439\u0442\u0438 (FP16) = \u043c\u0456\u043d\u0456\u043c\u0443\u043c VRAM. 7B \u00d7 2 = ~14 \u0413\u0411. \u041f\u043b\u044e\u0441 \u0430\u043a\u0442\u0438\u0432\u0430\u0446\u0456\u0457 \u0442\u0430 \u043e\u043f\u0442\u0438\u043c\u0430\u0439\u0437\u0435\u0440-\u0441\u0442\u0430\u043d\u0438: \u0434\u043b\u044f \u0442\u0440\u0435\u043d\u0443\u0432\u0430\u043d\u043d\u044f \u043c\u043d\u043e\u0436\u0442\u0435 \u043d\u0430 4-6x. 7B \u043c\u043e\u0434\u0435\u043b\u044c \u0434\u043b\u044f \u0442\u0440\u0435\u043d\u0443\u0432\u0430\u043d\u043d\u044f \u043f\u043e\u0442\u0440\u0435\u0431\u0443\u0454 56-84 \u0413\u0411. \u0414\u043b\u044f inference &#8211; \u0442\u0456\u043b\u044c\u043a\u0438 \u0432\u0430\u0433\u0438, \u0442\u043e\u043c\u0443 7B \u0432\u043c\u0456\u0449\u0430\u0454\u0442\u044c\u0441\u044f \u0432 14-16 \u0413\u0411 (FP16) \u0430\u0431\u043e 7-8 \u0413\u0411 (INT8).<\/span><\/p>\n<h3><b>CPU vs GPU \u0434\u043b\u044f machine learning?<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">CPU \u0432\u0438\u0433\u0440\u0430\u0454 \u0442\u0456\u043b\u044c\u043a\u0438 \u0432 \u043e\u0434\u043d\u043e\u043c\u0443 \u0441\u0446\u0435\u043d\u0430\u0440\u0456\u0457: \u0442\u0440\u0430\u0434\u0438\u0446\u0456\u0439\u043d\u0438\u0439 ML \u0431\u0435\u0437 \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u0438\u0445 \u043c\u0435\u0440\u0435\u0436 (XGBoost, sklearn, feature engineering). \u0414\u043b\u044f \u0432\u0441\u044c\u043e\u0433\u043e \u0456\u043d\u0448\u043e\u0433\u043e &#8211; GPU \u0448\u0432\u0438\u0434\u0448\u0435 \u043d\u0430 \u043f\u043e\u0440\u044f\u0434\u043e\u043a. \u041f\u0440\u0430\u043a\u0442\u0438\u0447\u043d\u0435 \u043f\u0440\u0430\u0432\u0438\u043b\u043e: \u044f\u043a\u0449\u043e \u0432\u0430\u0448 \u043a\u043e\u0434 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0454 PyTorch \u0430\u0431\u043e TensorFlow \u0437 \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u0438\u043c\u0438 \u043c\u0435\u0440\u0435\u0436\u0430\u043c\u0438 &#8211; GPU \u043e\u0431\u043e\u0432&#8217;\u044f\u0437\u043a\u043e\u0432\u0438\u0439 \u0434\u043b\u044f \u0431\u0443\u0434\u044c-\u044f\u043a\u043e\u0433\u043e \u0441\u0435\u0440\u0439\u043e\u0437\u043d\u043e\u0433\u043e \u043c\u0430\u0441\u0448\u0442\u0430\u0431\u0443.<\/span><\/p>\n<p>&nbsp;<\/p>\n<h2><b>\u041d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 \u043a\u0440\u043e\u043a<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">\u0412\u0438\u0437\u043d\u0430\u0447\u0442\u0435 \u0440\u043e\u0437\u043c\u0456\u0440 \u043c\u043e\u0434\u0435\u043b\u0456 \u0442\u0430 \u0442\u0438\u043f \u0437\u0430\u0434\u0430\u0447\u0456 &#8211; \u043a\u043e\u043d\u0444\u0456\u0433\u0443\u0440\u0430\u0446\u0456\u044f \u0441\u0442\u0430\u043d\u0435 \u043e\u0447\u0435\u0432\u0438\u0434\u043d\u043e\u044e. \u0410\u043a\u0442\u0443\u0430\u043b\u044c\u043d\u0456 GPU-\u0441\u0435\u0440\u0432\u0435\u0440\u0438 \u0434\u043b\u044f ML: <\/span><a href=\"https:\/\/unihost.com\/dedicated\/gpu\/\"><span style=\"font-weight: 400;\">GPU servers Unihost<\/span><\/a><span style=\"font-weight: 400;\">. Managed AI-\u0456\u043d\u0444\u0440\u0430\u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u0430:\u00a0<\/span><\/p>\n<p><a href=\"https:\/\/unihost.com\/dedicated\/ai-servers\/\"><span style=\"font-weight: 400;\">AI hosting Unihost<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u042f\u043a\u0449\u043e \u0432\u0438 \u0432\u0436\u0435 \u0437\u043d\u0430\u0454\u0442\u0435, \u0449\u043e \u0432\u0430\u043c \u043f\u043e\u0442\u0440\u0456\u0431\u0435\u043d GPU-\u0441\u0435\u0440\u0432\u0435\u0440 \u0434\u043b\u044f ML &#8211; \u043f\u043e\u0447\u0438\u043d\u0430\u0439\u0442\u0435 \u0437 \u0442\u0430\u0431\u043b\u0438\u0446\u0456 \u043d\u0438\u0436\u0447\u0435. \u042f\u043a\u0449\u043e \u0449\u0435 \u0441\u0443\u043c\u043d\u0456\u0432\u0430\u0454\u0442\u0435\u0441\u044f \u043c\u0456\u0436 CPU \u0456 GPU \u0430\u0431\u043e \u043d\u0435 \u0437\u043d\u0430\u0454\u0442\u0435, \u044f\u043a\u0430 \u043a\u043e\u043d\u0444\u0456\u0433\u0443\u0440\u0430\u0446\u0456\u044f \u043f\u0456\u0434\u0456\u0439\u0434\u0435 &#8211; \u0447\u0438\u0442\u0430\u0439\u0442\u0435 \u0434\u0430\u043b\u0456. &nbsp; \u0428\u0432\u0438\u0434\u043a\u0438\u0439 \u0432\u0438\u0431\u0456\u0440: \u044f\u043a\u0430 \u043a\u043e\u043d\u0444\u0456\u0433\u0443\u0440\u0430\u0446\u0456\u044f \u0432\u0430\u043c \u043f\u043e\u0442\u0440\u0456\u0431\u043d\u0430 \u0412\u0430\u0448\u0435 \u0437\u0430\u0432\u0434\u0430\u043d\u043d\u044f \u041c\u0456\u043d\u0456\u043c\u0430\u043b\u044c\u043d\u0430 \u043a\u043e\u043d\u0444\u0456\u0433\u0443\u0440\u0430\u0446\u0456\u044f \u041e\u043f\u0442\u0438\u043c\u0430\u043b\u044c\u043d\u0430 \u043a\u043e\u043d\u0444\u0456\u0433\u0443\u0440\u0430\u0446\u0456\u044f \u041f\u0440\u043e\u0442\u043e\u0442\u0438\u043f, \u043d\u0430\u0432\u0447\u0430\u043d\u043d\u044f \u043d\u0430 \u043c\u0430\u043b\u0438\u0445 \u0434\u0430\u0442\u0430\u0441\u0435\u0442\u0430\u0445 1x RTX 4090 (24 \u0413\u0411) 2x [&hellip;]<\/p>\n","protected":false},"author":7,"featured_media":7346,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[32,48],"tags":[],"class_list":["post-8607","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-business_uk","category-ai","has-post-title","has-post-date","has-post-category","has-post-tag","has-post-comment","has-post-author",""],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\u041d\u0430\u0439\u043a\u0440\u0430\u0449\u0456 GPU-\u0441\u0435\u0440\u0432\u0435\u0440\u0438 \u0434\u043b\u044f \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043d\u0430\u0432\u0447\u0430\u043d\u043d\u044f - Unihost.com Blog<\/title>\n<meta name=\"description\" content=\"Discover the best GPU servers for machine learning, including configs and performance tips\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/unihost.com\/blog\/uk\/best-gpu-servers-for-machine-learning-in-2026\/\" \/>\n<meta property=\"og:locale\" content=\"uk_UA\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u041d\u0430\u0439\u043a\u0440\u0430\u0449\u0456 GPU-\u0441\u0435\u0440\u0432\u0435\u0440\u0438 \u0434\u043b\u044f \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043d\u0430\u0432\u0447\u0430\u043d\u043d\u044f - 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