{"product_id":"k-node-v100-8x-sxm2","title":"Kentino AI Node V100 — 8× NVIDIA V100 SXM2, ex-datacentre","description":"\u003cdiv style=\"font-family: -apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,sans-serif; line-height: 1.7; color: #1a1a1a;\"\u003e\n\u003c!-- IMAGE PLACEHOLDER 1: 4U node front, white background --\u003e \u003c!-- IMAGE PLACEHOLDER 2: V100 SXM2 baseboard detail --\u003e \u003c!-- IMAGE PLACEHOLDER 3: refurbishment bench - thermal pad renewal --\u003e \u003c!-- IMAGE PLACEHOLDER 4: burn-in test rig --\u003e\n\u003cdiv style=\"background: linear-gradient(135deg,#0d0d0d 0%,#1a1a2e 100%); color: #fff; padding: 32px; border-radius: 12px; margin-bottom: 32px;\"\u003e\n\u003cp style=\"font-size: 18px; margin: 0 0 20px 0; color: #ccc;\"\u003eKentino AI Node V100 - refurbished\u003c\/p\u003e\n\u003cp style=\"font-size: 28px; font-weight: bold; margin: 0 0 16px 0; line-height: 1.3;\"\u003eNVLink Multi-GPU on a Grant Budget\u003cbr\u003e8x V100 SXM2 | 256 GB HBM2 | 1,000 TFLOPS fp16\u003c\/p\u003e\n\u003cdiv style=\"display: flex; gap: 16px; flex-wrap: wrap; margin-top: 24px;\"\u003e\n\u003cdiv style=\"background: rgba(51,79,180,0.12); border: 1px solid #334fb4; border-radius: 8px; padding: 14px 16px; text-align: center; flex: 1; min-width: 110px;\"\u003e\n\u003cdiv style=\"font-size: 24px; font-weight: 800; color: #334fb4;\"\u003e256 GB\u003c\/div\u003e\n\u003cdiv style=\"font-size: 11px; color: #555; text-transform: uppercase; letter-spacing: 1px;\"\u003eHBM2 pool\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv style=\"background: rgba(51,79,180,0.12); border: 1px solid #334fb4; border-radius: 8px; padding: 14px 16px; text-align: center; flex: 1; min-width: 110px;\"\u003e\n\u003cdiv style=\"font-size: 24px; font-weight: 800; color: #334fb4;\"\u003e900 GB\/s\u003c\/div\u003e\n\u003cdiv style=\"font-size: 11px; color: #555; text-transform: uppercase; letter-spacing: 1px;\"\u003eper-GPU bandwidth\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv style=\"background: rgba(51,79,180,0.12); border: 1px solid #334fb4; border-radius: 8px; padding: 14px 16px; text-align: center; flex: 1; min-width: 110px;\"\u003e\n\u003cdiv style=\"font-size: 24px; font-weight: 800; color: #334fb4;\"\u003e7.8 TFLOPS\u003c\/div\u003e\n\u003cdiv style=\"font-size: 11px; color: #555; text-transform: uppercase; letter-spacing: 1px;\"\u003eFP64 per GPU\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv style=\"background: rgba(51,79,180,0.12); border: 1px solid #334fb4; border-radius: 8px; padding: 14px 16px; text-align: center; flex: 1; min-width: 110px;\"\u003e\n\u003cdiv style=\"font-size: 24px; font-weight: 800; color: #334fb4;\"\u003eNo KYC\u003c\/div\u003e\n\u003cdiv style=\"font-size: 11px; color: #555; text-transform: uppercase; letter-spacing: 1px;\"\u003enot export-controlled\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cp style=\"margin-top: 20px; font-size: 15px; color: #aaa;\"\u003eEx-datacentre Volta, refurbished in-house. Availability is whatever the market has.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003cp style=\"font-size: 17px; color: #333; margin-bottom: 24px;\"\u003eAn eight-GPU Volta node sourced from the ex-datacentre market and refurbished by us: eight V100 SXM2 modules on an NVLink baseboard, 256 GB of HBM2 at 900 GB\/s per GPU, 300 GB\/s GPU-to-GPU. This is deliberately a budget machine. It is the cheapest way we know to get real NVLink-connected multi-GPU with 7.8 TFLOPS of FP64 per GPU into a lab, and for FP64 HPC and validated fp16 training codes it is still a perfectly good tool. For modern LLM serving it is not - see below, we are specific about why.\u003c\/p\u003e\n\u003ch2 style=\"font-size: 22px; font-weight: bold; margin: 40px 0 16px 0; padding-bottom: 8px; border-bottom: 3px solid #334fb4;\"\u003eHardware\u003c\/h2\u003e\n\u003ctable style=\"width: 100%; border-collapse: collapse; margin-bottom: 24px; font-size: 15px;\"\u003e\n\u003cthead\u003e\n\u003ctr style=\"background: #0d0d0d; color: #fff;\"\u003e\n\u003cth style=\"padding: 12px 16px; text-align: left;\"\u003eComponent\u003c\/th\u003e\n\u003cth style=\"padding: 12px 16px; text-align: left;\"\u003eDetail\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr style=\"background: #f8f8f8;\"\u003e\n\u003ctd style=\"padding: 10px 16px; font-weight: 600;\"\u003eGPUs\u003c\/td\u003e\n\u003ctd style=\"padding: 10px 16px;\"\u003e8x NVIDIA V100 SXM2 32 GB HBM2 (Volta, 300 W per module)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding: 10px 16px; font-weight: 600;\"\u003eVRAM pool\u003c\/td\u003e\n\u003ctd style=\"padding: 10px 16px;\"\u003e256 GB HBM2. 16 GB module variants give 128 GB - we will tell you which we have\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"background: #f8f8f8;\"\u003e\n\u003ctd style=\"padding: 10px 16px; font-weight: 600;\"\u003eMemory bandwidth\u003c\/td\u003e\n\u003ctd style=\"padding: 10px 16px;\"\u003e900 GB\/s per GPU\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding: 10px 16px; font-weight: 600;\"\u003eGPU interconnect\u003c\/td\u003e\n\u003ctd style=\"padding: 10px 16px;\"\u003eNVLink 2 hybrid-cube mesh, 300 GB\/s. No NVSwitch - not full any-to-any\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"background: #f8f8f8;\"\u003e\n\u003ctd style=\"padding: 10px 16px; font-weight: 600;\"\u003eCompute\u003c\/td\u003e\n\u003ctd style=\"padding: 10px 16px;\"\u003e1,000 TFLOPS fp16 aggregate | 62.4 TFLOPS FP64 aggregate\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding: 10px 16px; font-weight: 600;\"\u003eCPUs\u003c\/td\u003e\n\u003ctd style=\"padding: 10px 16px;\"\u003e2x Intel Xeon Scalable, dual socket\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"background: #f8f8f8;\"\u003e\n\u003ctd style=\"padding: 10px 16px; font-weight: 600;\"\u003eSystem memory\u003c\/td\u003e\n\u003ctd style=\"padding: 10px 16px;\"\u003e1 TB DDR4-2666 ECC RDIMM (16x 64 GB). Other capacities on request\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding: 10px 16px; font-weight: 600;\"\u003eStorage\u003c\/td\u003e\n\u003ctd style=\"padding: 10px 16px;\"\u003e2x 1.92 TB U.2 NVMe boot + 2x 3.84 TB U.2 NVMe data\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"background: #f8f8f8;\"\u003e\n\u003ctd style=\"padding: 10px 16px; font-weight: 600;\"\u003eChassis\u003c\/td\u003e\n\u003ctd style=\"padding: 10px 16px;\"\u003e4U rack-mount, datacentre-grade front-to-back airflow\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding: 10px 16px; font-weight: 600;\"\u003eRefurbishment\u003c\/td\u003e\n\u003ctd style=\"padding: 10px 16px;\"\u003eFresh thermal pads and paste, fan replacement, 72 h burn-in with written report\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003ch2 style=\"font-size: 22px; font-weight: bold; margin: 40px 0 16px 0; padding-bottom: 8px; border-bottom: 3px solid #334fb4;\"\u003eWhat it runs\u003c\/h2\u003e\n\u003cp\u003eVolta is fp16 and FP64 hardware. These are the workloads it is still genuinely good at.\u003c\/p\u003e\n\u003ctable style=\"width: 100%; border-collapse: collapse; margin-bottom: 24px; font-size: 15px;\"\u003e\n\u003cthead\u003e\n\u003ctr style=\"background: #0d0d0d; color: #fff;\"\u003e\n\u003cth style=\"padding: 12px 16px; text-align: left;\"\u003eComponent\u003c\/th\u003e\n\u003cth style=\"padding: 12px 16px; text-align: left;\"\u003eDetail\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr style=\"background: #f8f8f8;\"\u003e\n\u003ctd style=\"padding: 10px 16px; font-weight: 600;\"\u003eClassic HPC\u003c\/td\u003e\n\u003ctd style=\"padding: 10px 16px;\"\u003eCUDA \/ OpenACC, molecular dynamics, CFD, FEA at 62.4 TFLOPS FP64 per node\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding: 10px 16px; font-weight: 600;\"\u003efp16 training, validated codes\u003c\/td\u003e\n\u003ctd style=\"padding: 10px 16px;\"\u003eResNet\/BERT-era architectures and anything already tuned for Volta\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"background: #f8f8f8;\"\u003e\n\u003ctd style=\"padding: 10px 16px; font-weight: 600;\"\u003e13B-34B inference\u003c\/td\u003e\n\u003ctd style=\"padding: 10px 16px;\"\u003efp16. ~15-30 tok\/s single stream per model instance\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"padding: 10px 16px; font-weight: 600;\"\u003eTeaching and research clusters\u003c\/td\u003e\n\u003ctd style=\"padding: 10px 16px;\"\u003ereal multi-GPU NVLink behaviour for students on a fixed grant\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"background: #f8f8f8;\"\u003e\n\u003ctd style=\"padding: 10px 16px; font-weight: 600;\"\u003eV100 estate expansion\u003c\/td\u003e\n\u003ctd style=\"padding: 10px 16px;\"\u003enode parity with existing Volta hardware you already operate\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003cp style=\"font-size: 14px; color: #666;\"\u003e\u003cem\u003eThroughput figures are estimates derived from published vendor specifications and community benchmarks for this GPU generation, not measurements taken on this exact configuration. We will give you measured numbers for your model and context length before you commit.\u003c\/em\u003e\u003c\/p\u003e\n\u003ch2 style=\"font-size: 22px; font-weight: bold; margin: 40px 0 16px 0; padding-bottom: 8px; border-bottom: 3px solid #334fb4;\"\u003ePower and cooling\u003c\/h2\u003e\n\u003cp\u003eRoughly 3.2 kW sustained: 2.4 kW of GPU plus host. Lowest wall draw of our node range in absolute terms, but by far the worst performance per watt - around an eighth of an H100 node's fp16 throughput for a bit over half the power. Volta SXM2 was built for datacentre-grade front-to-back airflow and is unforgiving in a poorly ventilated rack.\u003c\/p\u003e\n\u003ch2 style=\"font-size: 22px; font-weight: bold; margin: 40px 0 16px 0; padding-bottom: 8px; border-bottom: 3px solid #334fb4;\"\u003eBe clear about what this is\u003c\/h2\u003e\n\u003cp\u003e\u003cstrong\u003eNo bf16, no fp8, no INT8 tensor cores.\u003c\/strong\u003e Volta has none of them. Most 2026 quantised inference stacks assume bf16 or better, so a large part of the current ecosystem either will not run or will run badly. Treat this as fp16 training and FP64 HPC hardware, not an LLM serving node.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eNo NVSwitch.\u003c\/strong\u003e NVLink 2 is a hybrid-cube mesh, not full any-to-any. Eight-way tensor parallelism is measurably worse here than on the A100, H100 and H200 nodes.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability is not guaranteed.\u003c\/strong\u003e There is no new-channel supply for this hardware. Every unit is individually sourced from the secondary market, so we quote on confirmed availability only and cannot promise a date before we have a unit identified.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eRefurbished, not new.\u003c\/strong\u003e This is six to eight year old hardware. Warranty is Kentino refurb terms, not manufacturer channel warranty. Degraded thermal pads are the main failure mode on these modules and every unit we ship has had them replaced.\u003c\/p\u003e\n\u003cp\u003eOne genuine advantage: V100 predates the Advanced Computing Item thresholds, so there is \u003cstrong\u003eno KYC form and no End User Certificate\u003c\/strong\u003e. If export paperwork is the thing blocking you, this is the node that does not need it.\u003c\/p\u003e\n\u003ch2 style=\"font-size: 22px; font-weight: bold; margin: 40px 0 16px 0; padding-bottom: 8px; border-bottom: 3px solid #334fb4;\"\u003eWhat you get from Kentino\u003c\/h2\u003e\n\u003cp\u003eKentino sources, refurbishes and supports this node: thermal pad and paste renewal, fan replacement, 72 hour burn-in with a written report, OS and driver stack pinned to a Volta-compatible CUDA version, rack and airflow guidance, EU-based logistics and our refurb warranty.\u003c\/p\u003e\n\u003cp\u003eWe run the same refurbishment process on ex-mining accelerator hardware, so this is an established bench workflow rather than a one-off.\u003c\/p\u003e\n\u003cp style=\"font-size: 14px; color: #666;\"\u003e\u003cem\u003ePrice shown is EUR excluding VAT, \u003cstrong\u003eEXW Prague\u003c\/strong\u003e (Incoterms 2020). Collection, freight, insurance and any destination import duties are for the buyer’s account. Lead time on request.\u003c\/em\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003ePriced per sourced unit — these come from the secondary market, so availability drives the date. Tell us what you need it for - if your workload is modern LLM inference we will point you at the A100 node instead, which is better value than this once you account for what Volta cannot do.\u003c\/em\u003e\u003c\/p\u003e\n\u003c\/div\u003e","brand":"Kentino","offers":[{"title":"Default Title","offer_id":54417301045576,"sku":null,"price":195964.0,"currency_code":"EUR","in_stock":true}],"url":"https:\/\/kentino.com\/el\/products\/k-node-v100-8x-sxm2","provider":"Kentino","version":"1.0","type":"link"}