NVIDIA Tesla P100 16GB PCIe HPC Accelerator

Budget high-performance and scientific computing. Its strengths are 16GB of HBM2 memory at 732 GB/s, high bandwidth for its price, and strong FP64 double-precision performance, which scientific and engineering computing depends on. That makes it a fit for FP64-heavy simulation, research and teaching clusters on a budget, and labs maintaining or expanding existing P100 deployments.

No. The P100 is a 2016-generation card and predates Tensor cores entirely, the tensor acceleration that defines current AI hardware was introduced on Volta in 2017, after the P100. It cannot train or serve modern large models competitively. For AI training, the Hopper and Blackwell systems in this catalog are the tools, and the A10 for inference. The P100's honest role is budget FP64 and HBM2 compute, not modern AI.

16GB of HBM2 at 732 GB/s on a 4096-bit interface is genuinely high memory bandwidth, and unusual to find at the P100's current secondary-market price. For bandwidth-bound compute, scientific workloads and simulations that move large amounts of data, that bandwidth is the P100's most valuable remaining attribute, alongside its FP64 capability.

It is one of the P100's two real strengths. The Pascal Tesla line delivered strong FP64 performance, the high-accuracy numerical format that computational physics, CFD, simulation, and much scientific computing require. Many newer consumer and even some professional cards have weak FP64; the P100's double-precision capability is a reason it still suits HPC workloads that need it.

Pascal architecture, 3,584 CUDA cores, 16GB of HBM2 at 732 GB/s on a 4096-bit interface, 9.3 TFLOPS of FP32, a 250W TDP (300W maximum), in a dual-slot, full-height data center form. Note it has no Tensor cores, it predates them. MillionMiner confirms the card and its condition in the quote.

Given the Pascal generation, units are secondary-market. MillionMiner verifies each card and confirms its condition in the quote, the same discipline applied across the catalog's prior-generation inventory. As a data center accelerator with HBM2 and no moving parts on the GPU package itself, a verified P100 within spec is a reasonable secondary-market buy for its role.

t is generations behind. The A10 is a modern Ampere inference and VDI card with Tensor cores; the Hopper and Blackwell systems are current AI training silicon with HBM3 and HBM3e and tensor throughput the P100 cannot approach. The P100 is chosen instead for budget HPC, FP64 work, or P100-fleet maintenance, not to compete with modern accelerators on AI. MillionMiner matches the card to the workload.

Light or legacy AI work on its CUDA cores, yes, but without Tensor cores it is far slower at modern AI math than any current accelerator, and its 16GB suits only smaller models. It can run older or smaller workloads and CUDA-based compute, but for any serious or current AI training or inference, the Hopper, Blackwell, or A10 hardware is the correct tool. Be realistic about the generation gap.

A 250W TDP with a 300W maximum, in a dual-slot full-height form that fits standard data center racks. As a passively-cooled data center card, it relies on server chassis airflow rather than an onboard fan, so it needs a host built for data center GPU cooling. MillionMiner confirms the host and cooling fit in the quote.

Submit your workload, HPC, FP64 compute, or P100-fleet maintenance, and quantity through the quote form. A MillionMiner specialist confirms the card, its condition, the host and cooling fit, and delivery. Every unit is verified before shipment and shipped worldwide DDP with duties handled, with hosting available, and the specialist will tell you honestly if modern hardware better fits your workload.