NVIDIA Tesla V100 GPU Server | RedSwitches
// gpu compute

NVIDIA Tesla V100 GPU Server

Up to 130 TFLOPS DL Performance, HBM2 Memory & NVLink 2.0. Proven Volta Power for AI Training, Research & Multi-GPU Scaling.

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GPUTesla V100

NVIDIA Tesla V100 GPU Server Price

Single and multi-GPU V100 builds on bare metal. No setup fee, full root access, and unmetered 1/10/25 Gbps uplinks.

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Bare Metal // Standard Equipment

All Bare Metal Plans Include

Every server ships fully dedicated: no shared resources, no usage meters, no surprises.

Setup Cost
Free
Provisioning
Instant & Automated
Access
KVM, IPMI, Root
Protection
DDoS Shield Included
Cores
Up to 128 (Dual Socket)
Memory
Up to 2TB RAM
Storage
Enterprise NVMe & SSD
Support
24/7/365 Human Engineers
OS & Panels
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* Select your OS and panel at checkout

NVIDIA Tesla V100 Core Specifications

Volta GV100 silicon with up to 32 GB HBM2, first-gen Tensor Cores, and NVLink 2.0 for multi-GPU scaling.

GPU Architecture
NVIDIA Volta GV100, 12 nm, 21.1B transistors, Volta SMs with first-gen Tensor Cores
CUDA / Tensor Cores
5,120 CUDA cores, 640 Tensor Cores capable of 130 TFLOPS deep-learning performance
Memory
16 GB or 32 GB HBM2, 4,096-bit bus, ~900 GB/s bandwidth
Performance Highlights
BERT inference
557 sentences/sec vs CPU 23.5/sec (24× speedup)
Maximum Efficiency Mode
Up to 40% more rack compute at 50% power
NVLink
Up to 300 GB/s per link, enabling scalable 4-8 GPU nodes
TDP & Form Factor
Dual-slot PCIe, 250-300 W, passive cooling

Why Choose V100

A proven Volta building block: power-efficient rack scaling, NVLink multi-GPU, and deep CUDA/HPC integration.

Legacy-standard for AI & HPC

First GPU to exceed 100 TFLOPS, delivers 12× Tensor FLOPS training, 6× inference over Pascal.

Power-efficient rack scaling

Maximum Efficiency Mode boosts rack throughput by 40% under constrained power.

Proven multi-GPU building block

NVLink enables efficient scaling and peer-to-peer access.

Deep integration with CUDA/HPC ecosystem

Supports CUDA, cuDNN, PyTorch, TensorFlow, MPI, and HPC libraries.

Cost-effective today

More affordable than newer GPUs; ideal for legacy or budget-aware infrastructure.

Ideal Use Cases

From deep-learning training to RAPIDS pipelines, see where the Tesla V100 changes what your team can ship.

Deep Learning Training & Inference

Best for BERT, translation, smaller GPT releases, significantly faster than CPU clusters.

HPC & Scientific Computing

Ideal for CFD, molecular dynamics, and simulation where double precision and Tensor Core support matter.

Multi-GPU Research & Scalability Testing

NVLink connectivity enables early prototyping for larger A100/H100 cluster workloads.

Data Science & RAPIDS Pipelines

Accelerates GPU-optimized ETL, graph analytics, and data transformations with RAPIDS and Spark.

Cloud Repatriation & Cost Efficiency

Replace costly cloud GPU instances with reliable bare-metal performance at predictable cost.

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Deep Dive & FAQs

Common questions about V100 memory variants, clustering, power, and present-day value.

Can I mix 16 GB and 32 GB variants?

Yes, boards support mixed configurations, though memory pooling is limited.

Can V100 cluster at scale?

Yes, NVLink 2.0 enables up to 300 GB/s bandwidth; 4-8 GPU clusters are standard.

Is power usage an issue?

Not with optimized infrastructure, Maximum Efficiency Mode boosts rack output by 40%.

Still useful today?

Absolutely, great for multi-GPU testing, RAPIDS, and smaller AI workloads on a budget.

How many can fit per server?

Server-grade PCIe chassis typically fit 4-8 dual-slot V100 GPUs with adequate cooling and power.

Not Sure Exactly What You Need

No problem. Our talented engineers will consult, architect, migrate, manage, and do whatever it takes to help your business grow and succeed.