NVIDIA Tesla V100 16 GB/32 GB GPU infrastructure in configurations from 1 to 8 GPUs, suited to AI inference, training, HPC and rendering.
The NVIDIA V100 is a good fit for inference, training, HPC and rendering when you need a stable GPU, sufficient VRAM and controlled cost.
The V100 GPU for AI inference, training, HPC and compute-intensive workloads.
V100 16 GB and V100 32 GB configurations are available at various GPU counts.
The multi-GPU configurations support NVLink at up to 50 GB/s for fast data exchange.
Options from 16 GB to 128 GB of VRAM for AI models, rendering and HPC.
Tesla V100 16 GB
Tesla V100 16 GB
Tesla V100 16 GB
Tesla V100 16 GB
NVIDIA V100 32G
NVIDIA V100 32G
NVIDIA V100 32G
NVIDIA V100 offers options from single GPU to multi-GPU, scaling with your AI and HPC deployment needs.
The V100 suits workloads that need a powerful GPU on a smaller budget than newer generations.
Choose 1, 2, 4 or 8 GPUs based on inference, training and parallel processing needs.
The configuration uses Intel Xeon Gold 6342 CPUs, with RAM and storage balanced for GPU workloads.
The multi-GPU plans include peer-to-peer NVLink to accelerate workloads with GPU-to-GPU traffic.
Billing cycles from 1 month to 60 months.
HiTechCloud advises on drivers, runtime, AI frameworks and the right configuration.
Deploy the V100 for model training, fine-tuning, embeddings and AI applications.
Optimized for inference, batch processing and production AI services at a reasonable cost.
Accelerate simulation, rendering, scientific computing, and data-intensive processing.
Powerful capacity, optimized to accelerate AI/ML and high-performance workloads at any scale.
Training, inference, or fine-tuning - HiTechCloud offers a range of GPUs to match your needs, with transparent pricing and on-demand deployment environments.
HiTechCloud GPU instances combine NVLink/PCIe, InfiniBand (RDMA), and RAIL topology to optimize AI/HPC performance.
A network and GPU fabric designed so AI workloads scale reliably, easing bandwidth bottlenecks and holding performance at scale.
High-speed GPU-to-GPU connectivity within and between nodes, reducing bottlenecks during model training.
Low-latency connectivity optimized for distributed training and reduced processing load on the host.
A parallel network architecture delivering higher bandwidth, redundancy, and consistent performance at any scale.
From a single GPU to large clusters, HiTechCloud provisions resources ahead of demand and gets the most out of every instance.
Scale GPU resources automatically from a handful to thousands of GPUs, with forecasting and provisioning ahead of demand.
Access over SSH, TCP, and HTTP with built-in protection layers that keep enterprise data safe and access fully controlled.
Partition a single GPU into multiple independent instances to run AI workloads in parallel, improving utilization and lowering infrastructure cost.
From large-scale training to real-time inference - pay only when you actually run, on a GPU cloud platform built for every AI and high-performance workload.
From sign-up to a running GPU instance in under 5 minutes - no complex setup, no resource reservations, no charges while idle. Just deploy, run, and pay only for what you use.
A complete management platform for deploying and operating AI agents securely at scale on enterprise-grade infrastructure.
Explore AgentBaseA unified platform for training, fine-tuning, and deploying AI models at any scale.
Explore AI PlatformSupports fast search, real-time analytics, log and large-scale event data, and vector databases for RAG.
Explore Vector DatabaseManaged Kubernetes for container orchestration, AI services, and GPU cloud workloads.
Explore KubernetesDeploy systems and applications closer to your users, reducing latency and meeting local regulatory requirements.
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Large enterprises and fast-growing startups choose HiTechCloud for secure, high-performance AI cloud solutions that let them innovate and scale.
The HiTechCloud team advises on GPU architecture, networking, security, and an operating model matched to your real workloads.
Quick facts before choosing an NVIDIA V100 configuration at HiTechCloud.
Suited to AI training, fine-tuning, inference, HPC, rendering and workloads that need a reliable GPU at controlled cost.
V100 16 GB suits inference, experimentation and small training runs. V100 32 GB suits workloads that need more VRAM.
Yes. The V100 still suits mid-sized training, inference, HPC, data science, and workloads that need a data center GPU at a lower cost.
Choose the V100 when the workload does not require A100 performance but still needs a stable GPU for AI, HPC, or mid-sized experiments.
Yes. HiTechCloud advises on the CUDA environment, frameworks, containers and how to move workloads onto cloud GPU.
Plans are available on 1-month, 3-month, 6-month, 12-month, and long-term cycles.
HiTechCloud helps you choose the GPU count, VRAM, CPU cores, storage, runtime, and framework that fit your budget.
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