HPC performance without HPC overhead

Compute with Hivenet offers GPU and CPU instances for suitable high-performance computing workloads without buying local hardware. Its documented RTX 5090 family has 32GB of VRAM per GPU; RTX 4090 is retired from new workloads. The distributed Compute platform for AI and HPC provides infrastructure, while users configure their software, data and operating requirements.

Finally, an HPC solution built for modern AI and research teams

If you’re trying to train models, run advanced simulations, render high-resolution scenes, or process massive data volumes, you should not have to choose between a traditional HPC infrastructure project and expensive cloud HPC capacity. Most teams need computing power now, not a months-long buying cycle, a queue on institutional hpc systems, or spot instances that can disappear mid-run.

Compute with Hivenet supports AI, research, simulation and rendering workloads that fit its available GPU or CPU presets. Choose a container or virtual machine, review the active price and capacity, and configure the application stack. Read why developers choose Compute with Hivenet for further context, then test the requirements of the specific job.

High-Performance Computing (HPC) solutions can perform quadrillions of calculations per second, significantly surpassing the capabilities of average desktop computers. Supercomputing is capable of performing quadrillions of calculations per second, significantly surpassing the capabilities of average desktop computers. But not every team needs a full supercomputing center, high speed interconnects, or a custom scheduler to get useful high performance computing capabilities.

Many modern hpc workloads are GPU-heavy, iterative, single-node, or loosely parallel. They need reliable memory bandwidth, high processing speed, dedicated compute capacity, and predictable billing more than they need institutional complexity. Compute with Hivenet gives you HPC-style performance for hpc and ai workloads such as machine learning, artificial intelligence, data analytics, computational fluid dynamics, rendering, benchmarking, and scientific research.

Compute with Hivenet provides access to current GPU presets, including documented RTX 5090 configurations. The provider handles the underlying infrastructure, but application deployment, dependencies, licensing, backups and workload monitoring still need an owner.

Why Compute with Hivenet works

Evaluate the following parts of the service against the requirements of the workload:

• GPU configuration – Review the selected preset’s GPU count, VRAM, CPU, system RAM, disk and bandwidth. Hardware allocation alone does not guarantee constant throughput or uninterrupted execution.
• Published pricing – RTX 5090 pricing starts at €0.75 per hour. Eligible usage is billed per second; confirm the current configuration and active price in the console.
• On-demand lifecycle – Launch when the selected preset has capacity. Stop/start preserves an environment temporarily but releases hardware capacity; restarting is subject to availability.
• Runtime choice – Use a prepared container template for supported application environments, or a virtual machine when full OS-level control and system packages are required. Container root access is disabled.
• Support requirements – Review the available support channels and agree any response-time or operational requirements before relying on the service for a critical workload.

High performance computing hpc often depends on massively parallel computing. HPC systems often utilize massively parallel computing, which involves running multiple tasks simultaneously across numerous processors or computer servers, enhancing computational efficiency. Supercomputers utilize massively parallel computing, which involves running multiple tasks simultaneously across tens of thousands to millions of processors or processor cores.

Compute with Hivenet does not pretend to replace every full-scale supercomputing environment. If your hpc applications require MPI-scale architecture, HBM-heavy A100/H100 clusters, high performance file system tuning, or specialized high speed interconnects, a traditional data center or hyperscaler may still be the right fit. For GPU-first workloads that fit the available hardware, test RTX instances against the required runtime, memory, reliability and total cost.

Compare Hivenet with the specific Microsoft Azure, Google Cloud or AWS configuration under consideration. Include quotas, capacity, software, networking, storage, support and the cost of completing the same job.

How it works

Getting started depends on the application and runtime. Use the following steps to assess and configure a suitable instance.

Step 1: choose your GPU instance

Select the GPU that matches your VRAM, performance, and budget needs.

The RTX 4090 hardware guide describes a card that Hivenet no longer offers for new Compute workloads. For current rentals, review the RTX 5090 options and the active console preset. Check memory, CPU, disk, bandwidth and capacity before selecting a configuration.

Compute instances use on-demand billing. Long-running jobs need sufficient credits, external checkpoints and a recovery plan. Do not treat an on-demand instance or a stopped environment as a permanent capacity reservation.

Step 2: deploy your workload

Choose a virtual machine if the workload requires sudo, kernel changes or full OS-level control. A container can use a prepared template, but root access is disabled. Connect using the console’s access details, then verify application dependencies, driver compatibility, data paths and software licenses before running the job.

HPC as a service (HPCaaS) allows organizations to access high-performance computing resources via the cloud, providing a scalable and cost-effective solution for complex computational tasks that increasingly depend on GPUs in modern computing for AI and research. Compute with Hivenet applies that model to high-quality dedicated GPU compute: no hardware ownership, no long procurement cycle, and no need to build hpc infrastructure before you can process data.

Step 3: scale and monitor

Launch additional instances when capacity allows, and monitor runtime and credits. The console displays estimated hourly costs, while eligible usage is charged per second. More instances do not automatically make one application run faster.

The documented GPU family supports multi-GPU configurations up to eight RTX 5090 GPUs per instance when capacity is available. Software must explicitly support multi-GPU work, and VRAM is not automatically pooled. For cross-instance or tightly coupled jobs, verify network reachability, bandwidth, synchronization and storage requirements before committing to an architecture.

Plan around available capacity, the running balance and the instance lifecycle. Checkpoint important work outside the instance and test recovery; no cloud instance should be described as immune to interruption.

What makes us different

Compute with Hivenet is one option for teams comparing GPU access, operating requirements and cost. The guide to renting GPUs for AI with flexible cloud solutions can help frame that comparison; use workload measurements to make the decision.

• Resource allocation – Check whether the selected service uses full-device, partitioned or shared resources, and test the resulting performance. Hivenet’s console lists the resources for each available preset.
• Price and lifecycle – Compare the active on-demand rate with the exact alternative being considered. Interruption policy, restart capacity and recovery costs matter alongside the GPU price.
• Included resources – Hivenet’s standard instance price includes the listed storage and network volume. Separate services, unusual storage requirements and software-license entitlements need their own cost and terms review.
• Infrastructure and impact – Hivenet uses distributed, Policloud-backed infrastructure. Environmental comparisons must specify the product, workload, baseline, electricity mix and other assumptions; distribution alone does not establish an energy saving.
• Workload fit – Compare memory, interconnect, storage, software and operating requirements. A suitable rented instance can avoid a local hardware purchase, but it does not remove application or system-administration responsibilities.

HPC high performance computing plays a major role in multiple industries, from life sciences and weather prediction to oil and gas exploration, space exploration, quantum computing research, medical record management, algorithmic trading, and molecular modeling. But many teams working on these complex problems do not need to own a supercomputer or manage a room full of Intel Xeon processors to make progress.

Microsoft Azure, Google Cloud and other providers offer services for a range of HPC requirements. Compare their configurations with Hivenet’s available presets and the support, networking, compliance and operating requirements of the project.

Compare the infrastructure choices against the work you need to complete.
Assign responsibility for software, data, access and recovery before deployment.

How to evaluate the service

The following questions are evaluation prompts, not customer testimonials. Measure completed workloads, runtime variation, total spend, recovery effort and iteration time.

Does the configuration run your job with acceptable runtime variation and total cost?

• Evaluation focus: training runtime and cost

Can you recover a simulation from an external checkpoint after an instance interruption?

• Evaluation focus: checkpoint recovery

Compare the full configuration and cost of completing the same workload. A100 and H100 data center GPUs differ from RTX hardware in memory and interconnect options, so their hourly prices do not establish equivalent performance or cost. Hivenet’s public RTX 5090 pricing starts at €0.75 per hour; confirm the active preset and review how Hivenet billing and instance rental work.

HPC and AI infrastructure serves research, simulation and application development. For a purchasing decision, assess the resources and operating requirements of the actual workload rather than relying on an uncited market-size estimate.

The technical impact is just as important. High-Performance Computing (HPC) accelerates research by enabling scientists to process vast amounts of data and perform complex calculations at unprecedented speeds, which is essential for tackling previously intractable problems across various disciplines.

HPC can reduce data processing times from weeks to hours, allowing researchers to gain deeper insights into diseases such as cancer and Alzheimer’s, thereby accelerating the pace of medical research and discovery.

The emergence of exascale computing represents a significant technological shift, enabling organizations to process massive amounts of data and tackle complex workloads at unprecedented speeds. Compute with Hivenet brings the same buyer logic to practical cloud hpc: get the computing resources you need, reduce overhead, and keep your team focused on the work instead of the infrastructure.

Proof can come from your own benchmarks, too. Run your training job, rendering queue, CFD case, data analytics workflow, or inference workload on a dedicated instance and compare:

• Cost per completed run
• Runtime consistency
• GPU utilization
• VRAM headroom
• Retry rate
• Engineering time spent managing infrastructure

That is the metric that matters: not just cost per hour, but cost per useful completed workload.

Who it’s for

Compute with Hivenet is an option for teams whose workload fits the available GPU or CPU configurations and whose operating requirements match the service terms.

• AI researchers and ML teams training foundation models, fine-tuning custom models, running inference, benchmarking architectures, or developing artificial intelligence workflows that need stable NVIDIA GPUs.
• Simulation engineers running computational fluid dynamics, finite element analysis, molecular dynamics, advanced simulations, and complex simulations where repeatability and processing speed matter.
• Creative studios rendering 3D content, ray-traced scenes, animation frames, high-resolution media, and batch video workloads without fighting shared GPU contention.
• Data science and computer vision teams processing massive data volumes, building data analytics pipelines, running feature extraction, and scaling data analysis across reliable GPU instances.
• Research institutions that need hpc resources for scientific research without waiting for institutional hpc clusters, procurement cycles, or specialist hpc infrastructure upgrades.
• Financial teams running risk analysis, fraud detection, algorithmic trading research, economic simulation, portfolio modeling, or other complex calculations.
• Industrial and engineering teams in automotive, aerospace, discrete manufacturing, energy, and life sciences that need fast experimentation without owning permanent compute infrastructure.

High-Performance Computing (HPC) is transforming the life sciences industry by accelerating drug discovery, genomic research, and personalized medicine, enabling faster development of new treatments and better understanding of diseases.

HPC applications are prevalent in energy, where they are used to model complex energy systems, simulate renewable energy sources, and optimize power grids for efficiency and reliability.

In the automotive industry, HPC is utilized to simulate and optimize product designs and processes, including computational fluid dynamics (CFD) applications that analyze fluid flows to improve aerodynamics and battery performance.

High-Performance Computing (HPC) enables manufacturers to optimize production processes by identifying bottlenecks, predicting equipment failures, and improving energy efficiency.

The adoption of HPC systems with computer-aided engineering software for high-fidelity modeling and simulation is on the rise in industries such as automotive, aerospace, and discrete manufacturing.

HPC can significantly reduce the time required for Computational Fluid Dynamics (CFD) simulations, allowing manufacturers to experiment with more parameters and achieve faster, more accurate results.

High-Performance Computing (HPC) is a cornerstone of the financial services industry, enabling complex calculations, risk modeling, and fraud detection at unprecedented speeds.

Financial institutions harness HPC to process vast datasets, identify market trends, optimize investment portfolios, and simulate economic scenarios.

HPC is increasingly used in financial services for risk analysis, allowing firms to model the impact of hypothetical portfolio changes for better decision-making.

Before choosing Compute with Hivenet, run a representative test and check software compatibility, memory, networking, security, data retention and recovery requirements.

Pricing & plans

Choose the GPU setup that matches your workload.

RTX 4090: retired from new Compute workloads

RTX 4090 remains hardware to evaluate for compatible AI, rendering, analytics and simulation workloads where it is already owned or offered by another provider.

Its 24GB VRAM is a hardware specification, not a current Hivenet rental configuration. Runtime, access rights and billing depend on the service where the card is used.

No current RTX 4090 rental offer

Keep RTX 4090 hardware comparisons separate from Hivenet availability. For a new Hivenet workload, review the current GPU presets, including the documented RTX 5090 family.

RTX 5090 instances - for demanding applications

Evaluate RTX 5090 for models, simulations, rendering and data-processing jobs that fit its memory and supported software. Use a representative workload to establish runtime and cost.

The documented RTX 5090 family has 32GB of VRAM per GPU. Check the preset for attached CPU, RAM, disk and bandwidth. Choose a virtual machine for full OS-level control; container root access is disabled.

From €0.75/hour; confirm the active console price

Compare RTX 5090 with alternatives using memory fit, software support and cost per completed job. More VRAM or a lower hourly rate alone does not establish the best configuration.

Discuss larger workloads

For multi-instance workloads, long-term research, recurring batch jobs or production services, contact the team to assess the required configuration and operating arrangements before committing.

Confirm capacity, networking, software support, data retention and any service commitments explicitly. Do not infer a custom configuration or support agreement from the standard instance offer.

Contact for volume pricing and specialized requirements.

Start with a configuration that fits a representative workload. If requirements grow, review the available presets and discuss capacity or architecture needs before expanding.

Frequently asked questions

How does this compare to AWS or Azure GPU instances?

Compute with Hivenet offers GPU and CPU instances with console-listed configurations and usage pricing. Suitability depends on the workload, capacity and operating requirements.

Compare Hivenet with the exact AWS, Microsoft Azure or Google Cloud service being considered. Review GPU memory, interconnect, region, capacity, lifecycle, storage, network volume and support, then test the same workload. Avoid treating an entire provider as one pricing or reliability model.

For some workloads, A100 or H100 data center GPUs remain the better choice, especially when you need larger HBM memory, high speed interconnects, or tightly coupled multi-node scaling. For workloads that fit the documented RTX 5090 presets, test their performance and cost against those alternatives. RTX 4090 is no longer available for new Hivenet workloads.

Can I run multi-GPU workloads?

The documented RTX 5090 family supports up to eight GPUs per instance when capacity is available. Applications must support multi-GPU execution; VRAM is not automatically pooled. For multiple instances, verify networking, synchronization, storage and software requirements instead of assuming cluster-style performance.

The important trade-off is communication intensity. Loosely parallel workloads often scale well across multiple GPU instances. Tightly coupled hpc workloads that require specialized interconnects, synchronized MPI-style execution, or a high performance file system may be better suited to traditional hpc clusters or specialized cloud hpc platforms.

If you are unsure, talk to the Compute with Hivenet team about your workload pattern, model size, data movement, and scaling goals.

What about data transfer and storage?

Standard instance pricing includes the storage and network volume listed for that preset. Check those amounts and the terms of any separate service; included network volume is not a promise of unlimited transfers.

Keep important datasets, model checkpoints and outputs backed up outside disposable instance storage. Stopping preserves an environment only within the documented lifecycle; termination deletes local data. For retained data or separate services, review Hivenet storage and transfer pricing and confirm the applicable terms before running the job.

That clarity matters because hidden transfer, storage, and licensing costs can turn a low advertised GPU price into an expensive final bill.

Is this suitable for production workloads?

Production suitability depends on the application and operating plan. Configure monitoring, sufficient credits, access controls, external backups, checkpoints and tested recovery, and confirm any required service commitments.

On-demand billing does not guarantee uninterrupted execution or permanent capacity. Hardware faults, lifecycle rules, insufficient credits and availability can affect a workload, so long-running jobs and services need a recovery plan.

For regulated enterprise deployments, specialized compliance needs, or mission-critical architectures, discuss requirements directly with the team. Compute with Hivenet is strongest when you need accessible, stable, dedicated GPU compute without the full weight of traditional hpc systems or hyperscaler complexity.

Get started today

Before launch, check the workload’s resource needs, budget, software and recovery requirements against the available console configurations.

Evaluate current GPU presets, including RTX 5090 with public pricing from €0.75 per hour. Confirm the active price and capacity, select a suitable container or virtual machine, and test the workload before scaling. RTX 4090 is retired from new Hivenet workloads.

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Use a representative pilot to validate the configuration and operating requirements before expanding.

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