Cœurs CUDA RTX 4090 : 16 384 cœurs pour le calcul GPU dédié

16,384 CUDA cores and 24GB VRAM: RTX 4090 hardware explained

The RTX 4090 has 16,384 CUDA cores and 24GB of GDDR6X memory for parallel GPU compute. Hivenet has retired its RTX 4090 fleet, so this hardware guide is not an offer to rent a new RTX 4090 instance. Check the current Compute GPU reference and live console for available presets.

CUDA stands for Compute Unified Device Architecture. CUDA cores are the foundational hardware units inside an NVIDIA GPU designed to execute mathematical calculations in parallel, primarily standard single-precision floating-point (FP32) and integer calculations. On the NVIDIA GeForce RTX 4090, those CUDA cores sit inside the NVIDIA Ada Lovelace architecture with a Compute Capability of 8.9, making the GPU a serious choice for AI inference, rendering, simulation, data science, and CUDA development.

This page is built around usable performance, not just specs. The performance you see depends on CUDA cores, Tensor Cores, RT Cores where the application uses them, clock speed, VRAM capacity, memory bandwidth, software support, and resource allocation. For Compute with Hivenet, review the current GPU preset and active price before launch.

Why you’ll love RTX 4090 cuda performance

• Massive parallel processing – The NVIDIA GeForce RTX 4090 has 16,384 CUDA cores, compared with 10,496 on the RTX 3090. This increases available parallel hardware, but application speed also depends on memory, software and the workload.
• 24GB VRAM – The RTX 4090 has 24GB of GDDR6X memory and 1008GB/s of memory bandwidth. These are hardware specifications, not a current Hivenet rental allocation.
• Cloud access – Renting a current GPU preset avoids a local hardware purchase, cooling setup and PC build. Availability, provisioning and software startup depend on the selected configuration; RTX 4090 is no longer offered for new Compute workloads.
• Cost comparison – Check the current preset price and resource charges before launch. Compare the full workload cost, utilization and service terms across GPU rental services for AI workloads, rather than using the retired RTX 4090 rate as a live quote.
• Workload continuity – For persistent workloads, benchmarking, AI inference, PyTorch, TensorFlow, rendering and training, check the service terms and plan checkpoints and recovery. Hardware specifications alone do not guarantee uninterrupted execution.

The RTX 4090 supports DLSS 3 frame generation and Ada graphics features such as shader execution reordering, an optical flow accelerator and third-generation RT Cores. These features accelerate supported graphics tasks. They do not establish a general speedup for arbitrary CUDA or machine-learning workloads.

RTX 4090 hardware and current Compute options

When comparing a GPU rental, check whether the advertised resources are dedicated or shared, the available VRAM, interruption terms and the complete price. For Hivenet, use the current console configuration rather than assuming the retired RTX 4090 offer still applies.

• GPU resources – An RTX 4090 has 16,384 CUDA cores and 24GB of GDDR6X memory on a 384-bit bus. Memory bandwidth and capacity can still limit a workload; a full card does not eliminate bottlenecks.
• Rental and ownership costs – Check a current rental quote and a current hardware quote separately. Ownership also brings power, cooling, warranty, depreciation and setup costs; renting replaces the purchase with ongoing resource charges.
• Available configurations – Select an available preset and software environment, then allow time for provisioning and workload startup. Review the current options through Compute with Hivenet.
• Support and operations – Compute gives you control over your runtime and applications, which you operate and configure. Consult the documentation and Compute billing and instance rental FAQs, and confirm support scope for your environment.

An RTX 4090 and an A100 can produce different results across workloads, precision modes, model sizes and serving configurations. A fixed “70–90% of A100 performance at one-sixth the cost” is not established for most machine-learning tasks. Compare a stated benchmark and current total cost; memory capacity and interconnect requirements can change the choice. The discussion of why developers choose RTX 4090 over A100 for AI workloads should be read with those limits.

How cuda cores power your workloads

1. Parallel execution
   CUDA cores process thousands of threads simultaneously for AI, rendering, simulation, and scientific computing. CUDA cores primarily handle standard single-precision floating-point (FP32) and integer calculations, which makes them useful for preprocessing, custom kernels, physics, image operations, data transforms, and the general compute that surrounds deep learning.
2. Memory coordination
   CUDA cores work with 24GB GDDR6X memory and 1008GB/s memory bandwidth. Tensor Cores accelerate supported matrix operations, while RT Cores accelerate ray-tracing tasks such as bounding-volume traversal and ray-triangle intersection testing. Software determines which engines a workload uses.
3. Scalable results
   From single-model AI inference to batch rendering and CUDA development, the RTX 4090 adapts to workload size. With 24 GB of VRAM, the RTX 4090 can handle selected smaller models and machine-learning tasks. Quantization and efficient memory use can extend what fits, while larger models may need more VRAM, multiple GPUs, offloading, or a different hardware path. The RTX 4090, RTX 5090, and A100 Llama 3.1 8B results provide one BF16 serving comparison; CUDA core counts alone do not predict serving performance.

Real-world performance is not automatic just because the number of cores is high. Real-world scaling of game performance is constrained by external factors, meaning doubling the number of CUDA cores does not automatically double performance. The same principle applies to AI and compute: CPU preprocessing, storage I/O, memory bandwidth, framework support, quantization, clock behavior, and whether the workload is memory-bound can all become the bottleneck.

Technical specifications

• CUDA Cores: 16,384 on the Ada Lovelace architecture
• Compute Capability: 8.9, making the RTX 4090 highly suitable for CUDA development tasks
• Memory: 24GB GDDR6X
• Memory Bandwidth: 1008 GB/s
• Memory Bus: 384-bit
• Tensor Cores: 512 4th generation Tensor Cores
• RT Cores: 128 ray tracing cores
• FP32 shader performance: Up to 82.6 TFLOPS of theoretical non-Tensor FP32 throughput at the reference boost clock. Tensor Core throughput is a separate, precision-dependent measure; neither figure is an application benchmark.
• Architecture: NVIDIA Ada Lovelace architecture, which enhances performance and efficiency in graphics processing
• Ray tracing: 128 third-generation RT Cores accelerate supported ray-tracing operations. Architecture-level throughput gains do not guarantee the same speedup in every game or application.
• Local hardware power: NVIDIA lists 450W total graphics power for the reference RTX 4090. Actual power draw depends on the card and workload.
• Local PSU requirement: NVIDIA lists 850W required system power for a reference PC using a Ryzen 9 5900X. Requirements vary with the system configuration and graphics-card design.
• Local connector requirement: NVIDIA specifies three PCIe 8-pin power cables through the included adapter, or a PCIe Gen 5 cable rated for 450W or more. Follow the card and PSU manufacturers’ installation instructions.
• Compute with Hivenet Power: Managed infrastructure, no PSU planning, no cooling buildout, and no local power consumption management
• Current Compute pricing: RTX 4090 is retired for new workloads. Review the active price and resource allocation for a currently launchable preset.
• Current alternative: RTX 5090 cloud GPUs with 32GB VRAM. Confirm the available preset, location and price in the console.

The RTX 4090 has 16,384 CUDA cores, compared with 10,496 on the RTX 3090, and uses NVIDIA’s Ada Lovelace architecture. That comparison describes hardware, not a universal application-speed ranking. Compare memory requirements, software support and measured workload results when reviewing the best AI GPUs for 2026 ML workloads.

One market note: historical hardware prices are not current purchase or rental quotes. Check the specific card, region, resource configuration and service terms before estimating the cost of a workload.

Who should use RTX 4090 cuda cores

Ideal for:

• AI researchers training smaller models or using parameter-efficient fine-tuning when the complete memory footprint fits. Full training also needs gradients, optimizer state, activations and buffers; 24GB does not establish that full 7–13B training will fit.
• Data scientists running PyTorch, TensorFlow, RAPIDS, notebooks, and GPU-accelerated experiments who want to understand how to rent compute for AI workloads
• Developers deploying AI inference for LLMs, embeddings, computer vision, and generative workflows
• Computer vision teams processing large image datasets with fast GPU memory and high parallel throughput
• CUDA developers testing kernels, optimizing algorithms, and using Compute Capability 8.9 features
• 3D artists rendering complex scenes, ray tracing previews, simulations, and high-resolution outputs
• Gamers, creators, and technical users benchmarking GeForce RTX performance, NVIDIA Broadcast workflows, multi monitor setups, DLSS 3, and frame generation behavior
• Teams that need serious GPU compute without buying hardware, configuring a power supply, managing cooling, or maintaining a local system and who want to know why developers choose Compute with Hivenet

The RTX 4090 can fit workloads that use one GPU and stay within its 24GB memory limit. An RTX 5090 for fast AI and LLM inference provides 32GB per GPU, but it is not an NVLink-based substitute for a data-center configuration. If ECC, a particular interconnect or larger-scale training is required, choose hardware and software that explicitly meet those requirements.

Frequently asked questions

How many CUDA cores does the RTX 4090 have?
The RTX 4090 features 16,384 CUDA cores and has a Compute Capability of 8.9, making it highly suitable for CUDA development tasks. These cores are built on NVIDIA’s Ada Lovelace architecture and are designed for parallel FP32 and integer calculations.

Do I get the full GPU or shared access?
Hivenet no longer offers RTX 4090 instances for new Compute workloads. For a current preset, inspect the displayed GPU count, memory and allocation details before launch.

How quickly can I start using CUDA cores?
Choose an available current preset, complete the setup and wait for the instance to reach Running. Provisioning and application startup take time; this guide does not promise an RTX 4090 launch or a fixed startup time.

What if my workload needs more than 24GB memory?
Quantization, offloading or parameter-efficient methods may reduce memory requirements, with trade-offs. A current RTX 5090 has 32GB per GPU. Multiple GPUs require software support and do not automatically create one pooled memory space.

Are there any setup fees or minimum commitments?
Review the current preset’s active price and applicable resource charges and terms before launch. Do not use the retired RTX 4090 rate or this hardware guide as a guarantee about fees or commitments.

Is the RTX 4090 better than an A100?
There is no universal winner. Compare the specific model, precision, software and hardware configuration, including VRAM and interconnect needs, then use a reproducible benchmark and current total cost. A result for one workload does not establish better value for other jobs.

Does CUDA core count alone define performance?
Non. Les cœurs CUDA sont importants, mais les performances dépendent aussi des Tensor Cores, des RT Cores, de la fréquence d'horloge, de la capacité VRAM, de la bande passante mémoire, des E/S CPU et de stockage, du support des pilotes, de l'optimisation du framework et du type de charge de travail.

Choisissez une configuration Compute actuelle

La location dans le cloud évite l’achat et l’entretien de matériel GPU local. La flotte RTX 4090 de Hivenet a été retirée : consultez la console pour vérifier les configurations GPU et les tarifs disponibles avant de planifier une nouvelle charge de travail.

Ce guide présente le matériel RTX 4090 et ses limites. Pour un nouveau déploiement Compute, choisissez une configuration actuelle adaptée à votre modèle, à votre environnement logiciel et à vos besoins en mémoire, puis testez-la avec votre charge de travail.

Découvrir les options Compute actuelles

Vérifiez la capacité disponible, les ressources et les tarifs avant le lancement.

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