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

16,384 cuda cores with full dedicated access at €0.40/hr through compute with hivenet

The RTX 4090 gives you 16,384 CUDA cores for parallel GPU compute, and Compute with Hivenet lets you rent the full card-dedicated 24GB VRAM included-for €0.40/hr without shared slices, spot interruptions, or bidding games.

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 GeForce RTX 4090 is a beast of a consumer GPU, but the performance difference you feel depends on CUDA cores, Tensor Cores, RT Cores, clock speed, VRAM capacity, memory bandwidth, framework support, and whether you actually get the whole graphics card. With Compute with Hivenet’s secure distributed GPU cloud, you get full dedicated RTX 4090 access at a public hourly price.

Why you’ll love RTX 4090 cuda performance

• Massive parallel processing – The NVIDIA GeForce RTX 4090 features 16,384 CUDA cores, which is a significant increase from the previous generation’s 10,496 CUDA cores. That enormous leap helps the GPU handle thousands of simultaneous operations for AI, rendering, simulations, and CUDA workloads.
• Dedicated 24GB VRAM – The RTX 4090 is equipped with 24 GB of GDDR6X memory, providing a memory bandwidth of 1008 GB/s. With Compute with Hivenet, that full VRAM is yours, so models, datasets, renders, and experiments are not competing with another customer’s load.
• Instant availability – Skip the hardware purchase, cooling setup, power supply planning, and PC build work. Start using NVIDIA RTX 4090 compute on demand and create results within minutes instead of waiting for a graphics card delivery.
• Predictable costs – Pay €0.40/hr for the RTX 4090 with transparent billing. No hidden fees, no bidding wars, no variable spot price, and no hyperscaler complexity pushing you toward expensive A100 or H100 instances when a 4090 is better value for your workload, especially compared with many GPU rental services for AI workloads.
• Professional stability – Use non-interruptible-by-default access for persistent workloads, benchmarking, AI inference, PyTorch, TensorFlow, rendering, and training models. Reliable access matters because 16,384 CUDA cores deliver value only when your job can keep running.

The RTX 4090 supports ultra-high performance gaming at 4K resolution with the ability to utilize NVIDIA’s DLSS 3 technology, which enhances frame rates and image quality through AI-generated frames. For compute customers, the same Ada Lovelace strengths-Tensor Cores, frame generation technology, shader execution reordering, the optical flow accelerator, and improved RT hardware-also point to why the GPU is powerful across modern AI powered graphics and visualization workloads.

What makes our RTX 4090 cuda access different

Many cloud GPU providers advertise “RTX 4090” but deliver a weaker real experience through shared GPU slices, preemptible instances, limited VRAM access, dynamic pricing, or vague provisioning. Compute with Hivenet is built differently: rent a full RTX 4090, use the full memory, and know what you will pay before the workload starts.

• Full GPU dedication – All 16,384 CUDA cores and 24GB VRAM are allocated to your workload. The RTX 4090 balances its core count with 24GB of ultra-fast GDDR6X memory on a wide 384-bit bus to feed the cores enough data without bottlenecking.
• Transparent pricing – RTX 4090 access is €0.40/hr, billed by actual usage. The NVIDIA GeForce RTX 4090 has a starting price of $1599.00 as hardware, and local ownership also brings power, cooling, warranty, depreciation, and setup costs. Renting turns that capital expense into flexible compute.
• Immediate access – Book and start using within minutes for AI inference, data science, rendering, simulation, multi monitor testing, CUDA benchmarking, or experiments that need speed now using our RTX 4090 cloud GPUs.
• Professional support – Get reachable support when your CUDA environment, driver stack, memory load, framework behavior, or training workflow needs attention, backed by clear Compute billing and instance rental FAQs.

The RTX 4090 achieves 70-90% of the performance of the A100 40GB in most machine learning tasks, providing a cost-effective solution for single-GPU workloads. The RTX 4090 is also reported to achieve 70-90% of the performance of the A100 GPU at 1/6th the cost, making it a more affordable option for single-GPU workloads. That does not mean the RTX 4090 replaces A100 or H100 for every enterprise job, especially where ECC memory, more VRAM, NVLink, or large multi GPU training is required. It does mean many practical workloads get excellent value without Big Tech cloud complexity, and many developers now choose RTX 4090 over A100 for AI workloads.

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
   The CUDA cores work with 24GB GDDR6X memory, 1008 GB/s memory bandwidth, and specialized accelerators inside the NVIDIA Ada Lovelace architecture. Tensor Cores handle deep learning matrix math and RT Cores manage ray-traced lighting, relying on standard CUDA cores for heavy data preprocessing and activation functions. The RTX 4090 also includes ray tracing cores for realistic lighting, reflections, and incredibly detailed virtual worlds.
3. Scalable results
   From single-model AI inference to batch rendering and CUDA development, the RTX 4090 adapts to workload size. With 24GB of VRAM, the RTX 4090 can handle training models up to approximately 7 billion parameters, making it effective for machine learning tasks. For many fine-tuning workflows, quantization and efficient memory use can extend what fits, while larger models may need more VRAM, multiple GPUs, offloading, or an upgrade path—especially when you compare RTX 4090 and 5090 vs A100 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
• AI / Tensor Performance: The RTX 4090 can achieve up to 82.58 TFLOPS of performance with its 4th Generation Tensor cores, significantly improving data science and AI modeling capabilities compared to previous generations
• Architecture: NVIDIA Ada Lovelace architecture, which enhances performance and efficiency in graphics processing
• Ray Tracing: With the Ada Lovelace architecture, the RTX 4090 provides up to 2X ray tracing performance compared to its predecessor, allowing for more realistic graphics in supported games
• Local Hardware Power: The NVIDIA GeForce RTX 4090 has a thermal design power (TDP) of 450W, with transient spikes that can reach up to 600W during intensive tasks
• Local PSU Requirement: NVIDIA recommends a minimum power supply unit (PSU) of 850W for the RTX 4090 to ensure stable operation during demanding workloads
• Local Connector Requirement: The RTX 4090 requires a quality PSU with a single 16-pin 12VHPWR connector or three 8-pin adapters for stable power delivery during operation
• Compute with Hivenet Power: Managed infrastructure, no PSU planning, no cooling buildout, and no local power consumption management
• Pricing: €0.40 per hour, billed by actual usage
• Upgrade Path: RTX 5090 cloud GPUs with 32GB VRAM at €0.75/hr when the workload needs more headroom

The NVIDIA GeForce RTX 4090 features 16,384 CUDA cores, which is a significant increase from the 10,496 CUDA cores found in the previous generation RTX 3090, enhancing its performance capabilities. The RTX 4090 utilizes the NVIDIA Ada Lovelace architecture, supports modern NVIDIA RTX features, and is widely viewed as the ultimate GeForce GPU for users who need ultra high performance gaming, AI powered graphics, creative speed, and compute efficiency in one card, and it ranks among the best AI GPUs for 2026 ML workloads.

One market note: due to U.S. export restrictions, the market price of the RTX 4090 rose by 25% as demand increased in countries like China, which began stockpiling the GPUs. For customers in other countries, cloud access can reduce exposure to hardware availability swings and keep cost tied to actual usage instead of GPU resale conditions.

Who should use RTX 4090 cuda cores

Ideal for:

• AI researchers training models up to 7-13B parameters, depending on precision, quantization, and memory strategy
• 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 is a strong fit when your workload can use a single powerful GPU with 24GB VRAM. It is especially compelling compared with the previous generation when you need more speed, better Tensor Core acceleration, higher clock behavior, and improved efficiency. If your model requires more vram than 24GB, or if you need ECC memory, NVLink-style multi gpu scaling, or very large enterprise training, a data-center card or an RTX 5090 for fast AI and LLM inference path may be more appropriate.

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?
Yes, with Compute with Hivenet you get complete dedicated access to the RTX 4090, including all CUDA cores and the full 24GB VRAM. You are not renting a shared slice by default.

How quickly can I start using CUDA cores?
RTX 4090 instances are designed for fast access and can be ready within minutes of booking. This is useful for experiments, urgent rendering jobs, AI inference, and short benchmarking runs.

What if my workload needs more than 24GB memory?
If your workload needs more than 24GB VRAM, you can reduce memory use with quantization or offloading, use multiple GPUs where appropriate, or upgrade to RTX 5090 with 32GB at €0.75/hr through Compute with Hivenet.

Are there any setup fees or minimum commitments?
No. Pay only for hours used, with no setup costs or long-term contracts. The RTX 4090 price is €0.40/hr.

Is the RTX 4090 better than an A100?
Not in every workload. A100 and H100 GPUs can be better for very large models, ECC memory requirements, high-bandwidth multi GPU training, and enterprise-scale workloads. For many single-GPU AI inference, fine-tuning, rendering, and data science jobs, the RTX 4090 offers better value.

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.

Commencez à utiliser 16 384 cœurs CUDA dès aujourd'hui

Ne perdez plus de temps à attendre les livraisons de matériel, à lutter contre les contraintes locales d'alimentation et de refroidissement, ou à vous contenter de ressources GPU cloud partagées. Compute avec Hivenet vous offre les performances complètes de la NVIDIA RTX 4090 avec une VRAM dédiée, un accès stable et une tarification prévisible de 0,40 €/heure.

Optez pour la RTX 4090 si vous recherchez une puissance de calcul GPU sérieuse pour l'inférence IA, l'entraînement de modèles, le développement CUDA, le rendu, la simulation et la science des données sans avoir à acheter une carte graphique haut de gamme. Commencez avec la 4090 pour des performances économiques, puis passez à la RTX 5090 à 0,75 €/heure lorsque votre système aura besoin de plus de mémoire et de marge de manœuvre.

Réserver une instance RTX 4090

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