NVIDIA RTX 4090 GPU: 24GB Specs, Pricing & Rental. Rent RTX 4090 GPU from $0.59/hr
24GB GDDR6X Ada Lovelace with 4th gen Tensor Cores. Per-minute billing on RTX 4090 GPU rentals, live in under 2 minutes.
- GPU Architecture
- NVIDIA Ada Lovelace
- VRAM
- 24 GB GDDR6X
- Memory Bandwidth
- 1.0 TB/s
No long contracts · Per-minute billing · Deploy in ~2 minutes
At a glance
You can rent an NVIDIA RTX 4090 on Spheron AI starting at $0.59/hr per GPU, bookable right now rather than listed and unavailable. Per-minute billing, no contracts, deployed in under 2 minutes across data center partners in multiple regions. The RTX 4090 ships with 24GB GDDR6X, 16,384 CUDA cores, and 4th gen Tensor Cores, giving you the best dollar-per-hour for 7B model inference, LoRA fine-tuning, Stable Diffusion image generation, and general AI prototyping. Good fit for startups, solo developers, and machine learning practitioners who don't need H100-class memory or NVLink interconnect.
NVIDIA RTX 4090 pricing
| Provider | Price/hr | vs Spheron AI | Availability |
|---|---|---|---|
Spheron AIYour price | $0.59/hr | - | Bookable now |
Vast.ai | $0.30/hr | 49% cheaper | Community instance |
NeevCloud | $0.69/hr | 1.2x more expensive | Listed rate |
Runpod | $0.74/hr | 1.3x more expensive | Low capacity |
Spheron AI rates are live marketplace prices and move with partner supply, so what is shown is what you can book right now. Competitor figures are published on-demand rates taken from each provider's own pricing page. Each is measured against whichever of our own rates it actually sits against: our on-demand rate when it lands at or above it, and our spot rate when it falls below, because spot is preemptible and not the same product. A listed rate is not the same thing as available capacity: check before you plan a run. Where a competitor row carries a capacity note, that is what that provider's own console reported on 24 Aug 2026. It is a dated observation, not a live feed, and their supply may have changed since.
NVIDIA RTX 4090 specifications
NVIDIA Ada Lovelace
24 GB GDDR6X
1.0 TB/s
4th Generation
16,384
3rd Generation
82.6 TFLOPS
165.2 TFLOPS
330.3 TFLOPS
660.6 TOPS
24 GB DDR5
8 vCPUs
500 GB NVMe SSD
PCIe Gen4
450W
Need More RTX 4090 Than
What's Listed?
We'll Source It.
On-demand by default, reserved pricing when you commit. Tell us the cluster size, hardware and term, and we match you with the right data center partner.
Reserved Capacity
Custom Clusters
Supplier Matchmaking
How It Works
- 01
Submit your requirements
Tell us your GPU needs, timeline, and scale
- 02
Spheron AI sources from certified data centers
We match, negotiate pricing, and handle setup
- 03
Reserved capacity confirmed
Guaranteed availability, ready to deploy
Typical turnaround: 24–48 hours for custom sourcing
When to pick the RTX 4090
Pick RTX 4090 if
You're running 7B-class LLM inference, Stable Diffusion image generation, or LoRA/QLoRA fine-tuning on a budget. You want the lowest hourly GPU rate and 24GB VRAM is enough for your model. Great fit for Kaggle, prototyping, and cost-sensitive production inference.
Pick RTX 5090 instead if
You want Blackwell-generation throughput (roughly 28-50% more tokens/sec on LLMs), 32GB GDDR7, native FP4 support, or you're working with models that are slightly too big for 24GB. Small price bump, meaningful performance lift.
Pick L40S instead if
You need 48GB VRAM on a data center SKU with ECC memory, better multi-tenant isolation, and longer production lifecycle support. L40S is purpose-built for inference serving at scale.
Pick A100 or H100 instead if
You're fine-tuning or training 30B+ parameter models, need NVLink for multi-GPU, or your workload requires the HBM bandwidth and FP8 Transformer Engine of Hopper. RTX 4090 will be the bottleneck.
NVIDIA RTX 4090 use cases
Cost-efficient AI development
An affordable entry point for AI and ML development. Perfect for individuals and startups building their AI projects.
Small Model Fine-Tuning
Efficiently fine-tune 7B parameter models with LoRA and QLoRA techniques. Ideal for domain-specific model adaptation at minimal cost.
AI Inference Deployment
Deploy cost-effective inference workloads at scale. Serve 7B models and smaller architectures with excellent throughput per dollar.
Creative AI & Content Generation
Run generative AI workloads affordably. One of the best GPUs for Stable Diffusion and other creative AI applications.
NVIDIA RTX 4090 benchmarks
~55 tokens/s
vLLM, single stream
~580 tokens/s
vLLM, batched
~140 tokens/s
llama.cpp, single stream
~10 img/min
1024x1024, base + refiner
~520 tokens/s
INT4 fine-tuning
1,008 GB/s
GDDR6X, 384-bit bus
~2.3x prefill
~12% faster decode, llama.cpp 7B Q4_0
Serve Llama 3.1 8B on RTX 4090 with vLLM
Spin up an OpenAI-compatible inference endpoint on a single RTX 4090. 24GB fits Llama 3.1 8B in FP16 with a 4K-8K context window depending on batch size.
# SSH into your RTX 4090 instancessh root@<instance-ip> # Install vLLM (CUDA 12.x compatible)pip install vllm # Serve Llama 3.1 8B in FP16 on a single RTX 4090vllm serve meta-llama/Llama-3.1-8B-Instruct \ --dtype float16 \ --max-model-len 4096 \ --gpu-memory-utilization 0.9 \ --port 8000 # Test the OpenAI-compatible endpointcurl http://localhost:8000/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "meta-llama/Llama-3.1-8B-Instruct", "messages": [{"role": "user", "content": "Hello"}] }'01Technical Brief
RTX 4090 Price: $1,599 Launch MSRP vs Hourly Rental
NVIDIA put the GeForce RTX 4090 on sale on October 12, 2022, with a launch price starting at $1,599 for the Founders Edition. Partner cards from ASUS, MSI, GIGABYTE and others were priced by each maker, many of them above that. The RTX 5090 replaced it as the consumer flagship in January 2025, so what a 4090 costs today depends on remaining retail stock and the used market rather than on NVIDIA.
At the $1,599 launch MSRP, buying an RTX 4090 costs the same as about 2,710 hours of renting one on Spheron AI at $0.59/hr on-demand. That is roughly 339 eight-hour working days, before you count electricity, the host machine around the card, or what it resells for.
So the math comes down to utilisation. If the card would run most days for a year or more, and you already own a desktop with an 850W-class power supply, buying usually wins. If you need it for a project, a fine-tuning run or a few evenings a week, renting costs less and you skip the resale risk. Live RTX 4090 rates for every tier are on the GPU pricing page.
02Technical Brief
NVIDIA RTX 4090 Release Date and Cloud Availability
The NVIDIA GeForce RTX 4090 launched October 12, 2022 at $1,599 MSRP as the flagship of the consumer Ada Lovelace generation. NVIDIA announced the card at GTC September 2022. The RTX 4090 Laptop GPU was announced at CES in January 2023 and shipped in laptops from February 2023. Cloud availability arrived quickly through marketplace platforms like Vast.ai in late 2022, with broader neo-cloud coverage by mid-2023. Runpod, Spheron AI, and others now offer RTX 4090 as the cost-efficient consumer-class option for AI workloads.
On Spheron AI the RTX 4090 is available with per-minute billing and no contract. Live availability and pricing is on the pricing page. The RTX 4090 successor is the RTX 5090 (32GB GDDR7 Blackwell, launched January 2025), which delivers roughly 28-50% more AI throughput and 33% more VRAM. For teams on a tight budget, the RTX 4090 remains the cheapest data-center-accessible GPU for sub-13B model inference and LoRA fine-tuning.
03Technical Brief
RTX 4090 VRAM and Memory Bandwidth: 24GB GDDR6X at 1.01 TB/s
The RTX 4090 ships with 24GB of GDDR6X memory at 1.01 TB/s of bandwidth on a 384-bit bus. That is the same VRAM as the previous-generation RTX 3090 but with roughly 7% more bandwidth and significantly more compute throughput thanks to the Ada Lovelace 4th-gen Tensor Cores with FP8 support. For LLM inference, the 24GB VRAM fits a 7B model in FP16 with room for short-context KV cache, or a 13B model in INT4 quantization.
Where the 24GB VRAM matters: 7B model inference (Llama 3 8B, Mistral 7B, Qwen 7B) fits comfortably in FP16 with batched serving, Stable Diffusion 1.5 and SDXL run without OOM at standard resolutions, and LoRA fine-tuning on 7B-class models works with Unsloth or QLoRA. For 13B-class models in FP16, 30B-class models beyond 4-bit or with long context, and any 70B model even at 4-bit, the RTX 4090 is too tight; step up to the RTX 5090 (32GB GDDR7), RTX PRO 6000 Blackwell (96GB GDDR7 ECC), or H100 (80GB HBM3) depending on workload and budget. NVIDIA's EULA technically prohibits data center use of GeForce cards; for production deployments the L40S (48GB GDDR6 ECC) is the equivalent-tier data-center-compliant option.
NVIDIA RTX 4090 FAQ
Specs, pricing and availability questions teams ask before renting the NVIDIA RTX 4090.
Yes! The RTX 4090 is an excellent GPU for AI development, prototyping, small model fine-tuning, and inference workloads. With 24GB GDDR6X VRAM and 4th generation Tensor Cores, it offers the best price-performance ratio in our lineup. It's ideal for running 7B parameter models, Stable Diffusion, and a wide range of ML experiments without breaking the budget.
24GB comfortably fits Llama 3.1 8B (FP16, ~16GB), Mistral 7B (~14GB), Stable Diffusion XL, Flux.1 Dev (Q8), and Whisper Large V3. Q4-quantized 13B-14B models (Llama 2 13B, Qwen 2.5 14B) also fit. Llama 3.3 70B does not fit even at Q4 (Q4_K_M needs ~40GB); use an A100, H100, or H200 for that class. For a full sizing guide, see our GPU requirements cheat sheet.
The A100 has 80GB HBM2e memory compared to the RTX 4090's 24GB GDDR6X, and 2.0 TB/s memory bandwidth vs 1.0 TB/s. A100 is the right choice once you hit memory or multi-GPU bottlenecks, or need NVLink. For anything that fits in 24GB, the RTX 4090 costs a fraction of the A100's hourly rate and is plenty for 7B fine-tuning, inference, and image generation.
Yes, the RTX 4090 is well-suited for production inference for models that fit within 24GB VRAM. Multiple RTX 4090 instances can serve high traffic at a significantly lower cost than a single H100. This makes it an excellent choice for deploying 7B models, image classifiers, NLP pipelines, and chatbots in production.
Yes. RTX 4090 generates around 10 SDXL images per minute at 1024x1024 with base + refiner, and significantly more for SD 1.5 or lower resolutions. The combination of 82.6 TFLOPS FP32, 1 TB/s bandwidth, and 24GB VRAM makes it the default pick for self-hosted Stable Diffusion and Flux workflows.
All major deep learning frameworks are fully supported: PyTorch, TensorFlow, JAX, and ONNX Runtime. The RTX 4090 has full CUDA 12.x support with optimized drivers and libraries. Pre-configured Docker images are available for all frameworks, so you can start training or running inference immediately.
Run your own image as a GPU containerThe minimum runtime is 20 minutes, then Spheron AI bills per minute with no contracts or commitments. The RTX 4090 is one of the lowest per-hour rates in our GPU lineup, making it ideal for short experiments, quick prototyping sessions, and extended training runs alike.
Yes, you can use multiple RTX 4090 instances, but note that RTX 4090 does not support NVLink for direct GPU-to-GPU communication. For training, use data parallelism across separate instances with frameworks like PyTorch DDP. For inference, deploy multiple instances behind a load balancer to handle higher throughput at a fraction of the cost of a single H100.
RTX 4090 GPUs are currently available in US, Europe, and Canada regions. We're continuously expanding capacity and regions. Check our app or contact sales for specific region requirements and availability.
Yes! Our team provides technical support to help you get the most out of your RTX 4090 instances. We can assist with workload optimization, cost planning, and troubleshooting issues with GPU VMs. For teams needing dedicated support, we offer enterprise plans with priority assistance.
Book a call with our teamDedicated RTX 4090 instances are non-interruptible, run on a 99.99% SLA, and bill per-minute at the on-demand rate. So far the RTX 4090 has only been listed on dedicated capacity, so check the app before planning around spot. Where spot is listed, it runs on spare capacity and can be preempted when dedicated demand rises, and the two tiers track different pools, so compare the live rate on each rather than assuming one is always lower. Use spot for fault-tolerant workloads: QLoRA fine-tuning with checkpointing every 15-30 minutes, batch inference jobs, hyperparameter sweeps, and any training loop that can resume from a checkpoint. Use dedicated for production inference endpoints, customer-facing APIs, or any job where an interruption would cause data loss or an SLA breach. Both live in the same control plane, so you can mix tiers across a single project.
The GeForce RTX 4090 launched on October 12, 2022 at an MSRP of $1,599 for the Founders Edition. Partner cards were priced by each maker, and today's retail and used listings move with remaining stock rather than with NVIDIA's MSRP. At the $1,599 launch MSRP, buying an RTX 4090 costs the same as about 2,710 hours of renting one on Spheron AI at $0.59/hr on-demand. That is roughly 339 eight-hour working days, before you count electricity, the host machine around the card, or what it resells for.
On Spheron AI the NVIDIA RTX 4090 is $0.59/hr per GPU, billed per minute after a 20-minute minimum runtime, with no contract. Compared against published rates elsewhere, all per GPU: Vast.ai lists $0.30/hr (a community instance, container based rather than bare metal, 49% cheaper); NeevCloud lists $0.69/hr (1.2x more expensive); and Runpod lists $0.74/hr (low capacity, 1.3x more expensive). Each comparison above is against our on-demand rate. Competitor rates are published on-demand figures from each provider's own pricing page, and the capacity notes are what their own consoles reported on 24 Aug 2026. A listed rate is not the same thing as available capacity.