NVIDIA RTX 4090 GPU: 24GB Specs, Pricing & Rental. Rent RTX 4090 GPU from $0.65/hr
24GB GDDR6X Ada Lovelace with 4th gen Tensor Cores. The cheapest RTX 4090 GPU rentals for running 7B LLMs in the cloud.
You can rent an NVIDIA RTX 4090 on Spheron starting at $0.65/hr per GPU per hour, the lowest live marketplace rate. 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 specifications
NVIDIA RTX 4090 pricing
| Provider | Price/hr | Savings |
|---|---|---|
SpheronYour price | $0.65/hr | - |
Vast.ai | $0.30/hr | - |
RunPod (Community) | $0.34/hr | - |
RunPod (Secure) | $0.59/hr | - |
NeevCloud | $0.69/hr | 1.1x more expensive |
Need More RTX 4090 Than What's Listed?
Reserved Capacity
Commit to a duration, lock in availability and better rates
Custom Clusters
8 to 512+ GPUs, specific hardware, InfiniBand configs on request
Supplier Matchmaking
Spheron sources from its certified data center network, negotiates pricing, handles setup
Need more RTX 4090 capacity? Tell us your requirements and we'll source it from our certified data center network.
Typical turnaround: 24–48 hours
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
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/Meta-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/Meta-Llama-3.1-8B-Instruct", "messages": [{"role": "user", "content": "Hello"}] }'NVIDIA RTX 4090 guides and resources
NVIDIA RTX 4090 for AI and Machine Learning: Specs, Benchmarks, and Pricing
Complete guide to RTX 4090 specs, AI benchmarks, and why it's the most affordable entry point for ML.
How to Run LLMs Locally with Ollama: GPU-Accelerated Setup Guide
Step-by-step guide to running local LLMs on RTX 4090 with Ollama for GPU-accelerated inference.
Dedicated vs Shared GPU Memory: Why VRAM Matters for AI
Understanding RTX 4090's 24GB VRAM, what fits, what doesn't, and how to optimize memory usage.
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 Mobile (Laptop GPU) followed in January 2023. Cloud availability arrived quickly through marketplace platforms like Vast.ai in late 2022, with broader neo-cloud coverage by mid-2023. RunPod, Spheron, and others now offer RTX 4090 as the cost-efficient consumer-class option for AI workloads.
On Spheron 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.
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 or anything 30B+ even quantized, 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.