Ada Lovelace Architecture
Rent NVIDIA RTX 4090 Cloud GPU
The fastest consumer GPU ever made. 24GB GDDR6X, 16,384 CUDA cores, and 82.6 TFLOPS of FP32 compute—starting at just $0.49/hr on VectorLay. No egress fees. Per-minute billing. Auto-failover included.
NVIDIA RTX 4090: The King of Inference GPUs
The NVIDIA GeForce RTX 4090 is the flagship consumer GPU built on the Ada Lovelace architecture. Released in October 2022, it quickly became the most sought-after GPU for AI inference workloads thanks to its exceptional raw performance, generous 24GB VRAM, and outstanding price-to-performance ratio compared to data center GPUs.
For AI and machine learning practitioners, the RTX 4090 represents a sweet spot: it delivers enough VRAM to run the most popular open-source models (7B–34B parameters with quantization), while offering FP32 throughput that rivals or exceeds data center GPUs costing 3–7x more per hour. Whether you're running Llama 3, Stable Diffusion XL, Whisper, or custom ONNX models, the RTX 4090 handles it all with room to spare.
On VectorLay, you can rent RTX 4090 cloud GPUs for just $0.49 per hour—a fraction of what hyperscalers charge for comparable performance. Our distributed infrastructure means you get built-in auto-failover, no egress fees, and per-minute billing so you never pay for idle time.
RTX 4090 Technical Specifications
| Specification | RTX 4090 |
|---|---|
| GPU Architecture | Ada Lovelace (AD102) |
| VRAM | 24GB GDDR6X |
| CUDA Cores | 16,384 |
| Memory Bandwidth | 1,008 GB/s |
| FP32 Performance | 82.6 TFLOPS |
| FP16 (Tensor) | 165.2 TFLOPS (with sparsity: 330.3) |
| TDP | 450W |
| Memory Bus | 384-bit |
| Tensor Cores | 512 (4th Gen) |
| RT Cores | 128 (3rd Gen) |
The RTX 4090's 82.6 TFLOPS of FP32 compute puts it well ahead of the A100 (19.5 TFLOPS) and even the professional-grade L40S. Its 4th-generation Tensor Cores support FP8, INT8, and INT4 precision modes, making it exceptionally well-suited for quantized model inference—the standard approach for running LLMs in production.
RTX 4090 Cloud GPU Pricing on VectorLay
Compare that to other providers: RunPod charges $0.74/hr for the same GPU, AWS charges $1.21/hr for an A10G with less performance, and renting a comparable A100 on any hyperscaler starts at $3.40/hr. VectorLay saves you 34–86% depending on the alternative.
| Provider | GPU | $/hour | vs VectorLay |
|---|---|---|---|
| VectorLay | RTX 4090 | $0.49 | — |
| RunPod | RTX 4090 | $0.74 | +51% |
| AWS | A10G (24GB) | $1.21 | +147% |
| Lambda Labs | A10 (24GB) | $0.75 | +53% |
| CoreWeave | A100 (40GB) | $2.21 | +351% |
VectorLay includes storage, load balancing, and egress in the hourly price. No surprise bills at the end of the month. What you see is what you pay.
Best Use Cases for the RTX 4090
The RTX 4090's combination of high compute throughput, generous VRAM, and Ada Lovelace Tensor Cores makes it ideal for a wide range of AI and ML workloads. Here are the scenarios where it truly shines:
LLM Inference (7B–34B Parameters)
Run Llama 3 8B, Mistral 7B, CodeLlama 34B (GPTQ/AWQ quantized), Phi-3, and other popular open-source LLMs with excellent tokens-per-second performance. The 24GB VRAM accommodates 4-bit quantized models up to ~34B parameters, and the raw compute power delivers fast inference latency ideal for real-time chatbots and API endpoints.
Stable Diffusion & Image Generation
Generate images with Stable Diffusion XL, DALL·E-style models, and ControlNet pipelines. The RTX 4090 produces SDXL 1024×1024 images in under 3 seconds at 30 steps. Batch generation for production image APIs is blazing fast, and you have enough VRAM for high-resolution outputs and complex ControlNet configurations.
Speech-to-Text (Whisper)
Run OpenAI's Whisper large-v3 for production-grade transcription. The RTX 4090 processes audio at 10–20x real-time speed, making it viable for real-time transcription services, podcast processing pipelines, and meeting summarization tools.
Real-Time AI Applications
Build low-latency AI features: semantic search, embedding generation, RAG pipelines, and real-time video analysis. The RTX 4090's high clock speeds and massive parallelism deliver sub-50ms inference latency for most models, enabling interactive user experiences.
Fine-Tuning & LoRA Training
Fine-tune models up to 13B parameters with LoRA/QLoRA on the RTX 4090. The 24GB VRAM and fast memory bandwidth make fine-tuning runs significantly faster than on older GPUs. Train custom adapters for your specific domain in hours, not days.
Computer Vision & Object Detection
Run YOLOv8, SAM (Segment Anything), and other vision models at production throughput. Process video streams in real-time for surveillance, quality inspection, autonomous systems, and medical imaging analysis with the RTX 4090's exceptional FP16 and INT8 performance.
How to Deploy an RTX 4090 on VectorLay
Getting an RTX 4090 running on VectorLay takes minutes, not hours. No YAML files, no Kubernetes manifests, no cloud console rabbit holes. Here's how it works:
Create your account
Sign up at vectorlay.com/get-started. No credit card required for your first deployment. You'll get access to the dashboard and CLI immediately.
Select RTX 4090 as your GPU
Choose the RTX 4090 from the GPU catalog. Specify how many GPUs you need, your preferred region, and any container image requirements. VectorLay supports Docker images, so bring your existing inference stack.
Deploy your workload
Hit deploy and VectorLay handles the rest: provisioning, GPU passthrough via VFIO, network setup, and health monitoring. Your container gets full access to the RTX 4090 via Kata Containers for strong isolation.
Get your endpoint
Within minutes, you'll have a live endpoint with automatic load balancing across your GPU nodes. Auto-failover is built in—if a node goes down, traffic routes to healthy nodes automatically. Monitor everything from the dashboard.
RTX 4090 Performance for AI Workloads
Here's what you can expect from the RTX 4090 across common AI workloads. These numbers reflect real-world inference performance, not synthetic benchmarks:
| Workload | Model | Performance |
|---|---|---|
| LLM Inference | Llama 3 8B (INT4) | ~100 tokens/sec |
| LLM Inference | Mistral 7B (FP16) | ~55 tokens/sec |
| Image Generation | SDXL (30 steps, 1024×1024) | ~2.8 sec/image |
| Transcription | Whisper large-v3 | ~15x real-time |
| Embeddings | BGE-large-en | ~1,200 docs/sec |
These figures demonstrate why the RTX 4090 is the go-to GPU for production inference. At $0.49/hr, the cost per token, cost per image, and cost per transcription hour are a fraction of what you'd pay on hyperscaler alternatives.
RTX 4090 vs A100 vs H100: Which GPU Should You Choose?
The right GPU depends on your specific workload. Here's a quick comparison to help you decide:
Choose the RTX 4090 if: Your models fit in 24GB VRAM, you're optimizing for inference cost, you need fast single-GPU performance, or you're running real-time applications that need low latency. The 4090 is the best dollar-per-TFLOP value on the market.
Choose the A100 if: You need 40–80GB VRAM for larger models (70B+ parameters at FP16), you require HBM memory bandwidth for memory-bound workloads, or you need NVLink for multi-GPU training. The A100 is available on VectorLay for $1.64/hr.
Choose the H100 if: You're training large models from scratch, need the absolute highest throughput for FP8 inference, or require the Transformer Engine for mixed-precision training. The H100 is available on VectorLay for $2.49/hr.
Frequently Asked Questions
How much does it cost to rent an RTX 4090 on VectorLay?
VectorLay offers RTX 4090 cloud GPUs at $0.49 per hour with per-minute billing. There are no minimum commitments, no egress fees, and no hidden costs. That works out to approximately $353 per month for 24/7 usage, which is up to 60% cheaper than comparable providers.
What AI models can I run on an RTX 4090?
The RTX 4090 with 24GB VRAM can run most popular AI models including Llama 3 8B, Mistral 7B, Stable Diffusion XL, Whisper large-v3, CodeLlama 34B (quantized), and many more. It excels at inference for models up to ~34B parameters with quantization, and handles fine-tuning for models up to 13B parameters.
How does RTX 4090 compare to A100 for inference?
For inference workloads, the RTX 4090 often matches or outperforms the A100. The 4090 delivers 82.6 FP32 TFLOPS compared to the A100's 19.5 TFLOPS, and its Ada Lovelace architecture provides excellent power efficiency. The A100's advantage is its larger 40-80GB HBM memory for very large models. For models that fit in 24GB, the RTX 4090 at $0.49/hr offers dramatically better price-performance than an A100 at $1.64/hr.
Does VectorLay offer multi-GPU RTX 4090 setups?
Yes, VectorLay supports multi-GPU deployments. You can scale horizontally across multiple RTX 4090 nodes with built-in load balancing and auto-failover. This lets you distribute inference workloads across several GPUs for higher throughput without managing the infrastructure yourself.
What is the uptime guarantee for RTX 4090 GPUs on VectorLay?
VectorLay's distributed architecture provides built-in auto-failover. If a GPU node goes down, your workload is automatically migrated to a healthy node. This means your inference endpoints stay online even if individual hardware fails—something marketplace GPU providers cannot offer.
Can I use the RTX 4090 for training, or is it inference only?
While VectorLay is optimized for inference workloads, you can use the RTX 4090 for fine-tuning and training smaller models. The 24GB VRAM supports LoRA fine-tuning of models up to 13B parameters and full fine-tuning of smaller models. For large-scale training of 70B+ models, consider the H100 instead.
Ready to deploy on the RTX 4090?
Get started in minutes. No credit card required. No egress fees. No hidden costs. Just the fastest consumer GPU at the best price.