Ampere Architecture
Rent NVIDIA RTX 3090 Cloud GPU
The best-value 24GB GPU in the cloud. 10,496 CUDA cores, 24GB GDDR6X VRAM, and Ampere architecture—starting at just $0.29/hr on VectorLay. The most affordable way to run production AI inference.
NVIDIA RTX 3090: Maximum Value for 24GB AI Workloads
The NVIDIA GeForce RTX 3090 launched in September 2020 as the flagship of the Ampere consumer GPU lineup. With 24GB of GDDR6X VRAM, 10,496 CUDA cores, and 35.6 TFLOPS of FP32 performance, it quickly became the workhorse GPU for AI researchers and startups who needed serious compute without data center budgets.
Two years after the RTX 4090's release, the RTX 3090 remains incredibly relevant for AI workloads. Why? Because most inference workloads are VRAM-limited, not compute-limited. If your model fits in 24GB—and most popular models do—the RTX 3090 delivers the same capability as the 4090 at a significantly lower cost per hour.
On VectorLay, the RTX 3090 is available at just $0.29/hr—41% less than the RTX 4090 and a fraction of what any hyperscaler charges for comparable VRAM. For cost-conscious teams running batch inference, background processing, or development workloads, the RTX 3090 is the smartest GPU choice in the cloud.
RTX 3090 Technical Specifications
| Specification | RTX 3090 |
|---|---|
| GPU Architecture | Ampere (GA102) |
| VRAM | 24GB GDDR6X |
| CUDA Cores | 10,496 |
| Memory Bandwidth | 936 GB/s |
| FP32 Performance | 35.6 TFLOPS |
| FP16 (Tensor) | 71 TFLOPS (with sparsity: 142) |
| TDP | 350W |
| Memory Bus | 384-bit |
| Tensor Cores | 328 (3rd Gen) |
| RT Cores | 82 (2nd Gen) |
The RTX 3090's Ampere architecture introduced 3rd-generation Tensor Cores with support for TF32, BF16, and INT8 precision. While it lacks the newer FP8 support found in Ada Lovelace, its Tensor Cores are still highly effective for quantized inference workloads. The 936 GB/s memory bandwidth ensures fast model weight loading—critical for large language model inference where memory bandwidth often bottlenecks performance.
RTX 3090 Cloud GPU Pricing on VectorLay
At $0.29/hr, the RTX 3090 is the most affordable 24GB GPU in the cloud. That's 61% cheaper than RunPod's RTX 4090, 76% cheaper than AWS's A10G, and over 90% cheaper than any A100 offering. For teams that need VRAM more than raw compute speed, the savings are enormous.
| Provider | GPU | $/hour | $/month |
|---|---|---|---|
| VectorLay | RTX 3090 | $0.29 | $209 |
| VectorLay | RTX 4090 | $0.49 | $353 |
| RunPod | RTX 4090 | $0.74 | $533 |
| AWS | A10G (24GB) | $1.21 | $871 |
| GCP | A100 (40GB) | $3.67 | $2,642 |
All VectorLay pricing includes storage, load balancing, and network egress. No hidden fees or surprise bills at the end of the month.
Best Use Cases for the RTX 3090
The RTX 3090 excels in scenarios where you need 24GB of VRAM at the lowest possible cost. Here are the workloads where it delivers the best value:
Cost-Optimized LLM Inference
Run Llama 3 8B, Mistral 7B, Phi-3, and other popular models at the lowest cost per token in the cloud. For applications like internal chatbots, document Q&A, and background text processing where sub-second latency isn't critical, the RTX 3090 delivers outstanding value. The 24GB VRAM fits the same models as the 4090, just at slightly lower throughput—but at 41% lower cost.
Batch Image Generation
Process large queues of Stable Diffusion, DALL·E, or custom image generation requests. The RTX 3090 generates SDXL images in 5–7 seconds per image—fast enough for production APIs, and the $0.29/hr pricing makes batch processing extremely cost-effective. Generate thousands of images per dollar.
Batch Audio Transcription
Process audio files with Whisper at 8–12x real-time speed. Perfect for podcast transcription services, meeting recording platforms, and media content indexing. At $0.29/hr, you can transcribe approximately 8–12 hours of audio per dollar spent—the most cost-effective transcription infrastructure available.
Development & Staging Environments
Use the RTX 3090 as your development and staging GPU while deploying production on RTX 4090s. Since both have 24GB VRAM, your models will work identically on both—test on the cheaper GPU, deploy on the faster one. Perfect for CI/CD pipelines that need GPU access for model validation.
Embedding Generation & Vector Search
Generate embeddings for RAG pipelines, semantic search, and recommendation systems. The RTX 3090 processes hundreds of documents per second through embedding models like BGE, E5, and GTE. For indexing large document collections or building vector databases, the low hourly cost makes it ideal for bulk processing jobs.
Fine-Tuning Small Models
Fine-tune models up to 7B parameters with LoRA/QLoRA on the RTX 3090. The 24GB VRAM and Ampere Tensor Cores handle fine-tuning jobs efficiently. While the RTX 4090 is faster for training, the RTX 3090 at $0.29/hr lets you run more experiments for the same budget—perfect for hyperparameter sweeps and rapid iteration.
How to Deploy an RTX 3090 on VectorLay
Deploying on VectorLay is simple. No cloud certifications needed, no YAML files, no Kubernetes expertise. Here's the process from zero to running inference:
Sign up for free
Create your VectorLay account at vectorlay.com/get-started. No credit card required for your first deployment. You get immediate access to the dashboard and CLI tools.
Choose RTX 3090
Select the RTX 3090 from the GPU catalog. Configure the number of GPU nodes, pick your preferred region, and specify your Docker container image. VectorLay supports any Docker image, so your existing inference stack works out of the box.
Deploy with one click
VectorLay provisions your GPU node with full VFIO passthrough via Kata Containers. Your container gets bare-metal GPU access with strong security isolation. The entire process takes minutes, not hours.
Go live with auto-failover
Your deployment comes with a live endpoint, automatic load balancing, and auto-failover. If any node fails, traffic automatically routes to healthy nodes. Monitor GPU utilization, request latency, and costs from the VectorLay dashboard.
RTX 3090 vs RTX 4090: Which Should You Choose?
Both GPUs have 24GB VRAM and can run the same models. The key differences come down to compute speed versus cost efficiency. Here's a detailed comparison to help you decide:
| Feature | RTX 3090 | RTX 4090 |
|---|---|---|
| Price | $0.29/hr | $0.49/hr |
| VRAM | 24GB GDDR6X | 24GB GDDR6X |
| FP32 | 35.6 TFLOPS | 82.6 TFLOPS |
| Best for | Cost optimization, batch jobs | Low latency, real-time AI |
| Cost/TFLOP | $0.0081 | $0.0059 |
Choose RTX 3090 when: Budget is your primary concern, you're running batch processing, your workload is VRAM-limited rather than compute-limited, you need a dev/staging environment, or you're processing background jobs where latency doesn't matter.
Choose RTX 4090 when: You need the lowest possible inference latency, you're running real-time user-facing applications, you want maximum tokens-per-second for chatbots, or you need FP8 support for the latest quantization methods.
RTX 3090 Performance for AI Workloads
Here's what to expect from the RTX 3090 across common inference scenarios:
| Workload | Model | Performance |
|---|---|---|
| LLM Inference | Llama 3 8B (INT4) | ~60 tokens/sec |
| LLM Inference | Mistral 7B (FP16) | ~30 tokens/sec |
| Image Generation | SDXL (30 steps, 1024×1024) | ~5.5 sec/image |
| Transcription | Whisper large-v3 | ~10x real-time |
| Embeddings | BGE-large-en | ~800 docs/sec |
While the RTX 3090 is roughly 40–50% slower than the RTX 4090 in raw compute, it's 41% cheaper per hour. For batch workloads where you're optimizing cost-per-output rather than latency, the RTX 3090 actually delivers better economics in many scenarios.
Frequently Asked Questions
How much does it cost to rent an RTX 3090 on VectorLay?
VectorLay offers RTX 3090 cloud GPUs at just $0.29 per hour with per-minute billing. There are no minimum commitments, no egress fees, and no hidden costs. At 24/7 usage, that's approximately $209 per month—the most affordable 24GB GPU option available in the cloud.
What AI models can I run on an RTX 3090?
The RTX 3090 with 24GB VRAM can run all the same models as the RTX 4090, just at slightly lower throughput. This includes Llama 3 8B, Mistral 7B, Stable Diffusion XL, Whisper large-v3, and quantized models up to 34B parameters. It's ideal for workloads where you need 24GB VRAM but don't need maximum inference speed.
How does RTX 3090 compare to RTX 4090 for inference?
The RTX 4090 is approximately 2.3x faster in FP32 compute (82.6 vs 35.6 TFLOPS) and has newer Tensor Cores. However, both have 24GB VRAM, so they can run the same models. The RTX 3090 at $0.29/hr offers better price-per-VRAM-GB than the RTX 4090 at $0.49/hr, making it ideal for VRAM-limited workloads or batch processing where throughput-per-dollar matters more than latency.
Is the RTX 3090 good enough for production AI inference?
Absolutely. The RTX 3090 is a proven production inference GPU used by thousands of companies. Its 24GB VRAM handles all popular open-source models, and the Ampere architecture provides excellent ML performance. For latency-sensitive applications, the RTX 4090 is faster, but for batch processing, background jobs, and cost-optimized deployments, the RTX 3090 is the smart choice.
Can I run Stable Diffusion on an RTX 3090?
Yes, the RTX 3090 runs Stable Diffusion XL excellently. Expect generation times of around 5-7 seconds per 1024×1024 image at 30 steps—slightly slower than the RTX 4090 but more than fast enough for production image generation APIs. The 24GB VRAM also allows for high-resolution generation and complex ControlNet pipelines.
Does VectorLay offer auto-failover for RTX 3090 deployments?
Yes. Every VectorLay deployment, including RTX 3090, includes built-in auto-failover. If a GPU node goes down, your workload is automatically migrated to a healthy node. Your inference endpoints stay online regardless of individual hardware failures. This is a key advantage over marketplace providers like Vast.ai.
Ready to deploy on the RTX 3090?
The most affordable 24GB GPU in the cloud. No credit card required. No egress fees. No hidden costs. Deploy in minutes.