NVIDIA GeForce RTX 3080 VS NVIDIA GeForce RTX 3090
Choosing between **RTX 3080** and **RTX 3090** depends on your specific AI workload requirements. The **RTX 3090** leads in both memory capacity and raw compute power, making it a stronger choice for high-end LLM training. Currently, you can rent these GPUs starting from **$0.10/h** and **$0.11/h** respectively across 9 providers.
RTX 3090
📊 Detailed Specifications Comparison
| Specification | RTX 3080 | RTX 3090 | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Ampere | Ampere | - |
| Process Node | 8nm | 8nm | - |
| Target Market | consumer | consumer | - |
| Form Factor | 2-slot PCIe | 3-slot PCIe | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 10GB | 24GB | -58% |
| Memory Type | GDDR6X | GDDR6X | - |
| Memory Bandwidth | 760 GB/s | 936 GB/s | -19% |
| Memory Bus Width | 320-bit | 384-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 8,704 | 10,496 | -17% |
| Tensor Cores (AI) | N/A | 328 | |
| RT Cores (Ray Tracing) | N/A | 82 | |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 29.8 TFLOPS | 35.58 TFLOPS | -16% |
| FP16 (Half Precision) | N/A | 71 TFLOPS | |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 320W | 350W | -9% |
| PCIe Interface | PCIe 4.0 x16 | PCIe 4.0 x16 | - |
🎯 Use Case Recommendations
LLM & Large Model Training
NVIDIA GeForce RTX 3090
Higher VRAM capacity and memory bandwidth are critical for training large language models. The RTX 3090 offers 24GB compared to 10GB.
AI Inference
NVIDIA GeForce RTX 3090
For inference workloads, performance per watt matters most. Consider the balance between FP16/INT8 throughput and power consumption.
Budget-Conscious Choice
NVIDIA GeForce RTX 3080
Based on current cloud pricing, the RTX 3080 starts at a lower hourly rate.
Technical Deep Dive: RTX 3080 vs RTX 3090
Both GPUs utilize the NVIDIA Ampere architecture. The primary difference lies in their memory capacity and compute core counts. The RTX 3090 has a significant **14GB VRAM advantage**, which is crucial for training massive datasets or large language models. From a cost perspective, the **RTX 3080** is currently about **9% cheaper** per hour, offering better value for budget-conscious projects.
NVIDIA GeForce RTX 3080 is Best For:
- Gaming
- Cloud PCs
- VRAM-intensive models
NVIDIA GeForce RTX 3090 is Best For:
- Affordable AI development
- Enterprise availability
Frequently Asked Questions
Which GPU is better for AI training: RTX 3080 or RTX 3090?
For AI training, the key factors are VRAM size, memory bandwidth, and tensor core performance. The RTX 3080 offers 10GB of GDDR6X memory with 760 GB/s bandwidth, while the RTX 3090 provides 24GB of GDDR6X with 936 GB/s bandwidth. For larger models, the RTX 3090's higher VRAM capacity gives it an advantage.
What is the price difference between RTX 3080 and RTX 3090 in the cloud?
Cloud GPU rental prices vary by provider and region. Based on our data, RTX 3080 starts at $0.10/hour while RTX 3090 starts at $0.11/hour. This represents a 9% price difference.
Can I use RTX 3090 instead of RTX 3080 for my workload?
It depends on your specific requirements. If your model fits within 24GB of VRAM and you don't need the additional throughput of the RTX 3080, the RTX 3090 can be a cost-effective alternative. However, for workloads requiring maximum memory capacity or multi-GPU scaling, the RTX 3080's architecture may be essential.
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