NVIDIA RTX A4000 VS NVIDIA RTX 4000 Ada Generation
Choosing between **RTX A4000** and **RTX 4000 Ada** depends on your specific AI workload requirements. The **RTX 4000 Ada** 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 **$0.00/h** and **$0.00/h** respectively across 0 providers.
RTX A4000
RTX 4000 Ada
📊 Detailed Specifications Comparison
| Specification | RTX A4000 | RTX 4000 Ada | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Ampere | Ada Lovelace | - |
| Process Node | 8nm | 4nm | - |
| Target Market | professional | professional | - |
| Form Factor | Single-slot PCIe | Single-slot PCIe | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 16GB | 20GB | -20% |
| Memory Type | GDDR6 | GDDR6 | - |
| Memory Bandwidth | 448 GB/s | 360 GB/s | +24% |
| Memory Bus Width | 256-bit | 160-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 6,144 | 6,144 | |
| Tensor Cores (AI) | 192 | 192 | |
| RT Cores (Ray Tracing) | 48 | 48 | |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 19.2 TFLOPS | 26.7 TFLOPS | -28% |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 140W | 130W | +8% |
| PCIe Interface | PCIe 4.0 x16 | PCIe 4.0 x16 | - |
🎯 Use Case Recommendations
LLM & Large Model Training
NVIDIA RTX 4000 Ada Generation
Higher VRAM capacity and memory bandwidth are critical for training large language models. The RTX 4000 Ada offers 20GB compared to 16GB.
AI Inference
NVIDIA RTX 4000 Ada Generation
For inference workloads, performance per watt matters most. Consider the balance between FP16/INT8 throughput and power consumption.
Budget-Conscious Choice
NVIDIA RTX 4000 Ada Generation
Compare live pricing to find the best value for your specific workload.
Technical Deep Dive: RTX A4000 vs RTX 4000 Ada
This is a generational comparison within the NVIDIA ecosystem, pitting Ampere against Ada Lovelace.
NVIDIA RTX A4000 is Best For:
- Professional graphics
- Workstation AI
- High-end training
NVIDIA RTX 4000 Ada Generation is Best For:
- Compact workstations
- Professional graphics
- Deep learning training
Frequently Asked Questions
Which GPU is better for AI training: RTX A4000 or RTX 4000 Ada?
For AI training, the key factors are VRAM size, memory bandwidth, and tensor core performance. The RTX A4000 offers 16GB of GDDR6 memory with 448 GB/s bandwidth, while the RTX 4000 Ada provides 20GB of GDDR6 with 360 GB/s bandwidth. For larger models, the RTX 4000 Ada's higher VRAM capacity gives it an advantage.
What is the price difference between RTX A4000 and RTX 4000 Ada in the cloud?
Cloud GPU rental prices vary by provider and region. Check our price tracker for the latest rates from 50+ cloud providers.
Can I use RTX 4000 Ada instead of RTX A4000 for my workload?
It depends on your specific requirements. If your model fits within 20GB of VRAM and you don't need the additional throughput of the RTX A4000, the RTX 4000 Ada can be a cost-effective alternative. However, for workloads requiring maximum memory capacity or multi-GPU scaling, the RTX A4000's architecture may be essential.
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