NVIDIA L4 VS NVIDIA A30
Choosing between **L4** and **A30** depends on your specific AI workload requirements. Currently, you can rent these GPUs starting from **$0.26/h** and **$0.11/h** respectively across 38 providers.
📊 Detailed Specifications Comparison
| Specification | L4 | A30 | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Ada Lovelace | Ampere | - |
| Process Node | 4nm | 7nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | Single-slot PCIe | Dual-slot PCIe | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 24GB | 24GB | |
| Memory Type | GDDR6 | HBM2 | - |
| Memory Bandwidth | 300 GB/s | 933 GB/s | -68% |
| Memory Bus Width | 192-bit | 3072-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 7,424 | 3,584 | +107% |
| Tensor Cores (AI) | 232 | 224 | +4% |
| RT Cores (Ray Tracing) | 58 | N/A | |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 30.3 TFLOPS | 5.2 TFLOPS | +483% |
| FP16 (Half Precision) | 121 TFLOPS | 165 TFLOPS | -27% |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 72W | 165W | -56% |
| PCIe Interface | PCIe 4.0 x16 | PCIe 4.0 x16 | - |
🎯 Use Case Recommendations
LLM & Large Model Training
NVIDIA A30
Higher VRAM capacity and memory bandwidth are critical for training large language models. The A30 offers 24GB compared to 24GB.
AI Inference
NVIDIA L4
For inference workloads, performance per watt matters most. Consider the balance between FP16/INT8 throughput and power consumption.
Budget-Conscious Choice
NVIDIA A30
Based on current cloud pricing, the A30 starts at a lower hourly rate.
Technical Deep Dive: L4 vs A30
This is a generational comparison within the NVIDIA ecosystem, pitting Ada Lovelace against Ampere. From a cost perspective, the **A30** is currently about **58% cheaper** per hour, offering better value for budget-conscious projects.
NVIDIA L4 is Best For:
- Edge AI inference
- Video transcoding
- Large model training
NVIDIA A30 is Best For:
- Enterprise AI inference
- Mainstream compute
- Heavy model training
Frequently Asked Questions
Which GPU is better for AI training: L4 or A30?
For AI training, the key factors are VRAM size, memory bandwidth, and tensor core performance. The L4 offers 24GB of GDDR6 memory with 300 GB/s bandwidth, while the A30 provides 24GB of HBM2 with 933 GB/s bandwidth. Both GPUs have similar VRAM capacity, so performance characteristics become the deciding factor.
What is the price difference between L4 and A30 in the cloud?
Cloud GPU rental prices vary by provider and region. Based on our data, L4 starts at $0.26/hour while A30 starts at $0.11/hour. This represents a 136% price difference.
Can I use A30 instead of L4 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 L4, the A30 can be a cost-effective alternative. However, for workloads requiring maximum memory capacity or multi-GPU scaling, the L4's architecture may be essential.
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