NVIDIA L4 VS NVIDIA V100
Choosing between **L4** and **V100** depends on your specific AI workload requirements. While the **V100** offers more VRAM for larger models, the **L4** remains competitive in other areas. Currently, you can rent these GPUs starting from **$0.26/h** and **$0.13/h** respectively across 49 providers.
📊 Detailed Specifications Comparison
| Specification | L4 | V100 | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Ada Lovelace | Volta | - |
| Process Node | 4nm | 12nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | Single-slot PCIe | SXM2 / PCIe | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 24GB | 32GB | -25% |
| Memory Type | GDDR6 | HBM2 | - |
| Memory Bandwidth | 300 GB/s | 900 GB/s | -67% |
| Memory Bus Width | 192-bit | 4096-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 7,424 | 5,120 | +45% |
| Tensor Cores (AI) | 232 | 640 | -64% |
| RT Cores (Ray Tracing) | 58 | N/A | |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 30.3 TFLOPS | 15.7 TFLOPS | +93% |
| FP16 (Half Precision) | 121 TFLOPS | 125 TFLOPS | -3% |
| FP64 (Double Precision) | N/A | 7.8 TFLOPS | |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 72W | 300W | -76% |
| PCIe Interface | PCIe 4.0 x16 | PCIe 3.0 x16 | - |
🎯 Use Case Recommendations
LLM & Large Model Training
NVIDIA V100
Higher VRAM capacity and memory bandwidth are critical for training large language models. The V100 offers 32GB 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 V100
Based on current cloud pricing, the V100 starts at a lower hourly rate.
Technical Deep Dive: L4 vs V100
This is a generational comparison within the NVIDIA ecosystem, pitting Ada Lovelace against Volta. The V100 has a significant **8GB VRAM advantage**, which is crucial for training massive datasets or large language models. From a cost perspective, the **V100** is currently about **50% cheaper** per hour, offering better value for budget-conscious projects.
NVIDIA L4 is Best For:
- Edge AI inference
- Video transcoding
- Large model training
NVIDIA V100 is Best For:
- Deep learning training
- Scientific research
- Latest generation workloads
Frequently Asked Questions
Which GPU is better for AI training: L4 or V100?
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 V100 provides 32GB of HBM2 with 900 GB/s bandwidth. For larger models, the V100's higher VRAM capacity gives it an advantage.
What is the price difference between L4 and V100 in the cloud?
Cloud GPU rental prices vary by provider and region. Based on our data, L4 starts at $0.26/hour while V100 starts at $0.13/hour. This represents a 100% price difference.
Can I use V100 instead of L4 for my workload?
It depends on your specific requirements. If your model fits within 32GB of VRAM and you don't need the additional throughput of the L4, the V100 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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