NVIDIA L40S VS NVIDIA V100
Choosing between **L40S** and **V100** depends on your specific AI workload requirements. The **L40S** 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 **$0.26/h** and **$0.13/h** respectively across 49 providers.
📊 Detailed Specifications Comparison
| Specification | L40S | V100 | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Ada Lovelace | Volta | - |
| Process Node | 4nm | 12nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | Dual-slot PCIe | SXM2 / PCIe | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 48GB | 32GB | +50% |
| Memory Type | GDDR6 | HBM2 | - |
| Memory Bandwidth | 864 GB/s | 900 GB/s | -4% |
| Memory Bus Width | 384-bit | 4096-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 18,176 | 5,120 | +255% |
| Tensor Cores (AI) | 568 | 640 | -11% |
| RT Cores (Ray Tracing) | 142 | N/A | |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 91.6 TFLOPS | 15.7 TFLOPS | +483% |
| FP16 (Half Precision) | 183.2 TFLOPS | 125 TFLOPS | +47% |
| FP64 (Double Precision) | N/A | 7.8 TFLOPS | |
| INT8 (Integer Precision) | 733 TOPS | N/A | |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 350W | 300W | +17% |
| PCIe Interface | PCIe 4.0 x16 | PCIe 3.0 x16 | - |
🎯 Use Case Recommendations
LLM & Large Model Training
NVIDIA L40S
Higher VRAM capacity and memory bandwidth are critical for training large language models. The L40S offers 48GB compared to 32GB.
AI Inference
NVIDIA L40S
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: L40S vs V100
This is a generational comparison within the NVIDIA ecosystem, pitting Ada Lovelace against Volta. The L40S has a significant **16GB 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 L40S is Best For:
- AI inference
- Generative AI
- Maximum memory bandwidth
NVIDIA V100 is Best For:
- Deep learning training
- Scientific research
- Latest generation workloads
Frequently Asked Questions
Which GPU is better for AI training: L40S or V100?
For AI training, the key factors are VRAM size, memory bandwidth, and tensor core performance. The L40S offers 48GB of GDDR6 memory with 864 GB/s bandwidth, while the V100 provides 32GB of HBM2 with 900 GB/s bandwidth. For larger models, the L40S's higher VRAM capacity gives it an advantage.
What is the price difference between L40S and V100 in the cloud?
Cloud GPU rental prices vary by provider and region. Based on our data, L40S starts at $0.26/hour while V100 starts at $0.13/hour. This represents a 100% price difference.
Can I use V100 instead of L40S 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 L40S, the V100 can be a cost-effective alternative. However, for workloads requiring maximum memory capacity or multi-GPU scaling, the L40S's architecture may be essential.
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