NVIDIA GB200 NVL72 VS NVIDIA L4
Choosing between **GB200** and **L4** depends on your specific AI workload requirements. The **GB200** 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 **$10.50/h** and **$0.26/h** respectively across 35 providers.
📊 Detailed Specifications Comparison
| Specification | GB200 | L4 | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Blackwell | Ada Lovelace | - |
| Process Node | 4nm | 4nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | Rack-scale | Single-slot PCIe | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 384GB | 24GB | +1500% |
| Memory Type | HBM3e | GDDR6 | - |
| Memory Bandwidth | 16.0 TB/s | 300 GB/s | +5233% |
| Memory Bus Width | 8192-bit | 192-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 36,864 | 7,424 | +397% |
| Tensor Cores (AI) | N/A | 232 | |
| RT Cores (Ray Tracing) | N/A | 58 | |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 180 TFLOPS | 30.3 TFLOPS | +494% |
| FP16 (Half Precision) | 9,000 TFLOPS | 121 TFLOPS | +7338% |
| INT8 (Integer Precision) | 18,000 TOPS | N/A | |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 1200W | 72W | +1567% |
| PCIe Interface | PCIe 5.0 x16 | PCIe 4.0 x16 | - |
🎯 Use Case Recommendations
LLM & Large Model Training
NVIDIA GB200 NVL72
Higher VRAM capacity and memory bandwidth are critical for training large language models. The GB200 offers 384GB compared to 24GB.
AI Inference
NVIDIA GB200 NVL72
For inference workloads, performance per watt matters most. Consider the balance between FP16/INT8 throughput and power consumption.
Budget-Conscious Choice
NVIDIA L4
Based on current cloud pricing, the L4 starts at a lower hourly rate.
Technical Deep Dive: GB200 vs L4
This is a generational comparison within the NVIDIA ecosystem, pitting Blackwell against Ada Lovelace. The GB200 has a significant **360GB VRAM advantage**, which is crucial for training massive datasets or large language models. From a cost perspective, the **L4** is currently about **98% cheaper** per hour, offering better value for budget-conscious projects.
NVIDIA GB200 NVL72 is Best For:
- Massive LLM training
- Trillion-parameter models
- Single-node tasks
NVIDIA L4 is Best For:
- Edge AI inference
- Video transcoding
- Large model training
Frequently Asked Questions
Which GPU is better for AI training: GB200 or L4?
For AI training, the key factors are VRAM size, memory bandwidth, and tensor core performance. The GB200 offers 384GB of HBM3e memory with 16.0 TB/s bandwidth, while the L4 provides 24GB of GDDR6 with 300 GB/s bandwidth. For larger models, the GB200's higher VRAM capacity gives it an advantage.
What is the price difference between GB200 and L4 in the cloud?
Cloud GPU rental prices vary by provider and region. Based on our data, GB200 starts at $10.50/hour while L4 starts at $0.26/hour. This represents a 3938% price difference.
Can I use L4 instead of GB200 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 GB200, the L4 can be a cost-effective alternative. However, for workloads requiring maximum memory capacity or multi-GPU scaling, the GB200's architecture may be essential.
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