NVIDIA H100 PCIe VS NVIDIA L4
Choosing between **H100 PCIe** and **L4** depends on your specific AI workload requirements. The **H100 PCIe** 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 **$0.26/h** respectively across 32 providers.
H100 PCIe
📊 Detailed Specifications Comparison
| Specification | H100 PCIe | L4 | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Hopper | Ada Lovelace | - |
| Process Node | 4nm | 4nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | Dual-slot PCIe | Single-slot PCIe | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 80GB | 24GB | +233% |
| Memory Type | HBM3 | GDDR6 | - |
| Memory Bandwidth | 2.0 TB/s | 300 GB/s | +567% |
| Memory Bus Width | 5120-bit | 192-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 14,592 | 7,424 | +97% |
| Tensor Cores (AI) | 456 | 232 | +97% |
| RT Cores (Ray Tracing) | N/A | 58 | |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 51 TFLOPS | 30.3 TFLOPS | +68% |
| FP16 (Half Precision) | 1,513 TFLOPS | 121 TFLOPS | +1150% |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 350W | 72W | +386% |
| PCIe Interface | PCIe 5.0 x16 | PCIe 4.0 x16 | - |
🎯 Use Case Recommendations
LLM & Large Model Training
NVIDIA H100 PCIe
Higher VRAM capacity and memory bandwidth are critical for training large language models. The H100 PCIe offers 80GB compared to 24GB.
AI Inference
NVIDIA H100 PCIe
For inference workloads, performance per watt matters most. Consider the balance between FP16/INT8 throughput and power consumption.
Budget-Conscious Choice
NVIDIA L4
Compare live pricing to find the best value for your specific workload.
Technical Deep Dive: H100 PCIe vs L4
This is a generational comparison within the NVIDIA ecosystem, pitting Hopper against Ada Lovelace. The H100 PCIe has a significant **56GB VRAM advantage**, which is crucial for training massive datasets or large language models.
NVIDIA H100 PCIe is Best For:
- AI inference
- Enterprise AI
- Highest-end training
NVIDIA L4 is Best For:
- Edge AI inference
- Video transcoding
- Large model training
Frequently Asked Questions
Which GPU is better for AI training: H100 PCIe or L4?
For AI training, the key factors are VRAM size, memory bandwidth, and tensor core performance. The H100 PCIe offers 80GB of HBM3 memory with 2.0 TB/s bandwidth, while the L4 provides 24GB of GDDR6 with 300 GB/s bandwidth. For larger models, the H100 PCIe's higher VRAM capacity gives it an advantage.
What is the price difference between H100 PCIe and L4 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 L4 instead of H100 PCIe 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 H100 PCIe, the L4 can be a cost-effective alternative. However, for workloads requiring maximum memory capacity or multi-GPU scaling, the H100 PCIe's architecture may be essential.
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