NVIDIA H100 SXM VS NVIDIA L4
Choosing between **H100 SXM** and **L4** depends on your specific AI workload requirements. The **H100 SXM** 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 **$0.73/h** and **$0.26/h** respectively across 78 providers.
📊 Detailed Specifications Comparison
| Specification | H100 SXM | L4 | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Hopper | Ada Lovelace | - |
| Process Node | 4nm | 4nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | SXM5 | Single-slot PCIe | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 80GB | 24GB | +233% |
| Memory Type | HBM3 | GDDR6 | - |
| Memory Bandwidth | 3.35 TB/s | 300 GB/s | +1017% |
| Memory Bus Width | 5120-bit | 192-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 16,896 | 7,424 | +128% |
| Tensor Cores (AI) | 528 | 232 | +128% |
| RT Cores (Ray Tracing) | N/A | 58 | |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 67 TFLOPS | 30.3 TFLOPS | +121% |
| FP16 (Half Precision) | 1,979 TFLOPS | 121 TFLOPS | +1536% |
| TF32 (Tensor Float) | 989 TFLOPS | N/A | |
| FP64 (Double Precision) | 34 TFLOPS | N/A | |
| INT8 (Integer Precision) | 3,958 TOPS | N/A | |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 700W | 72W | +872% |
| PCIe Interface | PCIe 5.0 x16 | PCIe 4.0 x16 | - |
| Multi-GPU Interconnect | NVLink 4.0 (900 GB/s) | None | - |
🎯 Use Case Recommendations
LLM & Large Model Training
NVIDIA H100 SXM
Higher VRAM capacity and memory bandwidth are critical for training large language models. The H100 SXM offers 80GB compared to 24GB.
AI Inference
NVIDIA H100 SXM
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: H100 SXM vs L4
This is a generational comparison within the NVIDIA ecosystem, pitting Hopper against Ada Lovelace. The H100 SXM has a significant **56GB VRAM advantage**, which is crucial for training massive datasets or large language models. From a cost perspective, the **L4** is currently about **64% cheaper** per hour, offering better value for budget-conscious projects.
NVIDIA H100 SXM is Best For:
- LLM training
- Foundation model pre-training
- Small-scale inference
NVIDIA L4 is Best For:
- Edge AI inference
- Video transcoding
- Large model training
Frequently Asked Questions
Which GPU is better for AI training: H100 SXM or L4?
For AI training, the key factors are VRAM size, memory bandwidth, and tensor core performance. The H100 SXM offers 80GB of HBM3 memory with 3.35 TB/s bandwidth, while the L4 provides 24GB of GDDR6 with 300 GB/s bandwidth. For larger models, the H100 SXM's higher VRAM capacity gives it an advantage.
What is the price difference between H100 SXM and L4 in the cloud?
Cloud GPU rental prices vary by provider and region. Based on our data, H100 SXM starts at $0.73/hour while L4 starts at $0.26/hour. This represents a 181% price difference.
Can I use L4 instead of H100 SXM 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 SXM, the L4 can be a cost-effective alternative. However, for workloads requiring maximum memory capacity or multi-GPU scaling, the H100 SXM's NVLink support (NVLink 4.0 (900 GB/s)) may be essential.
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