NVIDIA B100 VS NVIDIA L4
Choosing between **B100** and **L4** depends on your specific AI workload requirements. The **B100** 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.
📊 Detailed Specifications Comparison
| Specification | B100 | L4 | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Blackwell | Ada Lovelace | - |
| Process Node | 4nm | 4nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | SXM | Single-slot PCIe | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 192GB | 24GB | +700% |
| Memory Type | HBM3e | GDDR6 | - |
| Memory Bandwidth | 8.0 TB/s | 300 GB/s | +2567% |
| Memory Bus Width | 8192-bit | 192-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 14,336 | 7,424 | +93% |
| Tensor Cores (AI) | 448 | 232 | +93% |
| RT Cores (Ray Tracing) | N/A | 58 | |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 70 TFLOPS | 30.3 TFLOPS | +131% |
| FP16 (Half Precision) | 3,500 TFLOPS | 121 TFLOPS | +2793% |
| TF32 (Tensor Float) | 1,750 TFLOPS | N/A | |
| FP64 (Double Precision) | 35 TFLOPS | N/A | |
| INT8 (Integer Precision) | 7,000 TOPS | N/A | |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 700W | 72W | +872% |
| PCIe Interface | PCIe 5.0 x16 | PCIe 4.0 x16 | - |
🎯 Use Case Recommendations
LLM & Large Model Training
NVIDIA B100
Higher VRAM capacity and memory bandwidth are critical for training large language models. The B100 offers 192GB compared to 24GB.
AI Inference
NVIDIA B100
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: B100 vs L4
This is a generational comparison within the NVIDIA ecosystem, pitting Blackwell against Ada Lovelace. The B100 has a significant **168GB VRAM advantage**, which is crucial for training massive datasets or large language models.
NVIDIA B100 is Best For:
- Large-scale AI training
- Budget deployments
NVIDIA L4 is Best For:
- Edge AI inference
- Video transcoding
- Large model training
Frequently Asked Questions
Which GPU is better for AI training: B100 or L4?
For AI training, the key factors are VRAM size, memory bandwidth, and tensor core performance. The B100 offers 192GB of HBM3e memory with 8.0 TB/s bandwidth, while the L4 provides 24GB of GDDR6 with 300 GB/s bandwidth. For larger models, the B100's higher VRAM capacity gives it an advantage.
What is the price difference between B100 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 B100 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 B100, the L4 can be a cost-effective alternative. However, for workloads requiring maximum memory capacity or multi-GPU scaling, the B100's architecture may be essential.
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