Specifications Compared
| Spec | L4 | QUADRO-RTX-5000 |
|---|---|---|
| TDP | 72W | 230W |
| VRAM | 24 GB | 16 GB |
| CUDA Cores | 7,424 | 3,072 |
| Memory Type | GDDR6 | GDDR6 |
| Architecture | Ada Lovelace | Turing |
| Form Factors | PCIe | PCIe |
| Interconnect | PCIe 4.0 | NVLink |
| Tensor Cores | 232 | 384 |
| FP8 Performance | 242 TFLOPS | |
| FP16 Performance | 121 TFLOPS | 11.2 TFLOPS |
| FP32 Performance | 30.3 TFLOPS | 11.2 TFLOPS |
| FP64 Performance | 0.5 TFLOPS | |
| INT8 Performance | 242 TOPS | |
| Memory Bandwidth | 300 GB/s | 448 GB/s |
Performance Analysis
The L4's compute superiority shines in AI workloads: its 121 TFLOPS FP16 performance enables faster mixed-precision training and inference compared to the Quadro RTX 5000's 11.2 TFLOPS, reducing epochs by over 10 times in large language models. The FP32 rate of 30.3 TFLOPS on the L4 versus 11.2 TFLOPS on the Quadro benefits simulation tasks requiring single precision.
FP8 support at 242 TFLOPS on the L4 accelerates quantized inference, a feature unavailable on the Turing-based Quadro, allowing sub-8-bit models to run with minimal accuracy loss. Memory bandwidth of 448 GB/s on the Quadro supports larger batch sizes in memory-bound scenarios despite its 16 GB VRAM limit, while the L4's 24 GB VRAM and 300 GB/s bandwidth handle bigger datasets overall.
Power efficiency defines real-world viability: the L4's 72W TDP versus 230W on the Quadro cuts cooling costs by over 70 percent and suits dense cloud racks. Newer Ada Lovelace tensor cores on the L4 optimize sparse operations, outperforming Turing in transformers.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
L4
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Vast.ai | NVIDIA L4 24GB VRAM | 24GB | 64 vCPU 101GB RAM 485GB Storage | Iceland | $0.33/GPU/hr | Available | ||
 RunPod | NVIDIA L4 24GB VRAM | 24GB | 12 vCPU 50GB RAM | 🌍global | $0.39/GPU/hr | |||
 TensorDock | NVIDIA L40S 48GB VRAM | 48GB | 0 vCPU 0GB RAM | Wolverhampton | $0.55/GPU/hr | Available | ||
RunPod | NVIDIA L40 48GB VRAM | 48GB | 8 vCPU 94GB RAM | 🌍global | $0.82/GPU/hr | |||
RunPod | NVIDIA L40S 48GB VRAM | 48GB | 16 vCPU 94GB RAM | 🌍global | $0.86/GPU/hr |
Quadro RTX 5000
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Paperspace | NVIDIA Quadro RTX 5000 16GB VRAM | 16GB | 8 vCPU 30GB RAM 50GB Storage | New York | $0.82/GPU/hr | Available | ||
Paperspace | 2×NVIDIA Quadro RTX 5000 16GB VRAM | 16GB | 16 vCPU 60GB RAM 50GB Storage | New York | $0.82/GPU/hr $1.64/hr total (2×) | Available |
When to Choose the L4
The L4 excels in modern AI inference and training where FP16 at 121 TFLOPS and FP8 at 242 TFLOPS accelerate large models. Its 24 GB VRAM fits LLMs up to 70B parameters, and 72W TDP enables cost-effective scaling in clouds at $0.32 per hour starting price.
Choose the L4 for energy-constrained environments or high-volume deployments needing PCIe 4.0 speed.
When to Choose the Quadro RTX 5000
The Quadro RTX 5000 suits legacy CAD or visualization software optimized for Turing, leveraging 448 GB/s bandwidth for high-resolution rendering with large batches. Its NVLink interconnect aids multi-GPU setups in older professional workflows.
Select it if applications demand maximum bandwidth per watt in pre-2020 codebases, despite 230W TDP.
Use Cases
L4's 121 TFLOPS FP16 and 30.3 TFLOPS FP32 enable faster training of large models compared to Quadro's 11.2 TFLOPS. Its 24 GB VRAM supports bigger batches.
FP8 at 242 TFLOPS and 24 GB VRAM on L4 optimize quantized serving. Quadro lacks FP8 and has only 16 GB VRAM.
L4's higher FP16/FP32 rates and lower $0.68 average hourly cost speed iterations. Efficiency at 72W reduces expenses.
Ada Lovelace architecture with 121 TFLOPS FP16 generates images faster than Turing's 11.2 TFLOPS. 24 GB VRAM handles high-res workflows.
L4 suits FP16-heavy simulations at 121 TFLOPS; Quadro's 448 GB/s bandwidth aids FP32-bound tasks at 11.2 TFLOPS.
Frequently Asked Questions
Which GPU has more VRAM, L4 or Quadro RTX 5000?▾
The L4 provides 24 GB GDDR6 VRAM, exceeding the Quadro RTX 5000's 16 GB. This allows the L4 to load larger models without offloading.
How do FP16 performance levels compare between L4 and Quadro RTX 5000?▾
L4 achieves 121 TFLOPS in FP16, over 10 times the Quadro RTX 5000's 11.2 TFLOPS. This gap accelerates AI training and inference significantly.
What are the power consumption differences?▾
L4 draws 72W TDP, far lower than the Quadro RTX 5000's 230W. The L4 offers better efficiency for cloud scaling.
Which is cheaper in the cloud?▾
L4 starts at $0.32 per hour with $0.68 average across 15 offers, versus Quadro RTX 5000 at $0.82 per hour across 2 offers. L4 provides superior value.
Does L4 support FP8 compute?▾
Yes, L4 delivers 242 TFLOPS in FP8 for quantized inference. Quadro RTX 5000 lacks FP8 capability.
How does memory bandwidth compare?▾
Quadro RTX 5000 has 448 GB/s, higher than L4's 300 GB/s. However, L4's 24 GB VRAM compensates in most workloads.
Which is cheaper to rent, the L4 or the Quadro RTX 5000?▾
Cloud rental prices for both the L4 and Quadro RTX 5000 vary by provider, configuration, and availability. This page shows live pricing from 25+ providers updated every 60 seconds. Scroll to the Live Cloud Pricing section to compare current rates.
How much VRAM does the L4 have compared to the Quadro RTX 5000?▾
The L4 has 24 GB of GDDR6 memory. The Quadro RTX 5000 has 16 GB of GDDR6 memory.
Can I find L4 and Quadro RTX 5000 GPUs available to rent right now?▾
Yes. This page shows real-time availability across 25+ cloud GPU providers. The Live Cloud Pricing section displays only in-stock offers with current pricing.
What is the main difference between the L4 and the Quadro RTX 5000?▾
The L4 uses the Ada Lovelace architecture (2023) while the Quadro RTX 5000 uses Turing (2018). The L4 delivers 10.8x the FP16 throughput and 1.5x the memory bandwidth of the Quadro RTX 5000.