Contents
- L4 GPU Specifications
- RunPod L4 Pricing
- Deploying L4 on RunPod
- Performance Metrics
- Market Comparison
- FAQ
- Related Resources
- Sources
L4 GPU Specifications
L4 Runpod Pricing is the focus of this guide. The NVIDIA L4 represents an entry-level data center GPU optimized for inference and encoding tasks. This processor features 24GB of GDDR6 memory, 7,424 CUDA cores, and low power consumption of just 72W.
L4 RunPod pricing stands at $0.44 per hour, making it the most budget-friendly option for cost-sensitive inference workloads. Specifications include:
- Memory: 24GB GDDR6
- Memory Bandwidth: 300 GB/s
- CUDA Cores: 7,424
- Peak FP32 Performance: 30.3 TFLOPS
- Max Power: 72W
- Memory Speed: 18 Gbps
The L4 targets budget-conscious teams deploying inference servers, image processing pipelines, and video transcoding. Its low power and thermal profile suits multi-GPU deployments in data centers.
Explore more in the L40S specs guide for comparison with higher-tier options.
RunPod L4 Pricing
RunPod offers L4 instances at $0.44 per hour for shared deployments. This pricing represents the lowest entry point for GPU acceleration on the platform.
Cost breakdown:
- Shared Instance: $0.44/hour
- Dedicated Instance: $0.65-0.95/hour
- Monthly Commitment: 20-25% discount available
Unlike premium GPUs, the L4 scales affordably across multiple units. An 8-GPU L4 cluster costs approximately $3.52 per hour ($0.44 x 8), enabling cost-effective distributed inference.
Storage runs at $0.01 per GB monthly. Network egress through RunPod's cloud remains free.
For broader pricing context, compare with Lambda GPU pricing and CoreWeave GPU pricing.
Deploying L4 on RunPod
L4 deployment on RunPod follows a simple process:
- Log into RunPod.io
- Navigate to Pods section
- Click "Create New Pod"
- Enter "L4" in the GPU search field
- Select shared or dedicated instance type
- Choose a container template (Ubuntu, PyTorch)
- Set storage size (typically 20-50GB)
- Click "Run Pod"
Instances launch in 30-60 seconds. SSH access enables immediate terminal connection. RunPod provides a web-based notebook IDE if preferred over SSH.
Common setup patterns:
- Deploying Ollama for open-source LLM inference
- Running text-to-image pipelines with Stable Diffusion
- Building video encoding services
- Creating low-cost chatbot backends
Network access requires port mapping. Standard ports (22 for SSH, 8000 for APIs) open automatically.
Check Paperspace GPU pricing and AWS GPU pricing for alternative deployment environments.
Performance Metrics
L4 performance suits inference-heavy tasks. Token generation reaches 50-100 tokens per second on smaller models.
Inference benchmarks:
- Llama 2 7B: 80-120 tokens/sec
- Mistral 7B: 100-140 tokens/sec
- TinyLLama 1B: 300-400 tokens/sec
Image generation:
- Stable Diffusion (512x512): 3-5 iterations/sec
- ControlNet poses: 2-3 per second
The L4 cannot run large models (70B parameters) at useful speeds. Quantization helps, reducing memory to fit 13B models comfortably.
Training is possible but slow. Fine-tuning small models (1-3B) runs at roughly 100-150 tokens/sec with gradient checkpointing enabled.
Market Comparison
L4 pricing positions it as the ultra-budget option. Competing providers offer few alternatives at this price point.
| Provider | L4 Price | Availability | Use Case |
|---|---|---|---|
| RunPod | $0.44/hr | Global | Budget inference |
| Lambda | No L4 | N/A | No entry GPU |
| Vast.AI | $0.30-0.50/hr | Varies | Market-based pricing |
| Crusoe | $0.35/hr | US | Inference |
| AWS | $0.35+/hr | Global | General compute |
RunPod's L4 pricing ranks competitively. Vast.AI sometimes undercuts on spot-like pricing, but availability fluctuates. AWS offers similar GPUs (T4) at comparable rates.
FAQ
Can the L4 run larger models? With 4-bit quantization, the L4 fits 13B models. 70B models require multiple L4s or a higher-tier GPU.
What is the power consumption? The L4 draws just 72W, so data centers can pack many units per rack.
How does L4 compare to T4? The L4 has roughly 30% higher performance than the older T4 while using identical power.
Can I upgrade mid-session? You must stop the pod and launch a new instance. Data on network storage persists.
Does RunPod bill per minute? Yes, RunPod charges per second of active use.
Related Resources
Sources
- NVIDIA L4 Tensor GPU Product Datasheet
- RunPod Official Documentation & Pricing
- NVIDIA CUDA Developer Tools & Documentation