Contents
- Pricing Overview
- GPU Specifications
- Lambda Labs Instance Options
- Rental Process and Setup
- FAQ
- Related Resources
- Sources
Pricing Overview
Lambda Labs doesn't offer RTX 4090. Lambda focuses on professional GPUs: A100, A6000, RTX 6000, A10. If developers want 4090, try RunPod ($0.34/hr) or Vast.ai.
This guide is for reference only. Use Lambda's A10 ($0.86/hr) as an alternative if developers need a mid-range card with SLA backing.
GPU Specifications
RTX 4090 specs: 16,384 CUDA cores, 24GB GDDR6X, 1,008 GB/s bandwidth. FP32: 82.6 TFLOPS. FP16: 165 TFLOPS. PCIe Gen 4.
Good for inference, light fine-tuning, generative tasks. Fits 7B-13B models in full precision. Dev teams use it before scaling to bigger GPU fleets.
Lambda Labs Instance Options
Lambda doesn't offer RTX 4090. But if renting 4090 elsewhere (RunPod, Vast.AI), instances typically include 30 CPU cores, 256GB RAM, 1TB NVMe. Storage and reserved capacity options available.
Spot pricing (cheaper, preemptible) vs on-demand (guaranteed uptime). RunPod spot: ~$0.22/hr, on-demand: $0.34/hr.
Instances launch in minutes. SSH and Docker built-in.
Rental Process and Setup
Account setup (elsewhere): credentials + verification, ~24 hours for approval.
Launch: select GPU, choose OS, set resources, pick storage. Defaults to PyTorch/TensorFlow images, but custom Docker works.
Startup in seconds. SSH key auth. Billing by the minute from provisioning.
FAQ
What model sizes fit on RTX 4090? Full precision: ~12B params. With INT8/INT4 quantization: 50B+ params (inference only).
4090 vs A100? A100's bandwidth (1.9TB/s) is 3x faster. Training is 2-4x faster on A100. 4090 is fine for inference and <13B models.
Can I run multiple instances of RTX 4090 together? Yes. RunPod supports multi-GPU pods with RTX 4090s connected via PCIe. Distributed training frameworks including PyTorch Distributed and Horovod require configuration for cross-instance communication. Note: no NVLink between consumer GPUs limits scaling efficiency.
What are typical uptime guarantees for RTX 4090 rentals? On-demand instances target 99.5% availability. Spot instances may terminate with short notice. Teams requiring guaranteed uptime should reserve capacity in advance or use on-demand pricing.
Is RTX 4090 suitable for production inference services? Yes, for moderate traffic. Single RTX 4090 instances handle approximately 5-10 concurrent inference requests depending on model size and response time requirements. Higher throughput needs benefit from multiple GPU instances or larger models like H100.
Related Resources
Explore broader Lambda GPU pricing options and compare with RunPod GPU pricing. Understand RTX 4090 specifications in detail and compare against H100 specs for larger workloads.