Nebius vs Lambda Labs: A Deep Dive into Cloud GPU Pricing and Performance
The cloud GPU market is moving fast. While Lambda Labs has been the go-to choice for AI researchers and engineers, a new player—Nebius—is gathering steam as an alternative.
Both offer high-performance GPUs for deep learning, scientific computing and large-scale simulations. But how do they compare on hardware, pricing and overall value?
You need to know the differences if you’re looking for the best cloud GPU provider. In this comparison, we’ll break down Nebius vs Lambda Labs, their GPU models, pricing and features. Let’s see if Nebius is ready to replace Lambda Labs or if the incumbent still holds the crown.
Table of Contents
Nebius vs Lambda Labs: Quick Comparison
Choose Nebius if:
- You want more flexible pricing models, including on-demand and reserved.
- You need cost-effective GPU instances with significant discounts for long-term use.
- You prefer transparent and predictable pricing without hidden fees.
- You require multiple NVIDIA GPU models (H200, H100, L40S) with unlimited scaling.
Choose Lambda Labs if:
- You need enterprise-class AI compute with up to 512 NVIDIA GPUs on demand.
- You want pre-configured GPU instances with one-click Jupyter access.
- You want multiple GPU models (B200, A100, A6000, RTX 6000).
- You prefer a dedicated AI cloud service for LLM training, fine-tuning and inference.
Nebius vs Lambda Labs: Overview
| Feature | Nebius | Lambda Labs |
|---|---|---|
| Pricing | Starts at $0.80/hr (L40S) to $2.30/hr (H200) | Starts at $0.50/hr (RTX 6000) to $4.49/hr (H100) |
| Reserved Discounts | Up to 45% discount for long-term commitments | Up to 45% discount for long-term reservations |
| GPU Models | H200, H100, L40S | H200, H100, A100, A10, A6000, RTX 6000, V100 |
| Scaling | Up to thousands of GPUs in Infiniband clusters | Up to 512 GPUs per cluster |
| Pre-configured AI Stack | No pre-configured ML frameworks | Includes Lambda Stack with PyTorch, TensorFlow, CUDA, etc. |
| Storage | $0.0147/GiB (Object Storage), $0.16/GiB (Shared SSD) | 26 TiB SSD storage for clusters |
| Networking | Up to 3.2Tb/s InfiniBand | 400Gbps per GPU, 3200Gbps per cluster |
| Best For | Scalable AI workloads at lower costs | Enterprise AI teams and LLM training |
GPU Models & Pricing Comparison
Nebius GPU Offerings
Nebius offers competitive on-demand and reserved pricing across three NVIDIA GPU models:
- NVIDIA H200 Tensor Core
- $3.50/hr on-demand, $2.30/hr reserved
- 141GB HBM3e memory, 200GB system RAM, 16 vCPUs
- Best for: Large-scale LLM training, deep learning, scientific research
- NVIDIA H100 Tensor Core
- $2.95/hr on-demand, $2.00/hr reserved
- 80GB HBM3 memory, 200GB system RAM, 16 vCPUs
- Best for: AI model training, inference, and multi-GPU workloads
- NVIDIA L40S
- $1.55/hr on-demand, $0.80/hr reserved
- 48GB VRAM, 32GB RAM, 8 vCPUs
- Best for: AI inference, video rendering, and moderate ML workloads
Lambda Labs GPU Offerings
Lambda provides a wider selection of GPUs, catering to different AI and ML needs:
- NVIDIA H100 SXM
- $4.49/hr on-demand, $2.49/hr reserved
- 80GB HBM3 memory, 208 vCPUs, 1800GB RAM
- Best for: Enterprise-scale AI, fine-tuning large LLMs
- NVIDIA A100 SXM (80GB)
- $1.79/hr on-demand
- 80GB VRAM, 240 vCPUs, 1800GB RAM
- Best for: Inference and training of generative AI models
- NVIDIA A6000
- $0.80/hr on-demand
- 48GB VRAM, 400GB RAM, 1 TiB SSD storage
- Best for: Graphics-intensive AI applications, 3D rendering
- NVIDIA RTX 6000
- $0.50/hr on-demand
- 24GB VRAM, 46GB RAM
- Best for: Entry-level ML and general compute workloads
AI & Large Language Model (LLM) Support
Nebius: LLM Training Capabilities
Nebius is a top GPU cloud provider with a high-performance cloud platform for large-scale AI training. Suitable for training large language models (LLMs) like GPT-4, Llama 2, Falcon, and Mistral, it’s a software solution for AI teams. With multi-GPU instances, Nebius enables seamless multi-node distributed training so researchers and developers can train AI models with maximum performance.
One of the main advantages of Nebius is its advanced infrastructure, with high-speed InfiniBand networking. This speeds up communication between GPUs, reducing training time and making it efficient for large machine learning workloads. Nebius also offers big discounts for long-term commitments, so enterprises can balance cost and performance.
Nebius also scales easily with its GPU clusters, so users have the computing power to handle demanding AI workloads. With flexible pricing models, including reserved instances, companies can optimize costs while having access to top-tier GPUs like NVIDIA H100 and H200 Tensor Core GPUs.
Lambda Labs: AI Training & Fine-Tuning
Lambda Labs is a well-known player in the AI industry with a track record of providing cloud infrastructure for AI training. It specializes in open-source LLMs like Llama, Mistral, Falcon, BERT, MPT, and Grok and offers a software solution for AI research and development.
With high-performance multi-GPU instances, Lambda Labs lets organizations train AI models at scale, with clusters of up to 512 NVIDIA GPUs. The company’s infrastructure has a pre-installed AI environment, Lambda Stack, which simplifies setup for machine learning teams.
Compared to Google Cloud, Lambda Labs stands out with transparent pricing, no egress fees and big discounts on long-term reserved instances. So it’s a cost-effective choice for AI teams to scale their ai development while having access to top-tier GPU resources.
Scalability & Networking
Nebius Scalability Features
Nebius has a highly scalable infrastructure that can go from 1 GPU to thousands of GPUs in a single cluster. Perfect for AI teams that need to scale without hardware limitations.
One of the highlights is the 3.2 terabits per second (Tb/s) InfiniBand networking, which enables fast communication between GPUs, minimizing latency during AI training. This is crucial for large-scale distributed training, especially when working with large AI models that need real-time data exchange between multiple GPUs.
Also, Nebius has flexible instance resizing so you can allocate compute resources based on your project requirements. Whether scaling up for large training runs or scaling down to save costs, Nebius ensures you can manage your resources without delays or provisioning issues.
Lambda Labs Scalability Features
Lambda Labs has a scaling framework designed for AI development that can scale from 16 to 512 GPUs per cluster. You can start with small experiments and scale up to large-scale production training.
Lambda’s networking is also impressive, with dedicated 400 Gbps per GPU, which is perfect for teams working on large language models and other complex AI workloads where data flow is king.
Also, Lambda Labs uses non-blocking InfiniBand networking, which is an architecture designed to eliminate bottlenecks in distributed computing. So, each GPU in a multi-node setup can operate at full speed, which is perfect for teams that want to deploy high-performance distributed training at scale.
Storage & Data Transfer Costs
Nebius Storage Pricing
Nebius has a storage pricing model designed for AI and machine learning workloads. Object Storage is $0.0147 per GiB per month for large datasets and AI model checkpoints.
For higher performance needs, Nebius has a Shared Filesystem SSD at $0.16 per GiB per month for low-latency data access for intense AI workloads. For data transfer, Nebius charges an egress traffic fee of $0.015 per GiB. This should be factored in if your team moves large amounts of data across the network.
Lambda Labs Storage Pricing
Lambda Labs has 26TiB of high-speed SSD storage per GPU cluster, optimized for high-performance AI computing. Integrated storage means no additional provisioning, and data is always close to the compute resources, minimizing transfer delays.
A big plus for Lambda Labs is no egress fees. You can move data in and out of the platform without surprise costs. This is great for AI teams that want predictable storage costs and high-speed data access without extra transfer fees.
Which One Should You Choose?
Go with Nebius if:
- You need cheap AI compute with long-term discounts.
- You want to scale your GPU clusters for AI training workloads.
- You prefer a pay-as-you-go pricing.
Go with Lambda Labs if:
- You want enterprise AI infrastructure with high-end GPU models.
- You need pre-configured ML environments for faster deployment.
- You want multi-node AI clusters for training massive LLMs.
Both Nebius and Lambda Labs offer cloud GPU power, but they are for different audiences. Nebius is for cost-effective scaling, and Lambda Labs is for enterprise AI for researchers and engineers. Your choice depends on your budget, project size, and AI compute needs.
