Lambda Labs vs AWS: GPU Cloud Comparison

You’re entering the world of GPU cloud services and you’ve come across two big names—Lambda Labs and AWS. Both offer solutions for compute-intensive workloads like AI training and inference but they approach it differently. AWS has well-known EC2 instances, offering flexible scaling and a wide range of configurations for any workload.

Lambda Labs focuses on AI and offers on-demand NVIDIA GPUs like the H100 and H200 with competitive pricing. But which one gives you more for your money? Let’s get into the details in this Lambda Labs vs AWS comparison and see how their GPU cloud services compare.

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AWS vs Lambda Labs: The Key Difference

The key difference between AWS and Lambda Labs is that AWS has many different GPU instances for all sorts of workloads like graphics rendering, AI, and HPC, and Lambda Labs is optimized for deep learning and AI and has pre-configured high-performance GPUs for large-scale AI training and research.

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Lambda Labs vs AWS: GPU Compute Comparison

Let’s break down the pros and cons of these two GPU providers, looking at GPU models, architecture, storage, pricing, use cases, and more.

GPU Models Offered

AWS: Variety and Flexibility

AWS has many GPU cloud instance options, each for a specific use case. AWS has G3, G4, G5 and P2 to P5 instance families, with different NVIDIA GPU models (Tesla M60, T4, A100, A10G, V100, H100) and even AMD Radeon Pro GPUs. This gives AWS the ability to support a wide range of applications from low-cost machine learning inference to high-performance computing (HPC).

Tesla M60 (G3 Instances):

The Tesla M60 is designed for graphics-intensive workloads such as 3D rendering, video processing, and virtual desktop infrastructure (VDI). Each M60 has 8 GB of GDDR5 memory and supports NVIDIA GRID technology making it suitable for multi-user virtual desktops.

Although older, the M60 is still good for workloads that require high visual fidelity like CAD/CAM applications or high-end gaming in virtualized environments. Not the top choice for AI or machine learning workloads but the price-to-performance ratio makes it viable for certain graphic-centric tasks that don’t require the latest hardware.

NVIDIA T4 (G4dn Instances):

NVIDIA T4 GPUs are designed for low-cost, power-efficient machine learning inference, graphics rendering, and video transcoding. Available with G4dn instances, they have 16 GB of GDDR6 memory and are great for AI workloads like natural language processing, object detection, and recommendation systems.

These GPUs are a balance of price and performance, perfect for organizations with smaller deep learning models, inference jobs, or those who need cost-effective GPU options for virtual desktops. They can scale from small training to inference workloads without significant cost overhead.

AMD Radeon Pro V520 (G4ad Instances):

The AMD Radeon Pro V520, available with G4ad instances, is for customers who want strong graphics for a lower price. Great for workloads like game streaming, remote workstation rendering, and media encoding, the V520 has up to 32 GB of GDDR6 memory.

With a 45% better price-to-performance ratio compared to the NVIDIA T4-based G4dn instances, it’s a great option for budget-conscious users. G4ad instances are popular for high-resolution graphics rendering, virtualized workstations, and content creation environments that require high visual fidelity but don’t need the latest AI acceleration features.

NVIDIA A10G (G5 Instances):

The NVIDIA A10G GPU in G5 instances is designed for high-performance workloads in machine learning and real-time graphics rendering. It has Tensor Cores and 24 GB of GDDR6 memory, perfect for AI inference and training of smaller models and graphics-intensive applications like 3D visualization and virtual production.

Support for up to 8 GPUs per instance makes A10G great for companies that need to train models on medium to large datasets, do real-time inference, or render high-quality visuals without compromise. The combination of graphics and AI makes A10G a versatile tool for hybrid workloads.

NVIDIA A100 (P4 Instances):

NVIDIA A100 GPUs in AWS P4 instances are the latest in high-performance computing (HPC) and large scale machine learning training. With up to 80 GB of high-bandwidth memory (HBM2e) the A100 is ideal for training massive AI models like those used for natural language understanding, image recognition, and complex scientific simulations.

Built on the Ampere architecture these GPUs have third-generation Tensor Cores for significant performance gains, faster training times, and more efficient use of resources. They also support multi-instance GPU technology so multiple users can share a single GPU, reducing costs while maintaining high throughput.

NVIDIA H100 (P5 Instances):

The NVIDIA H100 is the latest GPU in AWS’s P5 instances and is designed for the most demanding AI and HPC workloads. Built on the Hopper architecture it has unprecedented levels of compute power, over 20 exaflops of AI performance, and up to 3,200 Gbps of networking throughput.

The H100 is great for deep learning training of extremely large models and distributed workloads, perfect for cutting-edge AI research, neural network development, and large-scale simulations. These GPUs have enhanced tensor processing and transformer engine acceleration, up to 30x faster training for AI models than previous generations, and AI compute is reimagined.

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Lambda Labs: AI-Centric GPU Powerhouse

Lambda Labs offers a simpler AI-focused GPU offering with a main focus on NVIDIA H100 and A100 Tensor Core GPUs. While AWS has a wider range of GPUs, Lambda Labs focuses on the highest-performance GPUs for deep learning and AI training.

NVIDIA H100 Tensor Core GPUs:

The flagship product for Lambda Labs, H100 GPUs are perfect for training large language models, complex AI computations, and advanced workloads like transformer models and diffusion models. Lambda Lab’s clusters can scale from 16 to 512 H100 GPUs, ideal for AI researchers and businesses with heavy compute needs.

NVIDIA A100 Tensor Core GPUs:

Lambda also offers A100 GPUs which are suitable for AI training, inference, and data analytics. They have 80 GB of GPU memory per GPU and are available in 1 to 8 GPUs per instance, optimized for both deep learning training and inference workloads.

Lambda Labs is heavily optimized for AI use cases, so it’s a preferred choice for AI researchers and developers, especially those working with large language models and other advanced AI applications. Unlike AWS which offers cloud GPUs for many use cases, Lambda Labs’ offerings are focused primarily on deep learning, AI, and data-heavy workloads.

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Architecture and Storage

AWS: Highly Customizable Instances

AWS has highly configurable architectures, you can choose from a wide range of vCPUs, memory, storage, and networking. Instance sizes can go from single-GPU instances like G3s.xlarge with 4 vCPUs and 30.5 GiB of memory to high-end configurations like P5.48xlarge with 192 vCPUs, 2 TiB of system memory and 8 NVIDIA H100 Tensor Core GPUs.

Instance Storage:

AWS has both NVMe SSD local storage and elastic block storage (EBS) for short-term high-performance storage and long-term scalable storage needs. Storage can scale up to 7.6 TB of NVMe SSD (G5 instances) and network bandwidth up to 400 Gbps (P5 instances).

Network Bandwidth:

Networking on AWS scales for massive datasets and distributed workloads. For example, P4d instances have 400 Gbps networking, and P5 instances can scale up to 3,200 Gbps with Elastic Fabric Adapter (EFA) for HPC and distributed AI training workloads.

Lambda Labs: Pre-Optimized for AI

Lambda Labs is designed to simplify the architecture decision-making process. Not as customizable as AWS, but the infrastructure is optimized for AI workloads. Each instance is pre-configured for AI performance, storage, network, and memory and is designed for large-scale machine learning.

Pre-installed Frameworks:

Lambda’s instances come pre-installed with machine learning software like TensorFlow, PyTorch and the Lambda Stack. Users can start training models without having to spend time on infrastructure setup, perfect for AI professionals who want to avoid configuration delays.

Storage:

Lambda Labs has high-performance SSD storage across their instances, up to 26 TiB for H100 GPU clusters. However, they don’t have the same level of storage options and flexibility as AWS, so Lambda Labs is more suitable for short-term high-speed storage needs in AI workloads.

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Pricing

AWS: Pay-As-You-Go and Reserved Pricing

AWS offers on-demand pricing, spot instances and reserved instances. This gives customers the ability to balance performance needs with budget.

On-Demand Pricing:

For example a G4dn.xlarge (NVIDIA T4) is $0.526 per hour on-demand and the top-tier P5.48xlarge (NVIDIA H100) is $32.77 per hour. This gives AWS the ability to support customers from small businesses to large enterprises.

Reserved Instances:

By choosing reserved instances (1 or 3 years) customers can save up to 75%. A 1 year reserved G4dn.xlarge can be $0.316 per hour and long-term reserved instances on the H100-based P5 can save big for continuous large workloads.

AWS’s flexibility in pricing models makes it attractive for companies that need to scale their compute on-demand but want the option to optimize their long-term costs.

Lambda Labs: Simple, Transparent Pricing

Lambda Labs has a simpler, more transparent pricing model for AI workloads. On-demand H100 GPU access is $4.49 per GPU per hour. For those looking to commit longer term, Lambda has deep discounts for reserved clusters:

Reserved Pricing:

A 12-month commitment is $2.49 per GPU per hour. Lambda Labs is very competitive for enterprises with long-term AI needs. Discounts up to 45% for those who need consistent high-performance GPU access for extended periods.

Unlike AWS, Lambda’s pricing is highly focused on GPU usage, without the complexities of additional storage or networking costs, making it an attractive, predictable option for deep learning applications.

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Use Cases

AWS: Versatility Across Multiple Workloads

AWS has many GPU options and instance types to support multiple workloads:

• Graphics and Rendering: G3 with NVIDIA Tesla M60 GPUs for graphics-heavy tasks like 3D rendering, gaming, and virtual desktops.
• AI Inference and Training: G4dn with NVIDIA T4 GPUs for small-scale machine learning training and AI inference.
• HPC and Distributed AI: P4d and P5 with A100 and H100 GPUs for deep learning training. AWS can handle workloads that require massive computing and networking.
• Gaming and Virtual Desktops: AMD Radeon-powered G4ad for cloud gaming, remote workstations, and real-time rendering at a lower price point than many of AWS’s other options.

AWS can handle any workload with its many instance types so it’s perfect for companies with varied computing needs across AI, graphics, and data-heavy workloads.

Lambda Labs: Built for Deep Learning and AI

Lambda Labs is built for deep learning and AI workloads and excels at large-scale model training, especially open-source frameworks. It’s the go-to platform for users who focus heavily on AI training, fine-tuning and inference:

• Large Language Models: Lambda Labs is optimized for training large-scale transformer models like GPT, LLaMA, and other state-of-the-art AI models that require a lot of GPU resources.
• Deep Learning Research: Academic institutions and research labs can benefit from Lambda’s pre-configured environments and dedicated deep learning clusters that are built for heavy-duty AI workloads.
• Scalable AI Training: Lambda allows AI professionals to scale from 16 to 512 GPUs so it’s perfect for enterprises or research teams that need to train AI models across massive datasets.

While AWS has broader applications, Lambda Cloud is a clear choice for professionals looking to exclusively focus on AI research and development.

AWS vs Lambda Labs: The Bottom Line

Ultimately it comes down to your use case and workload.

AWS is for companies that need flexibility across different workloads—graphics rendering, HPC, AI inference, and large-scale training. With multiple GPU options, lots of storage and networking options, and flexible pricing models, AWS is for businesses that need different computing power and scale.

Lambda Labs is for deep learning and AI. Simple pricing, AI-focused infrastructure and H100 GPU clusters make it the top choice for AI researchers and businesses training large deep learning models.

If you need an AI-specific infrastructure Lambda Labs is the way to go. If you need different computing power AWS is the better choice.

Lambda Labs vs AWS: Frequently Asked Questions

Who are Lambda Labs’ competitors?

Lambda Labs competes with CUDO Compute, Google Cloud Platform, Microsoft Azure, and other cloud providers that offer GPU instances for AI and machine learning. Companies like AWS also compete with Lambda Labs by offering customizable instances for different workloads. Lambda Labs has an edge because it owns its own hardware and optimizes AI workloads like stable diffusion. Other providers differentiate by offering persistent storage, low latency, and free credits for new users.

Who uses Lambda Labs?

Lambda Labs is used by AI researchers, machine learning engineers and data scientists who need powerful hardware for computational tasks. It’s popular with organizations and individuals working on deep learning, natural language processing, and large-scale neural network training due to its cost-effectiveness and performance optimization for GPU-based computing.

Is Lambda Labs bare metal?

Yes, Lambda Labs offers bare metal servers. These dedicated servers give users direct access to the hardware without any virtualization layer. This setup allows for maximum performance, so Lambda Labs is popular with those who need full control over the hardware for intense AI and machine learning workloads.

Is lambda GPU legit?

Yes, lambda GPU is legit. Used by AI pros and researchers for its GPU cloud services. With competitive pricing and performance, many use Lambda for heavy computation tasks like deep learning and AI training.

When it comes to fueling your AI and machine learning initiatives, CUDO Compute offers powerful and affordable cloud GPU solutions that are purpose-built for high performance. Sign up now!