Comparative Analysis of Leading GPU Vendors: CoreWeave, Lambda, Modal, OctoML, Together AI
Read about 2023's leading GPU vendors - CoreWeave, Lambda, Modal, OctoML, Together AI - for AI & ML: features, pricing, performance in a detailed review.
Diverse Range of Offerings: Each GPU vendor specializes in different areas, with CoreWeave focusing on high-end tasks, Lambda and Modal catering to smaller teams, and OctoML and Together AI offering unique solutions like AI model optimization and environmentally friendly computing.
Cost-Effective Solutions: CoreWeave provides high performance at competitive prices, while Lambda offers transparent, developer-friendly pricing. Modal's pay-per-use model is ideal for budget-conscious projects.
Innovative Technologies: OctoML stands out with its self-optimizing AI model deployment, and Together AI emphasizes high scalability and the integration of cutting-edge technologies like network compression and the Sophia optimizer.
Scalability and Flexibility: Modal's containerized approach allows for rapid scaling, and Together AI's GPU clusters offer extensive scalability options. CoreWeave's serverless Kubernetes infrastructure caters to innovative product development.
Environmental Considerations: Together AI offers the option to choose environmentally friendly data centers, aligning with the growing emphasis on sustainable computing solutions.
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In the dynamic world of artificial intelligence (AI) and machine learning, the role of GPU vendors is critically foundational for large language models. These powerful GPUs are not just components; they are the catalysts that propel complex AI algorithms and deep learning processes into action.
This article navigates through the contributions and innovations of five notable companies in this sector: CoreWeave, Lambda, Modal, OctoML, and Together AI. Each of these entities brings a unique approach to harnessing GPU power, shaping the landscape of AI computation and development. We’ll also do a comparative analysis of these GPU vendors, diving deep into the features of each.
CoreWeave
CoreWeave, founded in 2017, began as an Ethereum mining venture, evolving into a general-purpose cloud provider platform. CoreWeave's technology revolves around Nvidia GPUs, catering to a wide range of applications such as AI and machine learning, visual effects, rendering, batch processing, and pixel streaming.
A key highlight is the training of the GPT-NeoX-20B, the largest publicly available language model, on CoreWeave’s Nvidia A100 Tensor Core GPU training cluster. CoreWeave's serverless Kubernetes infrastructure is in high demand for building innovative products powered by large language models.
In early 2023, CoreWeave announced the availability of Nvidia H100 Tensor Core GPUs on its cloud platform, making it one of the first providers to offer cloud instances of Nvidia H100-based supercomputers. This GPU delivers exceptional performance, scalability, and security for a variety of workloads, with up to 9x faster training and 30x inference speedup on LLMs.
CoreWeave offers a diverse array of Nvidia GPUs, including the HGX H100, A100, A40, and RTX A6000, addressing various use cases. The pricing for these GPUs starts at $2.23 per hour for the Nvidia HGX H100s. CoreWeave's pricing model is designed for flexibility, allowing customization of GPU, CPU, RAM, and storage based on the user's needs. The company emphasizes delivering performance up to 35x faster and 80% less expensive than generalized public clouds.
The cost per hour for different GPUs varies, with options like the Nvidia H100 PCIe at $4.25 per hour and the A100 80GB NVLINK at $2.21 per hour. This flexible pricing structure is intended to provide control over configuration and cost, making it adaptable to different business requirements.
CoreWeave is known for its industry-leading hardware for inference, capable of autoscaling within three seconds. This rapid deployment capability is crucial for serving AI models efficiently. The company offers newer instance products, including Nvidia’s HGX H100 server, tailored for modern AI and machine learning applications.
Lambda
Lambda offers a range of NVIDIA GPUs including the NVIDIA GH200 Grace Hopper™ Superchip, H100, A100, A10, RTX A6000, RTX 6000, and V100 Tensor Core GPUs. They provide multi-GPU instances catering to various workloads and budgets with options of 1x, 2x, 4x, and 8x NVIDIA GPU instances. Their cloud service is developer-friendly, featuring an easy-to-use Lambda Cloud API, one-click Jupyter access, and pre-installed popular machine learning frameworks like Ubuntu, TensorFlow, PyTorch, CUDA, and cuDNN.
Lambda's pricing model is competitive, offering on-demand NVIDIA H100 Tensor Core GPUs starting at $1.99 per GPU per hour. The pricing is transparent with no hidden fees for data egress or ingress and includes pay-by-the-second billing.
Lambda also provides a high-speed filesystem for GPU instances, with shared filesystems costing $0.20 per GB per month. The pricing varies depending on the GPU model and configuration, ranging from $0.50 per hour for a single NVIDIA Quadro RTX 6000 GPU to $20.72 per hour for eight NVIDIA H100 SXM GPUs.
Lambda is known for its ease of use, particularly for developers working on GPU-intensive tasks. It offers pre-configured environments for machine learning, allowing developers to start training quickly.
Modal
Modal has positioned itself as a key data infrastructure platform for AI, addressing the crucial needs of hardware, software, and networking components essential for AI projects. Modal's containerized solutions facilitate the rapid launching of AI infrastructure, reducing the integration time of disparate solutions and thereby saving costs.
The platform is designed to automatically scale to different GPUs and CPUs, charging for actual usage on a per-second basis. This flexibility makes Modal suitable for a wide range of AI applications including generative AI, computational biotech, automated transcription, and code execution.
Modal has developed a container system from scratch using Rust, offering exceptionally fast cold-start times. This system is capable of scaling to hundreds of GPUs and back down to zero in seconds, with a payment model that only charges for actual usage. Their infrastructure enables the deployment of functions to the cloud in seconds, with custom container images and hardware requirements, eliminating the need for writing complex configuration files.
For GPU acceleration, Modal allows developers to attach GPUs to their functions easily, with options to specify GPU type and count. They offer Nvidia's high-end A100 GPUs, known for their robust performance in data center applications.
The starter plan is free and includes $30/month of free computing, suitable for small teams and independent developers. The team plan, aimed at startups and larger organizations, costs $100/month plus compute costs, offering more GPU concurrency and private Slack support. For enterprises, Modal provides a custom pricing model with personalized integration support and priority feature requests.
Together AI
Together AI's GPU clusters, known as Together GPU Clusters (formerly Together Compute), are an integral part of the company's suite of AI and machine learning tools. They have garnered attention for their high performance and innovative features, while also drawing some critical perspectives.
Together GPU clusters are built on state-of-the-art NVIDIA GPUs, including H100 and A100 models, which are connected via fast Infiniband networks or Ethernet, ensuring efficient data transfer and processing power. These clusters also feature NVMe storage with options for expanding to high-speed network-attached storage. The Together Compute’s Training Stack, a proprietary suite of training modules, integrates cutting-edge technologies like network compression, the Sophia optimizer, and FlashAttention to enhance training speed and reduce costs.
One of the standout features of Together AI's GPU clusters is their flexibility. They offer users the ability to scale their compute capacity based on changing needs, ranging from 16 to 2048 GPUs. This scalability is crucial for businesses with fluctuating computational requirements. Another interesting aspect of Together AI's offering is the option to choose environmentally friendly, carbon-reducing data centers. This feature aligns with growing concerns about the environmental impact of large-scale computing and data processing.
OctoML
OctoML, an artificial intelligence optimization startup, has gained prominence in the GPU market for its innovative approaches to AI model deployment and management. The company's foundational infrastructure, OctoAI, is designed to assist developers in building and scaling AI applications efficiently. This platform stands out as the industry’s first self-optimizing compute service for AI models, marking a significant shift in OctoML's strategic direction from its initial focus on AI optimization.
OctoML's flagship product, OctoAI, provides a fully managed cloud infrastructure designed for building and scaling AI applications. This service allows developers to run, tune, and scale models of their choice, including off-the-shelf, open-source software (OSS), and custom models. It offers a library of the fastest and most affordable generative AI models, such as Stable Diffusion 2.1, Dolly v2, and Llama 65B. These models are powered by OctoML’s model acceleration capabilities, emphasizing the platform's efficiency and scalability.
OctoML's OctoAI platform is recognized for its efficiency and flexibility. It enables companies to fine-tune AI models to meet specific requirements and balance costs effectively. One of the most significant aspects of OctoML’s service is its capability to optimize models and hardware, ensuring the right performance-efficiency tradeoffs.
This ability is crucial for companies scaling up AI operations, as it provides an efficient structure for running AI models on the cloud without the need to change code or retrain models. Notably, OctoML’s optimized models can run almost as efficiently on older Nvidia Corp. A10G GPUs as on the newer A100 GPUs, offering a significant advantage in the context of current GPU shortages.
OctoML emphasizes cost-efficiency in its offerings. The platform's automated hardware selection allows users to decide on price-performance tradeoffs, catering to different budget requirements.
Comparative Analysis
Each company carves out its niche, catering to specific user needs and project types. CoreWeave and Together AI are powerhouses for high-end, intensive computational tasks.
CoreWeave, with its wide array of Nvidia GPUs, is particularly suited for demanding AI applications, where robust processing and flexibility are key. Together AI, emphasizing high-performance computing and environmentally conscious data centers, aligns well with large-scale AI projects and businesses prioritizing sustainability alongside performance.
On the other end of the spectrum, Lambda and Modal offer solutions more accessible to smaller teams and individual developers. Lambda, with its competitive pricing and developer-friendly environment, is ideal for those starting in GPU-intensive tasks, providing a balance between affordability and performance. Modal’s containerized approach and scalable solutions, coupled with its cost-effective pricing models, make it a favorable option for startups and small teams that need to adapt rapidly to changing computational demands.
OctoML stands out with its focus on optimizing AI model performance, making it a versatile choice for various applications. Whether for a startup looking to efficiently deploy AI models or a larger enterprise needing to manage complex AI operations, OctoML’s platform provides a unique blend of efficiency and adaptability.
This landscape illustrates the diverse approaches of GPU vendors, highlighting how they tailor their offerings to different segments of the market - from heavy-duty computational tasks to budget-conscious, flexible projects, and efficient model management.
Conclusion
Each GPU vendor not only provides the necessary computational power but also brings tailored solutions to meet the specific needs of different users, from pricing flexibility to environmental considerations.
As AI and machine learning continue to advance, the role of these GPU vendors will only become more crucial, shaping the future of technology and its application across diverse industries. With the analysis above, it should be easier to decide on which vendor to choose based on individual requirements.
This article covers vital information for ML engineers who are running out of GPUs in large cloud providers these days. Great job!