Purpose-built for the most demanding
AI and deep learning workloads
SambaNova DataScale® is a fully integrated hardware-software system, powered by a dataflow architecture, that enables organizations to train and deploy the most demanding deep learning, foundation model, and AI for Science workloads and achieve world record performance with the largest and most challenging models.
SambaNova DataScale SN30
Develop and deploy the most demanding workloads, including large language models, true resolution imaging, and complex AI for Science workloads
Dataflow architecture eliminates the inherent inefficiencies of traditional systems to deliver massive performance advantages over GPUs
Fully integrated software-hardware system that extracts, optimizes, and executes the optimal dataflow graph of any of your models
Seamlessly scale from a single node to hundreds of systems with little to no code changes
What our customers say
Powering the most demanding deep learning and AI for Science workloads
State-of-the-art deep learning, foundation models, and AI for Science is advancing quickly, defined by very large models and data, and very detailed sparse models. GPU-based infrastructure struggles to effectively manage these workloads without complex and costly overhead and compromises in performance and accuracy.
DataScale’s dataflow architecture combined with large on-chip and system memory enable organizations to run workloads that cannot practically be handled by GPUs, including:
- Large models, such as 100B+ parameter large language models
- Large data, including true resolution 2D and 3D images
- Detailed models, such as RNNs, sparse models, and other AI for Science workloads
As incremental performance gains from each generation of CPU and GPU declines, the demands of state-of-the-art AI and deep learning is increasing.
Powered by the new Cardinal SN30™ RDU, DataScale SN30 delivers performance enhancements including world record GPT training that is 6x faster than DGX A100 systems (GPT 13B), 12.8x more memory capacity than DGX A100, and deployment automation and ease of scale capabilities.
Training and deploying deep learning and foundation models requires massive parallelization of hundreds, or even thousands, of GPUs. This process is costly, complex, and can degrade accuracy.
DataScale’s large on-chip and system memory enables it to handle the largest models and data with little to no parallelization required. The SambaFlow software manages scaling across any number of devices or configurations. Seamlessly scale up to 48 racks of DataScale systems with consistent rack-to-rack bandwidth and latency.
This enables organizations to train and deploy the largest models without the cost, complexity, and overhead that GPUs require.
A complete software stack for SambaNova DataScale® , SambaFlow™ fully integrates with popular standard frameworks such as PyTorch. SambaFlow provides an open, flexible, and easy-to-use development interface.
SambaFlow automatically extracts, optimizes, and executes the optimal dataflow graph of any of your models. This enables you to achieve out-of-the-box performance, accuracy, scale, and ease of use. With SambaFlow, you can maximize productivity by focusing your development efforts in the frameworks without ever again worrying about low-level tuning.
Fully integrated and optimized
The state-of-the-art in AI is constantly being redefined, introducing changing requirements as new breakthroughs emerge. Legacy systems such as GPUs require data science and engineering teams to use proprietary coding languages to optimize these new models. On top of that, sufficiently training and deploying even the same model requires different forms of scalabilities. Managing a mixed portfolio of hardware to cover the full usage spectrum can be overbearing, let alone managing the frequent needs to add or switch to new infrastructure in order to keep up with the new requirements.
DataScale is a rack level solution that can be
set up and deployed in less than an hour.
DataScale integrates directly with common
open-source frameworks such as PyTorch,
and uses low-code/no-code APIs to
significantly simplify deployment of models.
SambaFlow automatically extracts, optimizes,
and executes the optimal dataflow graph of
models for optimized training and inference
DataScale can manage training and
inference on the same platform, greatly
simplifying the necessary infrastructure to
manage the end-to-end ML pipeline.
SambaNova subscription pricing
Now organizations can achieve ROI faster, substantially reduce risk, and scale more cost effectively than is possible with any other AI infrastructure offering.
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