Highly Scalable Architecture
Tscale Cloud provides the foundation to support and accelerate projects — whether you need to scale training jobs to handle a few GPUs or thousands of nodes, and can dynamically scale up to meet any workload demand.
/ MODEL TRAINING
Model Training
Tscale’s cloud offers a highly scalable, performance-optimised architecture that significantly reduces training times while delivering the cost-efficiency that alternative cloud platforms can’t match.
Reduced Training Times
Tscale Cloud has been helping institutions, companies, and universities to significantly accelerate their model training — allowing you to iterate and ideate faster, helping you reach your AI goals quickly and efficiently.
Increased Productivity
Tscale’s resources free up your time to focus on development and experimental work, freeing up time to focus on the work that matters most. Our team of AI engineers provides expert-level management of your production AI workloads.
Accelerated Model Training
Training AI Models poses significant challenges that require robust, flexible, and efficient infrastructure to ensure reliability, cost-effectiveness, and ease of management. Tscale’s Cloud platform provides innovative tools to help reduce this complexity.
Simplified Scheduling and Orchestration
Stream and batch service simplified. A managed service by Tscale that lets you schedule jobs faster, allocate resources as needed, and handle complex workloads using a simple API. Less time setting up infra, more time training and iterating on your AI models. Access and run them from within a cluster of resources that supports distributed training.
Performance Optimisations
Tscale’s cloud platform offers the latest available GPU resources, supporting high-performance tuning and model training in minutes. Our platform also supports multi-node and multi-GPU training, helping you achieve up to 80% better performance at scale. Other key features include the ability to configure your hardware, OS, and other package requirements to match your specific needs.
/ THE STACK
Training Stack
Tscale provides a complete technology stack for running intensive training workloads in the most efficient and high-performing way possible.
Marketplace
- Jupyter Notebooks
- TensorFlow
- PyTorch
User Experience
- Web Console
- API
- CLI
Platform
- Virtual Machines
- Managed Kubernetes
Infrastructure
- GPU Compute
- Storage
- Networking
Hardware
- A40
- H100
- H200
- GB200
- MI300X
Data Centre
- Renewable Energy
- Low-latency Fabric
Performance
Accelerate time to value
Tscale Cloud accelerates time to insights by up to 30%, thanks to its AI-optimised stack.
More performance for less
Tscale delivers an average 80% cost-saving in comparison to hyperscalers.
Improved resource utilisation
Up to 40% improvement in efficiency across compute, memory, and networking.
Faster training throughput
GPUs with UCMM tuning improve throughput and latency by up to 7.2x.
Key Services
AI Compute Training
A highly scalable, performance-optimised technology that significantly reduces training times and lowers production costs.
Learn MoreAI Marketplace
An ecosystem of services for developing and deploying AI applications built using Tscale tools and other popular AI/ML software.
Learn MoreMore solutions
Tscale accelerates the journey from development to deployment, delivering faster time to productivity for your AI initiatives.
FAQs
Quick answers to the most common questions about Tscale’s Model Training platform, GPU performance, and the training stack.
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What makes Tscale’s GPU Cloud different from others?
Tscale is purpose-built for AI training workloads — not retrofitted from general-purpose cloud. Every layer of the stack is optimised for training: bare-metal GPU nodes with NVIDIA Blackwell and Rubin, behind-the-meter power for predictable costs, proprietary software tuning (UCMM) that delivers up to 7.2× faster throughput, and an integrated training environment that scales from a single 8-GPU node to multi-thousand GPU deployments.
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What types of GPUs does Tscale offer?
We deploy the full spectrum of modern AI accelerators: NVIDIA A40, H100, H200, and GB200 (Blackwell) for production training, plus AMD MI300X for cost-optimised paths. New hardware lands on the platform within weeks of release — your team always has access to the latest generation, on the same blueprint across regions.
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How does Tscale support sustainability?
Our data centres are powered by renewable energy, with behind-the-meter generation that decouples AI workloads from grid volatility. Direct-to-chip liquid cooling improves PUE (Power Usage Effectiveness) by 30–40% vs. air-cooled hyperscaler facilities, and our predictive digital twin operations reduce energy waste from over-provisioning. We publish sustainability metrics for every region.
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How long does it take to start training a new model?
For on-demand GPU instances, you can have a training job running within minutes. For reserved clusters at scale, the standard delivery window is 18–24 months from signed contract to operational capacity — significantly faster than the 3–5 year industry norm, thanks to our pre-cleared land bank and behind-the-meter power model.
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Can I bring my own model and frameworks?
Yes. The platform is framework-agnostic — bring any model in PyTorch, TensorFlow, JAX, or ONNX format, with any custom dependencies. The Marketplace ships pre-tuned containers for the most common frameworks, and you can also deploy fully custom containers via the API or CLI. No rate flexibility limits, no integration hurdles.
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Do you support both cloud and on-premises deployments?
Yes. Tscale supports cloud, on-premises, and hybrid environments from a single control plane. Migration is handled by our expert team with pre-built toolsets, so you can move workloads between Tscale Cloud, your own data centre, or a colocated facility without re-architecting. Sovereign deployments are available for regulated industries.