Compute purpose-built for AI workloads

Our Managed Kubernetes service handles the infrastructure layer for AI training workloads — automated scaling, node provisioning, GPU scheduling, and observability — so your team can focus on model development, not cluster management. It is built specifically for training LLMs and other large models, with native support for multi-tenant jobs, fine-grained resource allocation, and integration with your existing MLOps pipelines.

SLONK (Slurm on Kubernetes) combines the mature job-scheduling of Slurm with the elasticity of Kubernetes. You get Slurm’s advanced scheduling, gang-scheduling, fair-share policies, and checkpointing — running natively on top of a Kubernetes substrate. The result is 100% cluster utilisation, faster queue times, and the ability to run both interactive training and large batch jobs on the same hardware.

Yes. Tscale’s GPU clusters are designed to flex from a single 8-GPU node to multi-thousand GPU deployments on the same blueprint. Capacity is provisioned in modular blocks, then aggregated into campuses and regions. You can scale up for large pre-training runs and scale down for fine-tuning or evaluation — without waiting for hardware lead times.

Tscale supports the full training lifecycle: LLM pre-training and fine-tuning (causal, masked, multimodal), reinforcement learning from human feedback (RLHF), diffusion model training, recommendation systems, time-series forecasting, computer vision, and classical ML at scale. Both research workloads and production retraining pipelines run on the same infrastructure.

Our clusters run next-generation NVIDIA hardware — including Blackwell and Rubin architectures — alongside high-performance networking fabrics (InfiniBand and Spectrum-X Ethernet). We are also deploying AMD MI300X for cost-optimised training paths, with UCMM tuning delivering up to 7.2× faster throughput and latency on common workloads.

For on-demand GPU instances, you can be training within hours. For reserved clusters at hyperscale, 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.